Closing the Carbon Loop: Advanced Life Support Systems for Terrestrial and Space Applications

Abstract

This article provides a comprehensive analysis of carbon loop closure technologies in advanced life support systems, exploring their foundational principles, methodological applications, and optimization strategies. Tailored for researchers and scientists, it bridges knowledge from space exploration—where systems like the Advanced Closed Loop System (ACLS) and Next Generation Life Support (NGLS) demonstrate high-fidelity carbon recycling—with terrestrial ecosystem management and biomedical research. We examine carbon concentration, oxygen generation, and bioregenerative methods, address troubleshooting and system reliability, and validate performance through comparative analysis and modeling. The synthesis offers critical insights for developing closed-loop systems that ensure sustainability in isolated environments, from spacecraft to clinical settings, and informs future innovations in carbon-neutral technologies.

The Principles and Urgency of Carbon Loop Closure

Defining Carbon Loop Closure in Controlled Environments

Carbon loop closure represents a critical paradigm in environmental control and life support systems (ECLSS) for advanced human exploration and terrestrial applications. This technical framework involves the continuous recycling of carbon dioxide through capture, concentration, and conversion processes to regenerate oxygen and produce valuable resources. As research advances toward completely closed habitats for deep space missions, precise carbon loop management has become essential for reducing resupply requirements and enabling long-duration human presence in isolated environments. This whitepaper examines the core principles, technological implementations, and experimental methodologies defining carbon loop closure, with particular emphasis on integrated systems currently demonstrating operational efficacy in controlled settings.

Carbon loop closure encompasses the engineered processes that capture, manage, and convert carbon dioxide into usable resources within controlled environments. In advanced life support systems research, this concept extends beyond mere carbon dioxide removal to encompass comprehensive carbon cycling that minimizes external inputs and maximizes resource regeneration. The fundamental objective is to create a balanced mass exchange where carbon emitted through human respiration and other processes is continuously recycled rather than vented as waste [1] [2].

In practical terms, carbon loop closure represents a critical path toward sustainable long-duration space missions, where resupply from Earth becomes progressively more challenging and eventually impossible. The European Space Agency's Advanced Closed Loop System (ACLS) demonstrates this principle by recycling carbon dioxide from cabin air into breathable oxygen, thereby reducing water resupply requirements by approximately 400 liters annually [1]. Similarly, terrestrial applications are emerging in industrial carbon capture, utilization, and storage (CCUS) frameworks, where point-source carbon emissions are converted into valuable products including fuels, fertilizers, and construction materials [3] [4].

Core Principles and System Components

Fundamental Operational Principles

Carbon loop systems operate on three fundamental principles: concentration, conversion, and regeneration. The concentration phase involves selective capture of COâ‚‚ from atmospheric mixtures, typically achieved through chemical adsorption processes. The conversion stage transforms concentrated COâ‚‚ into chemically reduced forms through various catalytic pathways. Finally, regeneration completes the loop by returning useful products to the habitat environment while replenishing any consumables required for the concentration phase [1] [2].

Mass balance precision represents another critical principle, as system stability requires careful matching of COâ‚‚ production rates with processing capacity. In the ISS ACLS system, this balance is maintained through continuous monitoring and adjustment of the Carbon dioxide Concentration Assembly (CCA) operation to match crew metabolic output [2]. Systems must be designed with sufficient buffer capacity to accommodate fluctuations in crew size and activity levels while maintaining cabin COâ‚‚ within acceptable limits for human health and performance.

Key System Components

Closed-loop carbon systems integrate several specialized components that function in concert to maintain continuous operation:

• Carbon Concentration Assembly (CCA): Utilizes amine-functionalized adsorbent materials to selectively remove COâ‚‚ from cabin atmosphere. The ACLS system employs specialized amine-developed beads that exhibit high COâ‚‚ adsorption capacity and cycling stability [1]. Steam regeneration then releases concentrated COâ‚‚ for subsequent processing while restoring adsorption capacity.

• Carbon Dioxide Reprocessing Assembly (CRA): Implements Sabatier reaction chemistry where concentrated COâ‚‚ reacts with hydrogen over a catalyst (typically ruthenium on alumina) to produce methane and water. The standard reaction (COâ‚‚ + 4Hâ‚‚ → CHâ‚„ + 2Hâ‚‚O) achieves approximately 80-90% conversion efficiency at operational temperatures of 300-400°C [1] [2].

• Oxygen Generation Assembly (OGA): Utilizes proton exchange membrane (PEM) electrolysis to split water recovered from the Sabatier reactor into oxygen and hydrogen. The oxygen is returned to cabin atmosphere for crew consumption, while hydrogen is recycled to the Sabatier reactor [1].

Table: Performance Metrics of ACLS System Components [1] [2]

Component	Function	Efficiency	Output Capacity
Carbon Concentration Assembly (CCA)	COâ‚‚ capture from cabin air	>90% COâ‚‚ removal	Matches crew metabolic output
Carbon Dioxide Reprocessing (CRA)	Sabatier conversion of COâ‚‚ to CHâ‚„ and Hâ‚‚O	80-90% conversion	Water production for 50% Oâ‚‚ needs
Oxygen Generation Assembly (OGA)	Electrolysis of water to Oâ‚‚ and Hâ‚‚	>99% purity Oâ‚‚	Supports 3 crew members

Technological Implementation Frameworks

Current Space-Based Systems

The Advanced Closed Loop System (ACLS) represents the most technologically mature implementation of carbon loop closure in operational use. Deployed on the International Space Station, ACLS operates as a standardized 2-meter rack within the US Destiny module, integrating all necessary components for continuous carbon recycling [1]. The system demonstrates a partially closed loop where approximately 50% of recovered COâ‚‚ is ultimately converted back to oxygen, with the remainder vented as methane due to stoichiometric limitations of the Sabatier process [1] [2].

The ACLS operational concept employs a sequential processing approach where cabin air first passes through the CCA for COâ‚‚ concentration, then the concentrated COâ‚‚ moves to the CRA (Sabatier reactor) where it combines with hydrogen from the OGA. The resulting water is purified and transferred to the OGA for electrolysis, completing the oxygen regeneration cycle. This integrated approach reduces the Station's water resupply requirements by approximately 400 liters annually while maintaining cabin COâ‚‚ at safe levels without consumable cartridges [1].

Terrestrial Methodologies and Industrial Parallels

Terrestrial carbon closure strategies employ similar physicochemical principles but with expanded product outputs, particularly within carbon capture, utilization, and storage (CCUS) frameworks. India's research initiatives focus on point-source capture from industrial sectors (power, cement, steel) representing approximately 80% of the country's 2,600 Mt annual COâ‚‚ emissions [3]. Conversion pathways emphasize economic viability through production of high-value marketable products including fuels, fertilizers, aggregates, and construction materials that support circular carbon economies.

Industrial carbon conversion employs multiple catalytic pathways, each with distinct operational parameters and output profiles. Thermocatalysis utilizes heat and pressure (700-1000°C) with hydrogen to produce alcohols like methanol and ethanol. Electrochemical conversion employs renewable electricity for carbon-neutral operation at ambient conditions. Photocatalysis mimics natural photosynthesis using light energy, while biocatalysis leverages enzymatic or microbial processes for specific chemical production [4].

Table: Comparative Analysis of COâ‚‚ Conversion Technologies [4]

Conversion Method	Operating Conditions	Primary Products	Technology Readiness
Thermocatalysis	700-1000°C, high pressure	Methanol, ethanol, methane	Commercial demonstration
Electrochemical Conversion	Ambient, electrical input	Carbon monoxide, formic acid	Pilot scale
Photocatalysis	Ambient, light input	Hydrogen, syngas	Laboratory research
Biocatalysis	Ambient, biological	Ethanol, ethylene	Early commercial
Carbon Mineralization	Ambient to moderate	Carbonates, building materials	Commercial operation

Experimental Protocols and Methodologies

System Performance Validation

Research-grade evaluation of carbon closure systems requires rigorous experimental protocols to quantify performance across operational parameters. The ACLS validation approach implemented on the ISS involves continuous monitoring of key performance indicators over extended durations (typically 1 year of operation within a 2-year demonstration window) [2]. Standardized measurement protocols include:

• COâ‚‚ Concentration Efficiency: Measured via infrared spectroscopy at CCA inlet and outlet ports, calculating removal efficiency as [(Cin - Cout)/C_in] × 100%, with target performance >90% under nominal crew metabolic loads [2].

• Sabatier Reactor Conversion Rate: Quantified through gas chromatography of input (COâ‚‚ + Hâ‚‚) and output (CHâ‚„ + Hâ‚‚O + unreacted gases) streams, with conversion efficiency calculated based on COâ‚‚ depletion. Optimal performance achieves 80-90% conversion at 300-400°C with ruthenium catalysts [1] [2].

• Oxygen Generation Purity: Monitored via mass spectrometry of OGA output stream, with requirement for >99% oxygen purity for crew life support applications [1].

• System Mass Balance: Continuous tracking of input and output mass flows (COâ‚‚, Hâ‚‚, CHâ‚„, Hâ‚‚O, Oâ‚‚) to verify closure metrics and identify any accumulation losses or byproducts affecting long-term operation [2].

Material Testing and Catalyst Evaluation

Catalyst development represents a critical research domain for improving carbon conversion efficiency and longevity. Standard experimental protocols for novel catalyst evaluation include:

• Accelerated Lifetime Testing: Continuous operation under simulated feed conditions with periodic performance assessment to determine degradation rates and operational lifespan. The ACLS amine sorbent materials underwent >10,000 adsorption-desorption cycles during ground testing prior to flight approval [2].

• Contaminant Tolerance Assessment: Introduction of potential atmospheric contaminants (siloxanes, hydrocarbons, etc.) at measured concentrations to quantify performance impacts and develop mitigation strategies for closed environments [2].

• Surface Characterization: Pre- and post-testing analysis using SEM, XRD, and BET surface area measurements to correlate structural changes with performance degradation and identify failure mechanisms.

Visualization of System Processes

ACLS Carbon Loop Closure Process

Research Reagent Solutions and Essential Materials

Table: Essential Research Materials for Carbon Loop Closure Experiments

Material/Component	Function	Research Application
Amine-functionalized Adsorbents	COâ‚‚ capture from air	Carbon concentration subsystems
Ruthenium on Alumina Catalyst	Sabatier reaction facilitation	COâ‚‚ to CHâ‚„ conversion
Proton Exchange Membrane (PEM)	Water electrolysis	Oxygen generation from water
Zeolite Molecular Sieves	Gas separation and drying	Process air purification
Nickel-based Catalysts	Alternative Sabatier medium	Lower-cost COâ‚‚ conversion
Solid Oxide Electrolysis Cells	High-temperature electrolysis	Efficient oxygen generation
Calcium Oxide Sorbents	Carbon mineralization	COâ‚‚ to carbonate conversion

Carbon loop closure represents a critical capability for advancing human presence in isolated environments, with demonstrated efficacy in operational space systems and emerging applications in terrestrial carbon management. The integration of concentration, conversion, and regeneration technologies enables increasingly closed systems that reduce resource dependencies and support sustainable long-duration operations. Current implementations like the ACLS demonstrate technical feasibility while highlighting areas for further development, particularly in closing the methane venting gap and improving system energy efficiency.

Future research priorities include developing alternative catalytic pathways with improved stoichiometry, integrating biological processing components for food production, and advancing system autonomy for deep space missions where ground support is limited. The continuing evolution of carbon loop closure technologies will play a decisive role in enabling human exploration beyond low-Earth orbit while contributing valuable spinoff technologies for terrestrial carbon management challenges.

The Critical Role in Long-Duration Space Missions

Long-duration space missions beyond low-Earth orbit necessitate a paradigm shift from open-loop to closed-loop Environmental Control and Life Support Systems (ECLSS). The critical role of closing the carbon loop is paramount for mission sustainability, drastically reducing resupply mass and enabling human exploration of deep space. This whitepaper examines the core technologies for carbon dioxide (COâ‚‚) concentration, reduction, and oxygen generation, presenting quantitative performance data, detailed operational methodologies, and system-level integration strategies. Framed within the broader context of achieving full carbon loop closure, this analysis provides researchers and life support scientists with the technical framework for advancing regenerative life support systems for lunar Gateway, Mars transit, and sustained planetary habitation.

In the inhospitable environment of space, sustaining human life is a complex challenge of resource management. Open-loop systems, which rely on regular resupply of consumables like water and oxygen from Earth, are logistically and economically infeasible for missions to the Moon, Mars, and beyond. The cornerstone of sustainable long-duration missions is the development of robust ECLSS that progressively close the loops on air, water, and waste [5]. Central to this challenge is the carbon loop, which revolves around the astronaut's metabolic function of consuming oxygen (Oâ‚‚) and producing COâ‚‚.

Closing the carbon loop involves capturing and processing exhaled COâ‚‚ to recover oxygen, thereby creating a regenerative cycle. The European Space Agency's (ESA) Advanced Closed Loop System (ACLS) represents a significant leap forward, demonstrating a functional rack on the International Space Station (ISS) that recycles carbon dioxide into oxygen [1]. Similarly, NASA's expertise encompasses the research, development, and testing of closed-loop technologies for carbon dioxide removal, reduction, and oxygen generation [6]. This paper deconstructs the critical subsystems involved, their performance parameters, and their integrated operation within the broader goal of full carbon loop closure.

Core Subsystems and Quantitative Analysis

A closed-loop carbon system comprises three primary technological assemblies: the concentration of COâ‚‚ from cabin air, its chemical reduction, and the subsequent generation of oxygen. The performance data of these subsystems directly dictates the overall efficiency and degree of loop closure achievable.

Carbon Dioxide Concentration Assembly (CCA)

The CCA is the first critical step, responsible for removing COâ‚‚ from the cabin atmosphere to maintain acceptable levels for crew health and preparing it for downstream processing. The ACLS utilizes a solid amine-based chemical process, trapping COâ‚‚ from the air as it passes through small beads composed of a unique amine developed by ESA [1]. The concentrated COâ‚‚ is then released using steam for further processing.

Carbon Dioxide Reprocessing Assembly (CRA/Sabatier Reactor)

The CRA performs the key function of carbon dioxide reduction. The most common and flight-proven method is the Sabatier process, which converts CO₂ into water and methane. In this reaction, hydrogen and carbon dioxide react over a catalyst, typically nickel or ruthenium, at elevated temperatures (200-400°C) to form water (H₂O) and methane (CH₄) [1]. The water is condensed, separated, and fed to the oxygen generation assembly. The methane is typically vented to space, which represents a loss of hydrogen and explains why current systems like the ACLS recover only about 50% of the oxygen from the processed CO₂ [1].

Oxygen Generation Assembly (OGA)

The OGA completes the loop by electrolyzing water to produce oxygen for the crew and hydrogen for the Sabatier reactor. The OGA is an electrolyser that splits water into oxygen and hydrogen using an electrical current [1]. The oxygen is introduced into the cabin for the crew to breathe, while the hydrogen is directed back to the Sabatier reactor to facilitate the reduction of more COâ‚‚.

Table 1: Performance Metrics of the Advanced Closed Loop System (ACLS)

Parameter	Value	Significance
Oxygen Production Capacity	Supports 3 astronauts [1]	Demonstrates capability to support a significant portion of a standard ISS crew.
Water Savings	~400 liters per year [1]	Quantifies the direct reduction in resupply mass from Earth.
COâ‚‚ Recovery Rate	50% [1]	Highlights current system limitation due to methane venting.
Physical Dimensions	2 m high, 1 m wide, 85.9 cm deep [1]	Informs mass and volume constraints for vehicle integration.

Table 2: Comparative Analysis of Carbon Loop Closure Technologies

Technology	Process	Inputs	Outputs	Loop Closure Efficiency
Sabatier Reactor	CO₂ + 4H₂ → CH₄ + 2H₂O (over catalyst) [1]	Carbon Dioxide, Hydrogen	Water, Methane	Partial (50% O₂ recovery) [1]
Bosch Reaction	CO₂ + 2H₂ → C + 2H₂O	Carbon Dioxide, Hydrogen	Water, Solid Carbon	Potentially Full (no methane vented)
Advanced Sabatier	Sabatier with methane pyrolysis (CH₄ → C + 2H₂)	Carbon Dioxide	Water, Solid Carbon, Hydrogen	Potentially Full (hydrogen recycled)

Experimental Protocols and System Workflows

For researchers developing and testing these subsystems, standardized methodologies are crucial for benchmarking performance and ensuring reliability.

Protocol for Solid Amine COâ‚‚ Sorption/Desorption Cycling

This protocol details the process for evaluating and operating a solid amine COâ‚‚ concentrator.

• Objective: To characterize the efficiency and capacity of a solid amine sorbent for concentrating COâ‚‚ from a simulated cabin atmosphere.
• Materials:
  • Test chamber packed with solid amine sorbent beads.
  • Gas mixing system to simulate cabin air (∼0.5-1% COâ‚‚, 21% Oâ‚‚, 78% Nâ‚‚).
  • Steam generator for desorption phase.
  • Mass Flow Controllers (MFCs) for precise gas handling.
  • COâ‚‚, Oâ‚‚, and humidity sensors at inlet and outlet.
  • Data acquisition system.
• Procedure:
  • Sorption Phase: Condition the sorbent bed. Flow the simulated cabin air through the sorbent bed at a specified rate and temperature (e.g., 25-30°C). Monitor the outlet COâ‚‚ concentration until breakthrough is detected, indicating sorbent saturation.
  • Desorption Phase: Isolate the bed from the cabin air stream. Heat the bed internally or externally while introducing a controlled flow of steam. The steam lowers the partial pressure of COâ‚‚ and provides the thermal energy required to break the amine-COâ‚‚ bond, releasing a concentrated stream of COâ‚‚.
  • Collection & Measurement: Channel the desorbed gas stream through a condenser to remove water. Measure the volume and concentration of the dry, concentrated COâ‚‚.
  • Data Analysis: Calculate the dynamic COâ‚‚ adsorption capacity of the sorbent (grams of COâ‚‚ per kg of sorbent) and the concentration factor achieved.

Protocol for Sabatier Reactor Performance Characterization

This protocol outlines the testing of a Sabatier reactor's conversion efficiency.

• Objective: To determine the conversion efficiency of a Sabatier reactor catalyst under varying operational parameters.
• Materials:
  • Sabatier reactor core containing the catalyst (e.g., Ruthenium on Alumina).
  • Precisely controlled supply of Hâ‚‚ and COâ‚‚ gases.
  • Heated reactor jacket with temperature control.
  • In-line Gas Chromatograph (GC) or Mass Spectrometer (MS) for product analysis.
  • Pressure regulators and sensors.
  • Condenser and water separator.
• Procedure:
  • System Preparation: Purge the reactor with an inert gas. Set the reactor to the desired operating temperature (typically 300-400°C).
  • Reaction Initiation: Introduce Hâ‚‚ and COâ‚‚ at a stoichiometric ratio of 4:1. Adjust the total gas flow rate to achieve the desired space velocity.
  • Steady-State Operation: Maintain conditions until the product stream composition stabilizes (monitored via GC/MS).
  • Product Analysis: Sample the output gas stream after the condenser to analyze the composition (remaining Hâ‚‚, COâ‚‚, CHâ‚„). Collect and measure the quantity of water produced.
  • Data Analysis: Calculate the COâ‚‚ conversion efficiency: [(COâ‚‚in - COâ‚‚out) / COâ‚‚_in] * 100%. Correlate efficiency with temperature, pressure, and space velocity.

The logical and material flow between these subsystems and the crew is best understood through a system diagram.

Carbon Loop Closure in Life Support Systems

The Researcher's Toolkit: Essential Reagents and Materials

The experimental and operational protocols rely on a suite of specialized reagents and materials. The following table details key items critical for research and development in carbon loop closure.

Table 3: Key Research Reagents and Materials for Carbon Loop Systems

Item Name	Function / Role in Experimentation
Solid Amine Sorbents	Porous beads or structured substrates with amine functional groups for selective COâ‚‚ capture from cabin air through chemical sorption [1].
Sabatier Catalyst	A catalytic surface, typically ruthenium or nickel supported on alumina, that lowers the activation energy for the reaction between COâ‚‚ and Hâ‚‚, enabling efficient production of water and methane [1].
Proton Exchange Membrane (PEM) Electrolysis Cell	The core component of a modern OGA, where a solid polymer electrolyte facilitates the efficient splitting of water into oxygen and hydrogen gas using an electric current [1].
Mass Flow Controllers (MFCs)	Critical for laboratory setups, these devices precisely control and measure the flow rates of gases (e.g., COâ‚‚, Hâ‚‚, Nâ‚‚) into reactors, ensuring accurate stoichiometry and repeatable experimental conditions.
Gas Chromatograph / Mass Spectrometer (GC/MS)	An essential analytical instrument for quantifying the composition of gas streams before, during, and after reactor experiments, used to determine conversion efficiencies and identify byproducts.
PI3K-IN-38	PI3K-IN-38, MF:C20H24N6O2, MW:380.4 g/mol
Palmitoyl serinol-d5	Palmitoyl serinol-d5, MF:C19H39NO3, MW:334.5 g/mol

Integration and Future Directions for Full Closure

The ultimate objective is the integration of these subsystems into a highly reliable and largely autonomous ECLSS. The current state-of-the-art, as exemplified by the ACLS, represents a hybrid system—it closes a significant portion of the loop but is not fully closed due to the venting of methane, which contains valuable hydrogen atoms [1]. This hydrogen loss must be compensated by the electrolysis of resupplied water from Earth, creating a critical dependency.

Future research is directed towards achieving 100% oxygen recovery from metabolic COâ‚‚. This requires addressing the hydrogen loss in the Sabatier process. Promising paths include:

• Bosch Reaction: An alternative to the Sabatier process that produces solid carbon instead of methane, thereby retaining all hydrogen within the water product. The main challenge is managing the solid carbon, which can foul the reactor.
• Methane Pyrolysis: Coupling a Sabatier reactor with a secondary unit that "cracks" methane (CHâ‚„) into solid carbon and hydrogen gas, with the hydrogen being recycled back to the Sabatier reactor. This would create a fully closed carbon-oxygen cycle.
• In-Situ Resource Utilization (ISRU): For planetary surfaces, future systems could extract hydrogen from local resources, such as water ice on the Moon or Mars, to offset the hydrogen vented as methane and achieve full loop closure with local materials [5].

System reliability is paramount, as failure can be catastrophic. Strategies such as redundant components, regular maintenance protocols, and thorough ground-based testing are employed to ensure these systems can operate continuously for years with minimal intervention [5] [6]. As we venture further into the solar system, the critical role of a fully closed carbon loop will only increase, forming the very foundation of sustainable human presence in space.

Closing the carbon loop is a fundamental challenge for advanced life support systems (LSS) required for long-duration human space exploration. These systems must maintain a breathable atmosphere, provide sustenance, and manage waste within the isolated environment of a spacecraft or planetary habitat. The core of this challenge lies in effectively managing carbon dioxide (CO₂) produced by crew respiration and various processes, converting it from a waste product into valuable resources. This technical guide details the three core technological components—concentration, reduction, and oxygen generation—that work in concert to achieve carbon loop closure. The integration of these processes enables the creation of a self-sustaining ecosystem, reducing reliance on Earth-based resupply and enabling ambitious missions to the Moon, Mars, and beyond [7] [8].

The urgency for developing robust LSS is underscored by data from the Global Carbon Budget 2024, which shows atmospheric COâ‚‚ concentrations reached 419.31 ppm in 2023, with preliminary data for 2024 suggesting a rise to 422.45 ppm [9]. In the confined environment of a space habitat, preventing the accumulation of COâ‚‚ is immediately critical to crew health, while the subsequent conversion of this COâ‚‚ is crucial for long-term mission sustainability. Research and development in this field, exemplified by consortia such as NASA-funded initiatives and the European MELiSSA (Micro-Ecological Life Support System Alternative) project, are focused on creating efficient, reliable, and energy-effective systems for these purposes [7] [8].

Carbon Dioxide Concentration

The first step in closing the carbon loop is the efficient removal and concentration of COâ‚‚ from the cabin atmosphere. This process prevents the buildup of toxic COâ‚‚ levels and provides a concentrated stream for downstream reduction processes. Traditional methods have relied on physical adsorption materials like zeolites, but recent innovations focus on increasing efficiency and lowering the energy required for regeneration.

Advanced Materials for COâ‚‚ Capture

A groundbreaking development in this field is the creation of Micro/Nano-Reconfigurable Robots (MNRMs) for intelligent carbon management. These materials are not robots in a macroscopic sense but are molecular-scale systems designed to act autonomously in response to environmental cues. As detailed in recent research, MNRMs can capture COâ‚‚ with high capacity and regenerate at remarkably low temperatures [10].

Table 1: Performance Metrics of COâ‚‚ Capture Technologies

Technology/Material	COâ‚‚ Adsorption Capacity	Regeneration Temperature	Key Advantages
Micro/Nano-Reconfigurable Robot (MNRM)	6.19 mmol g⁻¹	55 °C	Ultralow regeneration energy, non-contact magnetic actuation, prevents local overheating [10]
Temperature-Sensitive Fiber-Based Sorbents	>6 mmol g⁻¹	~60 °C	Class-leading energy efficiency for solid amines [10]
Ag/UiO-66 MOF	1.14 mmol g⁻¹	Photothermal (90.5% release)	Utilizes solar energy for regeneration [10]
Liquid Amines	Varies	>110 °C	Established technology, but high energy penalty and health risks from amine leakage [10]

The MNRM is synthesized from a cross-linked network of cellulose nanofibers (CNF), polyethyleneimine (PEI) as the CO₂-hunting amino group provider, Pluronic F127 (F127) as a temperature-sensitive molecular switch, graphene oxide (GO) as a thermally conductive bridge, and Fe₃O₄ nanoparticles (NPs) as a photothermal conversion and magnetically-driven engine [10]. The core innovation is its reconfigurability:

• Nano-reconfiguration: The thermosensitive F127 network undergoes a conformational transition. At lower temperatures, the molecular chains are extended, facilitating COâ‚‚ access to amine groups. Upon heating, the chains curl, altering the amino microenvironment's electrostatic potential and energy levels. This change weakens the interaction with adsorbed COâ‚‚ products, inhibiting the formation of stable urea derivatives and enabling regeneration at a mere 55 °C [10].
• Micro-reconfiguration: The Fe₃Oâ‚„ NPs allow the material to be manipulated by an external magnetic field. This enables non-contact movement and heat management, preventing local overheating during the photothermal regeneration process and significantly extending the material's service life [10].

The efficacy of this system was validated in a confined-space animal model, where MNRMs prolonged the survival time of mice by 54.61% compared to the control group, effectively mitigating the risk of hypercapnia-induced lung failure [10].

Experimental Protocol: Testing MNRM COâ‚‚ Adsorption Capacity

Objective: To determine the COâ‚‚ adsorption capacity and regeneration efficiency of the Micro/Nano-Reconfigurable Robot (MNRM) under controlled conditions. Materials:

• Synthesized MNRM material (e.g., MNRM-Fe₃Oâ‚„(20)/F127(15))
• Fixed-bed adsorption reactor connected to a thermogravimetric analyzer (TGA) or gas chromatograph (GC)
• COâ‚‚ gas supply (typically 0.5-1% COâ‚‚ in Nâ‚‚ to simulate cabin air)
• Water vapor generator
• Light source for photothermal testing (e.g., simulated solar light)
• Alternating magnetic field generator

Methodology:

• Preparation: The MNRM sample is placed in the reactor and pre-dried under a gentle Nâ‚‚ stream at 40°C for 30 minutes.
• Adsorption Cycle: A gas mixture of 0.5% COâ‚‚ in Nâ‚‚, saturated with 2% water vapor at 25°C, is passed through the reactor. The weight gain (via TGA) or the outlet COâ‚‚ concentration (via GC) is monitored until saturation is reached. The adsorption capacity (mmol g⁻¹) is calculated from this data [10].
• Regeneration Cycle: The COâ‚‚ feed is switched to pure Nâ‚‚. The regeneration is initiated using one of two methods:
  • Photothermal Method: The sample is irradiated with a light source (e.g., 1 sun intensity) while the temperature is recorded.
  • Magnetic Actuation Method: An alternating magnetic field is applied to actuate the Fe₃Oâ‚„ NPs, providing non-contact heating.
• Desorption Monitoring: The released COâ‚‚ is quantified. The cycle is repeated at least 10 times to assess the material's stability and capacity retention [10].

Diagram 1: MNRM COâ‚‚ Adsorption/Desorption Workflow.

Carbon Dioxide Reduction

Once captured and concentrated, COâ‚‚ can be reduced into valuable organic compounds that serve as precursors for food, bioplastics, and other materials. This process transforms a waste product into essential resources, enhancing the sustainability of the life support system. Two prominent technological approaches are Chemical Looping Combustion (CLC) and biological conversion via microbial biomanufacturing.

Chemical Looping Combustion (CLC)

CLC is a promising technology for managing COâ‚‚ emissions with an inherently low energy penalty for capture. While its primary application in a life support context could be for energy generation from waste carbon, its principle is highly relevant for achieving efficient combustion with near-pure COâ‚‚ output [11].

The process utilizes a metal oxide (the "oxygen carrier"), such as iron, nickel, or copper oxides, circulated between two reactors:

• Fuel Reactor: The metal oxide (MeO) reacts with a fuel (e.g., syngas from waste), oxidizing the fuel to COâ‚‚ and Hâ‚‚O while the metal oxide is reduced to its metal form (Me) or a lower oxide.
  • Reaction: Fuel (CₙHₘOₖ) + MeO → CO₂ + H₂O + Me
• Air Reactor: The reduced oxygen carrier (Me) is transferred to the air reactor, where it is re-oxidized by air.
  • Reaction: Me + Air (O₂ + N₂) → MeO + N₂

The key advantage is that the fuel reactor's exhaust stream is not diluted with nitrogen from the air, consisting primarily of COâ‚‚ and Hâ‚‚O. After water condensation, a nearly pure COâ‚‚ stream is obtained, ready for storage or, more pertinently for LSS, as a feedstock for biological reduction processes [11]. When using biofuels, this process can achieve negative emissions [11].

Table 2: Comparison of COâ‚‚ Reduction Pathways

Reduction Pathway	Principle	Products	Key Challenges
Chemical Looping Combustion (CLC)	Metal oxide-mediated fuel combustion without Nâ‚‚ dilution	Concentrated COâ‚‚ stream, energy	Oxygen carrier lifetime and reactivity, fuel flexibility [11]
Anaerobic Digestion (AD)	Microbial conversion of organic waste in absence of oxygen	Volatile Fatty Acids (VFAs), COâ‚‚	Controlling methane production, microbial community balance [7]
Phototrophic Biosystem	Cyanobacteria using light and CO₂ for photosynthesis	Oxygen, protein-rich biomass, PHA bioplastics, β-carotene [7]	Efficiency in space conditions (e.g., low gravity, radiation) [7]

Biological Reduction and Biomanufacturing

Biological systems offer a versatile pathway for COâ‚‚ reduction. The AD ASTRA consortium, for example, is developing an integrative system that links anaerobic digestion with a phototrophic biosystem [7].

• Anaerobic Digestion (AD): This process uses microbial communities to break down human waste. The research focuses on optimizing these communities to produce volatile fatty acids (VFAs) like acetate, instead of methane. These VFAs and the resulting COâ‚‚ are then used as feedstocks for the next stage [7]. SLU researchers use molecular and sequencing-based techniques to monitor the conversion of waste to VFAs and control methane production by analyzing the mcrA gene responsible for methane biosynthesis [7].
• Phototrophic Biosynthesis: Cyanobacteria and other microbes are engineered to consume the VFAs, COâ‚‚, and nutrients from processed wastewater. They produce oxygen, protein-rich biomass for food, polyhydroxyalkanoate (PHA) polymers for bioplastics, and valuable natural products like β-carotene [7].

Another innovative approach, termed Alternative Feedstock-driven In-Situ Biomanufacturing (AF-ISM), leverages local resources. It uses Martian or Lunar regolith simulants as a mineral source and anaerobically pretreated fecal waste as a nutrient source to support the microbial production of nutrients like lycopene by Rhodococcus jostii PET strain S6 (RPET S6). This process has been validated under microgravity conditions, achieving production levels comparable to those on Earth [12].

Experimental Protocol: Anaerobic Digestion for Volatile Fatty Acid Production

Objective: To establish and optimize an anaerobic digestion process for converting human waste into volatile fatty acids (VFAs) while suppressing methane production. Materials:

• Anaerobic bioreactor with temperature and pH control
• Inoculum (adapted anaerobic microbial consortium)
• Synthetic or real human waste feedstock
• Anaerobic chamber for sample handling
• Gas chromatograph (GC) for VFA analysis (e.g., acetate, propionate, butyrate)
• PCR machine and reagents for genetic analysis (e.g., mcrA gene quantification)

Methodology:

• Reactor Setup: The bioreactor is filled with inoculum and feedstock. Anaerobic conditions are maintained by sparging with Nâ‚‚/COâ‚‚. Temperature is kept at mesophilic ranges (~35°C), and pH is monitored and controlled [7].
• Process Monitoring:
  • Chemical Analysis: Liquid samples are taken regularly, centrifuged, and the supernatant is analyzed by GC to quantify VFA production and composition.
  • Genetic Analysis: DNA is extracted from sludge samples. Quantitative PCR (qPCR) is performed targeting the mcrA gene to monitor the abundance of methanogenic archaea. The goal is to manipulate conditions (e.g., pH, retention time) to minimize mcrA expression [7].
• Optimization: The hydraulic retention time (HRT) and organic loading rate (OLR) are varied to find the optimal balance for high VFA yield and low methane production. The microbial community structure is characterized using 16S rRNA sequencing to understand the factors driving efficient VFA production [7].

Diagram 2: Anaerobic Digestion to VFAs Process.

Oxygen Generation

The final component of the carbon loop is the regeneration of oxygen, which is vital for crew respiration. Oxygen can be produced abiotically through the electrolysis of water, or biotically through photosynthetic organisms.

Biological Oxygen Generation

Phototrophic organisms, such as cyanobacteria and algae, use light energy to split water molecules and reduce COâ‚‚, releasing oxygen as a byproduct. The AD ASTRA consortium engineers cyanobacterial strains to use the COâ‚‚ and VFAs from the anaerobic digestion process, simultaneously producing oxygen and valuable biomass [7]. A significant research focus is understanding how simulated low gravity affects these phototrophic metabolisms and bioproduction rates [7].

The AF-ISM process also contributes to oxygen generation as part of the microbial metabolism during lycopene production, demonstrating the integration of multiple life support functions within a single biological process [12].

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Research Materials for Carbon Loop Closure Experiments

Reagent/Material	Function/Application	Example Use Case
Pluronic F127	Temperature-sensitive molecular switch	Enables low-temperature (55°C) regeneration of MNRM CO₂ sorbents by undergoing conformational change [10]
Polyethyleneimine (PEI)	COâ‚‚ "molecular hunter"; provides amine groups for chemical COâ‚‚ adsorption	Primary functional group in MNRMs and other solid amine sorbents for capturing COâ‚‚ [10]
Fe₃O₄ Nanoparticles	Photothermal conversion and magnetically-driven engine	Provides non-contact heating and actuation in MNRMs for energy-efficient sorbent regeneration [10]
mcrA Gene Primers	Genetic marker for methanogenic archaea	Used in qPCR to monitor and suppress methane production in anaerobic digesters, steering products toward VFAs [7]
Lunar/Martian Regolith Simulants	Analog for extraterrestrial mineral sources	Serves as a source of essential minerals (e.g., P, S, K, Mg) for microbial growth media in ISRU experiments (e.g., AF-ISM) [12]
Rhodococcus jostii PET S6	Engineered microbial chassis for bioproduction	Upcycles plastic hydrolysate or uses regolith minerals to produce lycopene; a candidate for off-world biomanufacturing [12]
Ripk1-IN-14	Ripk1-IN-14, MF:C25H25F2N3O2, MW:437.5 g/mol	Chemical Reagent
Pap-IN-1	Pap-IN-1, MF:C25H44NO4P, MW:453.6 g/mol	Chemical Reagent

The path to sustainable long-duration spaceflight hinges on the robust integration of the core components: COâ‚‚ concentration, reduction, and oxygen generation. The field is moving beyond simple, energy-intensive physical-chemical systems toward hybrid and fully biological solutions that offer greater closure of the carbon loop. Innovations like micro/nano-reconfigurable robots for low-energy COâ‚‚ capture, chemical looping for efficient combustion, and engineered microbial consortia that transform waste into food, oxygen, and materials represent the cutting edge of life support system research. The integration of these technologies, supported by in-situ resource utilization, will be the cornerstone of future closed-loop life support systems, enabling humanity to become a multi-planetary species.

The development of advanced, closed-loop life support systems is a critical prerequisite for long-duration human space exploration. These systems must efficiently regenerate vital resources—oxygen, water, and food—from astronaut metabolic waste, minimizing reliance on resupply from Earth. The core challenge lies in achieving robust carbon loop closure, wherein exhaled carbon dioxide (CO₂) is reconstituted into breathable oxygen and edible biomass. On Earth, the planet's natural ecosystems have performed this precise function for millennia through the global carbon cycle. This whitepaper examines terrestrial carbon cycle processes as analogue systems to inform the engineering of bioregenerative life support systems (BLSS) for space applications. By analyzing the mechanisms that govern carbon storage and flux in Earth's biosphere, researchers can derive design principles, identify potential bottlenecks, and develop strategies for creating stable, long-term life support systems for missions to the Moon, Mars, and beyond [13].

Terrestrial Carbon Cycle Fundamentals

The terrestrial carbon cycle represents a planetary-scale, closed-loop life support system, seamlessly transferring carbon between the atmosphere, biosphere, and pedosphere (soil). Understanding its components and fluxes is foundational to emulating its efficiency in a controlled habitat.

Key Carbon Pools and Fluxes

The major stocks and flows of carbon create a dynamic equilibrium. Carbon pools are reservoirs where carbon is stored for varying durations, while carbon fluxes are the rates of transfer between these pools [14]. The primary fluxes driving the cycle are gross primary production (GPP) and ecosystem respiration.

Table 1: Major Terrestrial Carbon Pools and Fluxes (Approximated from Global Carbon Budget 2025) [14] [15]

Component	Estimated Magnitude (Pg C)	Description
Atmospheric Pool	~900 Pg C	Carbon stored as COâ‚‚ and other gases; the immediate source for photosynthesis.
Vegetation Pool	~450-650 Pg C	Carbon incorporated into plant biomass (leaves, stems, roots).
Soil Pool	~1500-2400 Pg C	Carbon stored as organic matter in soils; the largest terrestrial pool.
Gross Primary Production (GPP)	~113 Pg C yr⁻¹	Total CO₂ captured by plants via photosynthesis per year.
Ecosystem Respiration	~111 Pg C yr⁻¹	Total CO₂ released back to the atmosphere by plants and soil organisms.
Net Land Sink (S_LAND)	1.9 ± 1.1 Pg C yr⁻¹ (2024)	Net annual CO₂ uptake by land; the residual of GPP minus respiration and disturbances.

Critical Processes for Loop Closure

For carbon loop closure, several biological processes are paramount:

• Photosynthesis: The foundational process that captures atmospheric COâ‚‚ and converts it into organic compounds using light energy. This is the analogue for food and biomass production in a BLSS [14].
• Respiration: The process in plants and soil microorganisms that metabolizes organic carbon, releasing COâ‚‚ back into the atmosphere. In a BLSS, this must be carefully managed to balance the system [14].
• Soil Organic Matter (SOM) Formation and Decomposition: This represents a critical carbon stabilization process. In a BLSS, an analogous "soil" or compost system could be key for recycling solid waste and stabilizing carbon cycles over the long term [16].

The following diagram illustrates the core logical relationships and carbon fluxes within the terrestrial carbon cycle that serve as the model for life support system closure.

Quantitative Framework for Analogue Analysis

Translating terrestrial cycle insights into engineering parameters requires a rigorous quantitative framework. The following data, synthesized from current global budgets and operational space systems, provides critical benchmarks for BLSS development.

Table 2: Carbon Flux and Sequestration Rates in Terrestrial and BLSS Contexts [1] [14] [15]

System / Process	Rate / Capacity	Relevance to BLSS Design
Global Net Land Sink (S_LAND)	1.9 ± 1.1 Pg C yr⁻¹	Demonstrates planetary-scale capacity for anthropogenic CO₂ offsetting.
Ocean Carbon Sink (S_OCEAN)	3.4 ± 0.4 Pg C yr⁻¹	Analogous to physico-chemical CO₂ scrubbing systems.
ESA ACLS Water Savings	~400 liters/year	Quantifies resupply mass reduction via COâ‚‚ recycling to Oâ‚‚.
ESA ACLS Oxygen Production	Supply for 3 astronauts	Benchmarks for current state-of-the-art mechanical closure.
Free-Air COâ‚‚ Enrichment (FACE) NPP boost	10-25% initial enhancement [16]	Informs expectations for crop yield response to elevated COâ‚‚ in habitats.

Experimental Protocols for Carbon Cycle Research

Methodologies developed for terrestrial carbon science provide robust experimental templates for BLSS component testing.

Free-Air COâ‚‚ Enrichment (FACE) Experiments

Objective: To quantify the long-term response of ecosystem productivity (NPP) and carbon storage to elevated atmospheric COâ‚‚ levels, simulating the high-COâ‚‚ environments anticipated in space habitats [16].

Detailed Methodology:

• Site Establishment: Select a representative ecosystem (e.g., forest, grassland). Arrange multiple experimental plots in a randomized block design. For example, the Duke and ORNL FACE experiments used plots of 25-30m diameter [16].
• COâ‚‚ Treatment Application: Construct a network of pipes and towers to release pure COâ‚‚ around the treatment plots. Use a computerized control system that monitors wind speed and direction in real-time to adjust COâ‚‚ release, maintaining a preset elevated concentration (e.g., +200 ppm above ambient) [16].
• Data Collection:
  • Carbon Fluxes: Measure Net Primary Production (NPP) via annual harvest of vegetation or allometric tree growth measurements. Quantify soil COâ‚‚ efflux (respiration) using automated soil chambers.
  • Carbon Stocks: Conduct periodic biomass inventories (above and below-ground). Collect soil cores to depth for analysis of Soil Organic Carbon (SOC) content.
  • Nutrient Cycling: Analyze foliar and soil N content. Measure rates of soil net nitrogen mineralization (f~Nmin~) through in-situ incubation assays [16].
• Data Analysis: Contrast C and N cycle responses (e.g., NPP, NUE, f~Nup~) between elevated COâ‚‚ and ambient control plots over a multi-year period to assess sustainability and the emergence of nutrient limitations [16].

Eddy Covariance Flux Measurements

Objective: To provide continuous, direct measurement of net ecosystem-atmosphere exchange of COâ‚‚ (NEE) for model validation.

Detailed Methodology:

• Instrumentation Setup: Erect a tall tower (exceeding canopy height) equipped with a 3D sonic anemometer (measures wind speed) and a high-precision, fast-response infrared gas analyzer (IRGA) (measures COâ‚‚ and Hâ‚‚O concentration).
• Data Acquisition: Collect raw data on wind vectors and gas concentrations at high frequency (10-20 Hz).
• Flux Calculation: Process the high-frequency data to compute the net ecosystem exchange (NEE) as the mean of the covariance between vertical wind velocity and COâ‚‚ concentration.
• Gap-Filling and Partitioning: Apply statistical models to fill data gaps from instrument failure or non-ideal turbulence. Partition NEE into its component fluxes, Gross Primary Production (GPP) and Ecosystem Respiration (R~eco~).

The workflow for implementing these key experiments is methodically structured as follows:

The Scientist's Toolkit: Research Reagents and Materials

Research at the intersection of terrestrial carbon science and BLSS development relies on a suite of specialized reagents, instruments, and models.

Table 3: Essential Research Tools for Carbon Cycle and BLSS Investigations

Tool / Reagent	Function	Application Context
Stable Isotopes (¹³C, ¹⁵N)	Trace the fate of carbon and nutrients through ecosystems.	Quantifying C allocation in plants; tracing waste N in BLSS recycling loops.
Fast-Response IRGA	Measures turbulent fluctuations of COâ‚‚ and Hâ‚‚O concentrations.	Core sensor for eddy covariance towers; monitoring cabin atmosphere.
Dynamic Global Vegetation Models (DGVMs)	Simulate vegetation dynamics and biogeochemical cycles.	Projecting long-term BLSS stability; testing N limitation scenarios [15] [16].
Amine-Based Sorbents	Chemically trap and concentrate COâ‚‚ from the air.	COâ‚‚ removal and concentration in systems like ESA's ACLS [1].
Sabatier Reactor	Catalytically converts COâ‚‚ and Hâ‚‚ into CHâ‚„ and Hâ‚‚O.	Key physico-chemical component for closing the oxygen loop [1].
Leaf Fluorometer	Measures chlorophyll fluorescence, a proxy for photosynthetic efficiency.	Monitoring plant health and COâ‚‚ response in BLSS crop chambers.
D-Arabinose-d2	D-Arabinose-d2, MF:C5H10O5, MW:152.14 g/mol	Chemical Reagent
Antifungal agent 62	Antifungal agent 62, MF:C23H25N3S, MW:375.5 g/mol	Chemical Reagent

Carbon-Climate Feedback and BLSS Stability

A primary lesson from terrestrial carbon science is that carbon-cycle processes are highly sensitive to environmental conditions, leading to complex feedback loops. The phenomenon of Progressive Nitrogen Limitation (PNL) is a critical feedback with direct implications for BLSS longevity [16]. In terrestrial ecosystems, eCOâ‚‚ initially boosts plant growth (NPP), but this increased growth requires more nitrogen. When N is limited, the extra plant biomass and soil carbon produced sequester available N, making it less accessible for further growth. This can cause the initial COâ‚‚ fertilization effect to decline over time, as observed at the ORNL FACE site [16].

In a BLSS context, this translates to a risk that enhanced food production efforts could deplete available nutrients, leading to a gradual decline in crop yields unless robust nutrient recycling systems—analogous to soil microbial networks and decomposers—are in place to regenerate essential elements from plant and human waste.

Synthesis and Research Outlook

Integrating terrestrial carbon cycle principles into BLSS engineering reveals a clear pathway toward robust carbon loop closure. The operational ESA Advanced Closed Loop System (ACLS), which combines amine-based COâ‚‚ capture with a Sabatier reactor and electrolysis, represents a significant achievement in physico-chemical closure of the oxygen loop, recovering about 50% of the COâ‚‚ and saving 400 liters of water annually [1]. This mirrors the function of the inorganic terrestrial carbon cycle.

The future challenge lies in fully integrating the biological component—the food production system—in a way that mimics the resilient, self-sustaining nature of Earth's ecosystems. Priority research areas, supported by ongoing funding initiatives from NASA and the DOE [17], must focus on:

• Closing the Nutrient Loop: Developing efficient waste processing systems to convert solid and liquid wastes into plant-available nutrients, preventing PNL in BLSS agriculture.
• Multi-Kingdom Integration: Engineering balanced systems that incorporate plants, microbes, and potentially other organisms to create a more resilient and self-regulating ecology.
• Modeling and Validation: Using terrestrial carbon cycle models, especially those that incorporate C-N interactions [16], to predict BLSS behavior and guide design before costly prototyping and in-situ testing.

By continuing to treat Earth's biosphere as the ultimate analogue system, researchers can extract the fundamental principles needed to build the life-support ecosystems that will sustain humanity as we venture into the solar system.

The pursuit of deep space exploration, encompassing missions to the Moon and Mars, is fundamentally constrained by the requirement for life support systems that are both highly reliable and self-sustaining. Unlike missions in low Earth orbit (LEO), where resupply from Earth is feasible, deep space habitats require near-perfect closure of mass loops, particularly for critical elements like carbon, oxygen, and water. Carbon dioxide (COâ‚‚), a primary metabolic waste product of human respiration, must be efficiently captured and recycled into breathable oxygen and other valuable resources. This whitepaper details the current state-of-the-art in Advanced Life Support Systems (ALS), tracing the evolution from operational systems aboard the International Space Station (ISS) to the groundbreaking technologies and simulation frameworks under development for future deep space habitats. The central thesis is that closing the carbon cycle is not merely an incremental improvement but a paradigm shift essential for long-duration, Earth-independent human presence in space.

Current State-of-the-Art: ISS-Based Systems

The International Space Station serves as the primary testbed for validating life support technologies in a sustained microgravity environment. After nearly 25 years of continuous human presence, the systems aboard the ISS represent the most advanced closed-loop life support capabilities ever operationally deployed [18] [19].

The Advanced Closed Loop System (ACLS)

A cornerstone of current carbon loop closure efforts on the ISS is the Advanced Closed Loop System (ACLS), developed by the European Space Agency (ESA). This system is a significant step towards revitalizing the atmosphere within the spacecraft by recycling carbon dioxide into oxygen [1].

The ACLS is integrated into a standard International Standard Payload Rack, measuring approximately 2 meters high, 1 meter wide, and 85.9 cm deep. It performs three major functions [1]:

• Carbon Dioxide Concentration (CCA): The system concentrates COâ‚‚ from the cabin air using unique amine-developed beads, maintaining acceptable COâ‚‚ levels for the crew.
• Carbon Dioxide Reprocessing (CRA): The concentrated COâ‚‚ is then fed into a Sabatier reactor, where it reacts with hydrogen (a byproduct of oxygen generation) over a catalyst to form water and methane.
• Oxygen Generation (OGA): The water produced by the Sabatier reactor is subsequently split via electrolysis into breathable oxygen and hydrogen. The oxygen is returned to the cabin, and the hydrogen is fed back into the Sabatier reactor.

This process allows the ACLS to recycle about 50% of the CO2, saving approximately 400 liters of water that would otherwise need to be launched from Earth each year. The methane produced is vented overboard, which is the primary reason the system does not achieve 100% carbon recovery [1].

Water Recovery and Oxygen Generation

Parallel developments on the ISS have focused on closing the water loop, which is intrinsically linked to oxygen production. The U.S. segment of the ISS has achieved 98% water recovery, a critical benchmark for missions beyond LEO where resupply is not feasible [18]. This recovered water is a key feedstock for the Oxygen Generation System (OGS), which uses electrolysis to produce oxygen for the crew. Maintenance of these systems, such as the replacement of components and advanced hydrogen sensors in the OGS, is a routine but vital activity for station operations [20].

In-Space Production and Resource Utilization

Beyond atmospheric revitalization, the ISS is pioneering technologies to utilize local resources, a concept known as In-Situ Resource Utilization (ISRU). Key advancements include [18]:

• Scalable Crop Production: Over 50 species of plants have been grown aboard the station using aeroponic and hydroponic systems. These systems are vital for producing fresh food and nutrients, and they contribute to carbon cycling by consuming COâ‚‚ and producing oxygen through photosynthesis.
• In-Space Manufacturing: 3D printing has been successfully demonstrated for creating tools and parts on-demand. The European Space Agency has 3D-printed the first metal part on the station, a step towards using recycled materials or even lunar and Martian regolith (soil) for construction. This reduces the need to launch supplies from Earth and supports habitat construction.

Table 1: Key Performance Metrics of Current ISS Life Support Systems

System/Technology	Key Metric	Performance Value	Significance for Deep Space
Advanced Closed Loop System (ACLS)	COâ‚‚ Recycling Rate	~50% [1]	Demonstrates core technology for Oâ‚‚ recovery; highlights need to close methane venting loop.
Water Recovery System	Water Recovery Rate	98% [18]	Meets target for water independence on long-duration missions beyond LEO.
Oxygen Generation System	Feedstock	Recovered Water [18]	Directly links water and oxygen loops, reducing Earth-based resupply.
Food Production	Plant Species Grown	>50 [18]	Tests scalable crop systems for fresh food and supplemental atmospheric revitalization.

Experimental Protocols & Methodologies

The advancement of life support systems relies on rigorous experimentation, both in space and on the ground. The following protocols detail the current approaches for testing and validating these technologies.

Protocol: Advanced Closed Loop System Operation

Objective: To concentrate cabin COâ‚‚ and convert it into oxygen, thereby reducing the reliance on Earth-based resupply of water for oxygen generation [1].

• Carbon Dioxide Concentration: Cabin air is passed through a column containing amine-developed beads, which selectively trap COâ‚‚ molecules.
• Desorption and Processing: Steam is applied to the saturated beads to release the concentrated COâ‚‚ stream.
• Sabatier Reaction: The concentrated COâ‚‚ is mixed with hydrogen (Hâ‚‚) and passed over a catalyst in a Sabatier reactor. The reaction COâ‚‚ + 4Hâ‚‚ → CHâ‚„ + 2Hâ‚‚O occurs, producing methane (CHâ‚„) and water (Hâ‚‚O).
• Water Electrolysis: The produced water is condensed and fed into an electrolysis assembly, where an electric current splits it into oxygen (Oâ‚‚) and hydrogen (Hâ‚‚). The oxygen is returned to the cabin atmosphere.
• Product Management: The hydrogen is recycled to the Sabatier reactor. The methane is vented into space.

Protocol: HabSim Virtual Testbed for Habitat Resilience

Objective: To model disruptive events in a deep space habitat and evaluate the efficacy of different contingency strategies for restoring system functionality, particularly during the critical transition from a dormant to a crewed state [21].

• System Modeling: A high-fidelity numerical model of a deep space habitat is created, integrating key subsystems including pressure control, thermal regulation, power management, and atmospheric composition.
• Disruption Introduction: A disruptive event (e.g., a micrometeoroid impact causing a pressure leak and dust accumulation on radiators) is introduced into the simulation.
• Propagation Analysis: The testbed models how the initial disruption propagates through the interconnected habitat systems, identifying cascading failures.
• Contingency Strategy Testing: Researchers implement and test different repair and recovery strategies. These can involve:
  • Single-Contingency Responses: Addressing one primary failure (e.g., patching a leak).
  • Multiple-Contingency Responses: Addressing multiple, interconnected failures simultaneously (e.g., patching a leak, removing dust from radiators, and compensating for temperature and pressure decreases).
• Strategy Optimization: Data from the simulations are used to identify the most effective strategies for restoring the habitat to a safe and fully functional state. The research has demonstrated that multiple-contingency responses are typically required for a successful recovery [21].

Diagram 1: Habitat resilience testing workflow.

Future Directions: Technologies for Deep Space Habitats

As missions venture farther from Earth, the technologies tested on the ISS are being refined and integrated with novel concepts to create truly sustainable habitats for the Moon, Mars, and beyond.

Closing the Carbon Loop: Multi-Product COâ‚‚ Utilisation

Current research is exploring pathways to achieve a higher degree of carbon cycle closure by converting COâ‚‚ into valuable products beyond just oxygen. Multi-product Carbon Capture and Utilisation (CCU) configurations represent a promising avenue. Studies have evaluated systems where COâ‚‚ is captured and converted into dimethyl ether (DME) and polyols simultaneously (parallel configuration) or in consecutive cycles (cascade configuration) [22]. When combined with a small amount of COâ‚‚ storage (CCUS), these multi-product systems can achieve significant reductions in climate change potential (up to -18% compared to a reference system) while remaining economically feasible, primarily due to the replacement of fossil feedstocks with utilized COâ‚‚ [22].

Autonomous and Resilient Habitat Operations

The Resilient Extra-Terrestrial Habitat institute (RETHi) is pioneering the development of smart habitats that can autonomously anticipate, adapt to, and recover from disruptions. As simulated using tools like HabSim, future habitats will require complex, multi-contingency response plans to handle events like micrometeoroid impacts, fires, or moonquakes [21]. The research demonstrates that a single response is insufficient; a coordinated strategy addressing dust removal, temperature control, and pressure stabilization is necessary for a successful recovery. This resilience is critical for maintaining a stable, life-sustaining environment where carbon loops remain closed even in the face of failures.

Biological Life Support Systems & In-Situ Resource Utilization

Biological systems will play an increasingly important role in closing carbon loops. Beyond supplemental food production, future research will focus on integrating microbial processes and higher plant growth to create a more balanced and robust Ecological Life Support System (ELSS). These systems can contribute to waste processing, water purification, and atmospheric management. Furthermore, the use of local resources, such as Martian COâ‚‚ for synthetic fuel production or lunar regolith for 3D printing habitats, will be essential for achieving long-term sustainability and reducing the mass that must be launched from Earth [18].

Table 2: Comparative Analysis of Carbon Management Technologies

Technology	Current TRL* (ISS)	Target TRL (Deep Space)	Key Challenge	Carbon Loop Impact
Sabatier Process (ACLS)	High (8-9) [1]	9	Venting of methane (CHâ‚„) breaks the carbon loop.	Partial (~50% recovery)
Bosch Reaction	Medium (4-5)	6-7	Carbon deposition clogs the reactor, requiring maintenance.	High (Theoretically 100%)
Photobioreactors (Algae)	Medium (4-5)	7	System volume, power, and stability in microgravity.	High (Converts COâ‚‚ to Oâ‚‚ and biomass)
Multi-Product CCU (e.g., DME)	Low (2-3) [22]	5-6	System complexity and energy efficiency for deep space.	High (Converts COâ‚‚ to useful products)
*Technology Readiness Level

The Scientist's Toolkit: Research Reagent Solutions

The development and testing of advanced life support systems rely on a suite of specialized materials and reagents.

Table 3: Key Research Reagents and Materials for Life Support Systems

Reagent/Material	Function	Example in Context
Amine-based Sorbents	Chemically captures and concentrates COâ‚‚ from the cabin atmosphere.	The "unique amine-developed beads" used in the ACLS's Carbon Dioxide Concentration Assembly (CCA) [1].
Sabatier Catalyst	Facilitates the chemical reaction between COâ‚‚ and Hâ‚‚ to produce methane and water.	A nickel or ruthenium-based catalyst used in the Carbon Dioxide Reprocessing Assembly (CRA) of the ACLS [1].
Electrolyte for Electrolysis	A medium that conducts ions to facilitate the splitting of water into oxygen and hydrogen.	A solid polymer electrolyte (like Nafion) or a liquid alkaline solution used in the Oxygen Generation Assembly (OGA) [18] [1].
Microbial Cultures	Used to process waste, produce nutrients, or in bioprocessing of COâ‚‚.	Cultures of specific bacteria or cyanobacteria studied for waste recycling or food production in closed systems.
Plant Growth Media	A soil-less substrate for supporting plant growth in space.	Hydroponic nutrient solutions or aeroponic misters used to grow over 50 plant species on the ISS [18].
3D Printing Feedstock	Material for manufacturing tools and parts on-demand.	Recycled plastics or metals, with future potential for regolith-based composites, used in ISS 3D printers [18].
Dehydrocorydaline (hydroxyl)	Dehydrocorydaline (hydroxyl), MF:C22H25NO5, MW:383.4 g/mol	Chemical Reagent
Kouitchenside G	Kouitchenside G|Research Compound|RUO	Kouitchenside G is a research compound identified in a study for potential bioactivity. This product is For Research Use Only. Not for human or diagnostic use.

The journey from the International Space Station to future deep space habitats is marked by a critical, escalating requirement: the need to close mass loops, with carbon being a central element. The current state-of-the-art, exemplified by the ISS's 98% water recovery and the ACLS's 50% COâ‚‚ recycling, provides a formidable foundation. However, achieving the near-total closure required for Earth-independent exploration demands a new generation of technologies. The path forward will be paved by integrating physicochemical systems like multi-product CCU, biological systems for food and air revitalization, and resilient autonomous operations as modeled by tools like HabSim. Closing the carbon cycle is not a solitary technical hurdle but a systems-level challenge that will define the feasibility, safety, and sustainability of humanity's future as a deep-space species.

Technological Pathways and System Architectures for Carbon Recycling

In the context of Advanced Life Support (ALS) systems for long-duration space missions, achieving closure of the carbon loop is a fundamental challenge. Physical-Chemical (P/C) systems, particularly Sabatier reactors and electrolyzers, form the technological backbone for converting waste carbon dioxide into vital resources, thereby reducing dependence on Earth resupply. An ALS system's degree of closure is defined as the percentage of total resources provided by recycling, with higher closure dramatically reducing launch mass and enabling sustained human presence in space [23]. The core function of these P/C systems is to facilitate the Carbon Dioxide Reduction Assembly (CDRA), a critical process where metabolic COâ‚‚ is transformed into water and methane, which can subsequently be used for oxygen generation or as propellant [1]. This technical guide examines the operational principles, system integrations, and experimental methodologies that underpin these essential technologies for carbon loop closure in advanced life support systems.

Fundamental Principles and System Architectures

The Sabatier Reaction: Core Reaction and Thermodynamics

The Sabatier reaction is a well-established catalytic process that converts carbon dioxide and hydrogen into methane and water. Its fundamental reaction is:

CO₂ + 4H₂ → CH₄ + 2H₂O ΔH° = −165 kJ/mol

This highly exothermic reaction requires a catalyst, typically nickel-based, and operates at elevated temperatures (150-400°C) [24]. The reaction's significance in life support systems is twofold: it removes metabolic CO₂ from the cabin atmosphere and produces valuable water. According to Le Chatelier's principle, in-situ water removal during the reaction shifts the equilibrium toward higher CO₂ conversion, a key principle exploited in advanced membrane Sabatier systems [24]. In the broader carbon loop, this methane can be utilized as rocket propellant for return journeys, while the water is recycled for human consumption or electrolysis to regenerate oxygen [24].

Electrolysis: Oxygen Generation and Hydrogen Production

Electrolysis systems complement Sabatier reactors by providing the hydrogen required for the methanation process while simultaneously generating breathable oxygen for crewed missions. Two primary electrolyzer technologies are relevant for space applications:

• Solid Oxide Electrolyzer Cells (SOEC): Employed in co-electrolysis of steam and COâ‚‚ to produce syngas (Hâ‚‚ + CO), which is subsequently methanated. SOEC-based systems demonstrate higher exergy and power-to-gas efficiencies compared to PEM systems [25].
• Polymer Electrolyte Membrane Electrolyzer Cells (PEMEC): Used for hydrogen production via water electrolysis, with the hydrogen then fed to a Sabatier reactor for methane production. PEMEC systems benefit from lower operational temperatures and pressures [25].

Table 1: Comparative Analysis of Electrolyzer Technologies for Space Applications

Parameter	Solid Oxide Electrolyzer Cell (SOEC)	Polymer Electrolyte Membrane Electrolyzer Cell (PEMEC)
Process Type	Co-electrolysis of steam & COâ‚‚	Water electrolysis for Hâ‚‚ production
Operating Temperature	High temperature (~700-850°C)	Low temperature (~50-100°C)
Efficiency	Higher exergy & power-to-gas efficiency	Lower efficiency but produces 1.2% more methane [25]
System Advantages	Lower electricity consumption; Direct COâ‚‚ processing	Less purchase cost; Longer life cycle; Faster response
Integration	With methanation reactor	With Sabatier reactor
LCOE (Based on LHV)	11% lower than PEMEC-based system [25]	Higher levelized cost of energy

System Integration Architectures for Carbon Loop Closure

The integration of Sabatier reactors with electrolyzers creates synergistic systems that enhance overall carbon loop closure. Two prominent architectures have emerged:

• SOEC with Methanation Reactor: This configuration relies on co-electrolysis of steam and carbon dioxide to produce syngas, which is subsequently converted to methane in a separate methanation unit. The system leverages the high efficiency of co-electrolysis, where the application of steam/COâ‚‚ co-electrolysis demonstrates 54-66% enhancements in energy efficiencies compared to steam electrolysis alone for synthetic natural gas production [25].

• PEMEC with Sabatier Reactor: In this architecture, a PEM electrolyzer produces hydrogen, which is then combined with COâ‚‚ in a Sabatier reactor. While consuming more electricity relative to SOEC, this system benefits from PEMEC's lower purchase cost and longer lifecycle, making it attractive for certain mission profiles [25].

The integration of these systems has been successfully demonstrated in operational space hardware, notably in the Advanced Closed Loop System (ACLS) on the International Space Station. The ACLS incorporates a Carbon Dioxide Concentration Assembly (CCA), Oxygen Generation Assembly (OGA) electrolyzer, and Carbon Dioxide Reprocessing Assembly (CRA) Sabatier reactor, collectively capable of recycling 50% of recovered COâ‚‚ and producing oxygen for three astronauts [1].

Figure 1: Carbon Loop Closure via Integrated Sabatier-Electrolysis System. This diagram illustrates the principal mass flows in a closed-loop life support system, showing how metabolic COâ‚‚ and water are processed to recover breathable oxygen and produce water, thereby reducing resupply requirements.

Advanced System Designs and Performance Optimization

Membrane Sabatier Reactor Technology

Recent innovations have introduced membrane Sabatier systems that significantly enhance COâ‚‚ conversion efficiency and system reliability. These systems integrate a catalytic reactor with a water vapor permselective membrane tube, typically composed of NaA zeolite, which continuously removes water vapor from the reaction zone [24]. This design leverages Le Chatelier's principle to drive the equilibrium toward higher methane yield while simultaneously addressing the challenge of water-caused catalyst sintering.

The performance advantages of membrane Sabatier systems are substantial:

• Enhanced COâ‚‚ Conversion: At 300°C, membrane reactors achieve 99% COâ‚‚ conversion with 100% CHâ‚„ selectivity, exceeding thermodynamic equilibrium conversion calculated based on feed conditions [24].
• Superior Space-Time Yield: Membrane systems demonstrate a space-time yield (STY) of CHâ‚„ reaching 1947 mmol g⁻¹ h⁻¹ with a space velocity of 342,857 mL gcat⁻¹ h⁻¹ at 300°C [24].
• Long-term Stability: The removal of Hâ‚‚O mitigates Hâ‚‚O-caused sintering of the catalyst, resulting in no obvious deactivation during long-term stability tests of 10 days at 240°C [24].
• Microgravity Compatibility: The membrane-based water separation eliminates the need for complex centrifugal phase separators required in microgravity environments, reducing system complexity, power consumption, and noise pollution [24].

Table 2: Performance Comparison of Conventional vs. Membrane Sabatier Systems

Performance Parameter	Conventional Sabatier System	Membrane Sabatier System
CO₂ Conversion at 300°C	~80-85% (thermodynamic equilibrium)	99% (exceeds equilibrium) [24]
CH₄ Selectivity at 300°C	>95%	100% [24]
Space-Time Yield of CH₄	Varies with conditions	1947 mmol g⁻¹ h⁻¹ [24]
Long-term Stability	Gradual deactivation from Hâ‚‚O exposure	No deactivation after 10 days [24]
Microgravity Adaptation	Requires centrifugal separator	Integrated membrane separation [24]
System Complexity	Higher due to separate gas-liquid separator	Simplified with integrated membrane [24]

Multiple-Inlet Reactor Configurations for Biogas Upgrading

Distributed feeding strategies in multiple-inlet fixed bed reactors represent another advancement in Sabatier reactor design. Parametric studies demonstrate that biogas dosing through several side inlets significantly improves methane selectivity compared to conventional single-inlet feeding configurations [26]. The effect becomes more pronounced as the number of feeding points increases, with higher inlet counts leading to greater selectivity enhancements toward the desired CHâ‚„ product [26].

Operational parameters significantly influence system performance in multiple-inlet configurations:

• Temperature and Hâ‚‚:COâ‚‚ Ratio: While these parameters influence selectivities as predicted at low conversions, space velocity (WHSV) emerges as the most critical factor for selectivities at higher conversion levels [26].
• Biogas Composition: Interestingly, modifying the biogas CHâ‚„:COâ‚‚ ratio in the broad range of 55-70 vol% methane shows no significant changes in reaction performance, indicating robust operation across varying feed compositions [26].

Experimental Protocols and Methodologies

Membrane Sabatier System Fabrication and Testing

The development of advanced membrane Sabatier systems requires precise fabrication and characterization protocols. The following methodology outlines the key steps for creating and evaluating a membrane Sabatier system:

Membrane Synthesis Protocol:

• Support Preparation: Use a ceramic hollow tube support (50mm length, 12mm OD, 8mm ID, 500nm pore size) with a rough surface to enhance seed immobilization [24].
• Seed Solution Preparation: Synthesize NaA zeolite seeds (50-200nm) via hydrothermal synthesis, confirmed through EDX elemental mapping showing presence of Al, O, Na, and Si elements [24].
• Seed Coating: Employ dip-coating method by pre-heating support to 120°C, dipping into zeolite seed solution for 20 seconds, followed by drying at 100°C for 1 hour [24].
• Annealing Process: Anneal coated support at 180°C for 12 hours to chemically bond zeolite seeds to the support surface via dehydration of surface hydroxyl groups [24].
• Membrane Growth: Prepare synthesis solution with molar composition 1Alâ‚‚O₃:5SiOâ‚‚:50Naâ‚‚O:1000Hâ‚‚O and crystallize on seeded support at 90°C for 20 hours [24].
• Membrane Characterization: Verify successful membrane formation through EDX elemental mapping, XRD profiling, and water contact angle measurements to confirm hydrophilicity [24].

Catalyst Preparation and Reactor Integration:

• Catalyst Synthesis: Prepare ZrOâ‚‚-supported Ni catalyst using impregnation method, followed by calcination and reduction steps to activate the catalyst [24].
• Reactor Assembly: Load catalyst into the annular space between the membrane tube and outer reactor shell, ensuring proper sealing for gas containment [24].
• System Testing: Evaluate performance across temperature range (150-300°C), space velocities (12,000-342,857 mL gcat⁻¹ h⁻¹), and Hâ‚‚:COâ‚‚ ratios (4:1), monitoring COâ‚‚ conversion, CHâ‚„ selectivity, and long-term stability [24].

Figure 2: Membrane Sabatier System Fabrication Workflow. This diagram outlines the key synthetic and assembly steps required to fabricate and validate a membrane Sabatier reactor, from initial support preparation through final performance testing.

Distributed Feeding Reactor Experimental Methodology

For investigating multiple-inlet reactor configurations, the following experimental approach provides comprehensive performance data:

Reactor Configuration Protocol:

• Reactor Setup: Configure fixed-bed reactor with multiple side inlets for distributed feeding of reactants, varying the number of inlets (N) to assess impact on selectivity [26].
• Parameter Variation: Systematically study operational parameters including dosage degree of reactants, temperature, Hâ‚‚:COâ‚‚ ratios, and biogas composition (CHâ‚„:COâ‚‚ ratios from 55:45 to 70:30) [26].
• Performance Metrics: Monitor COâ‚‚ conversion, CHâ‚„ selectivity, and CO selectivity at various space velocities to determine optimal conditions [26].
• Comparative Analysis: Compare performance across different feeding configurations (biogas dosing, Hâ‚‚ dosing, conventional feeding) to quantify selectivity enhancements [26].

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 3: Essential Research Materials for Sabatier and Electrolysis Experiments

Material/Reagent	Specification/Composition	Primary Function in Research
NaA Zeolite Seeds	50-200 nm particle size, composition: Al, O, Na, Si elements [24]	Formation of water-permselective membranes for enhanced Sabatier reaction
Ceramic Support Tubes	12mm OD, 8mm ID, 500nm pore size, rough surface [24]	Structural substrate for membrane deposition and reactor assembly
Ni/ZrOâ‚‚ Catalyst	Nickel supported on zirconia substrate [24]	Catalyzing the Sabatier reaction with enhanced stability
Solid Amine Beads	Unique amine developed by ESA for human spaceflight [1]	COâ‚‚ concentration from cabin air in Advanced Closed Loop Systems
SOEC Co-electrolysis Cells	Solid Oxide Electrolyzer Cells for high-temperature operation [25]	Simultaneous electrolysis of steam and COâ‚‚ to syngas for methanation
PEMEC Stacks	Polymer Electrolyte Membrane Electrolyzer Cells [25]	Hydrogen production from water electrolysis for Sabatier reaction
Reference Electrodes	Hydrogen reference electrodes (e.g., HydroFlex) compliant with IUPAC standards [27]	Precise electrochemical measurements in electrolyzer development
Test Cell Systems	Modular electrochemical test cells (e.g., FlexCell) [27]	Customizable half-cell experiments for electrolyzer optimization
Tmprss6-IN-1	Tmprss6-IN-1|TMPRSS6 Inhibitor|For Research Use
Antiparasitic agent-10	Antiparasitic agent-10, MF:C13H17N3O4S3, MW:375.5 g/mol	Chemical Reagent

The integration of advanced Sabatier reactors and electrolysis systems represents a critical pathway toward achieving closed carbon loops in advanced life support systems. Current technologies, particularly membrane Sabatier systems and optimized electrolyzer configurations, demonstrate substantial improvements in COâ‚‚ conversion efficiency, system reliability, and microgravity compatibility. The experimental methodologies and research tools outlined in this guide provide the foundation for continued innovation in this field.

For future long-duration missions to the Moon and Mars, these P/C systems will play an indispensable role in reducing resupply mass and enabling sustainable human presence in space. The transformative potential of these technologies extends beyond space applications, offering insights into carbon recycling and sustainable fuel production on Earth. As research continues to refine these systems, particularly in addressing challenges related to intermittent power supplies and further system miniaturization, the vision of fully closed-loop life support systems becomes increasingly attainable.

Bioregenerative Life Support Systems (BLSS) are advanced artificial ecosystems designed to sustain human life during long-duration space missions by regenerating essential resources through biological processes. These systems strategically integrate plant and microbial compartments to close metabolic loops, with a primary focus on carbon loop closure. This whitepaper provides an in-depth technical analysis of BLSS architectures, detailing the synergistic relationships between photosynthetic organisms and microbial communities that enable the conversion of waste carbon dioxide and organic wastes into oxygen, food, and recycled water. We present quantitative performance data, detailed experimental methodologies for key processes, and essential research tools required for advancing this critical field of study, framing all content within the overarching objective of achieving sustainable carbon cycling for advanced life support systems.

The imperative for crewed deep-space exploration to the Moon and Mars necessitates the development of regenerative systems that can maintain human life without continuous resupply from Earth. Bioregenerative Life Support Systems (BLSS) represent the most promising solution to this challenge, as they minimize the need for external supplies by in situ regeneration of oxygen, water, and food, while simultaneously recycling waste [28]. These systems are engineered manifestations of ecological principles, structured around three core biological compartments: producers (plants, microalgae), consumers (astronauts), and decomposers (microorganisms) [29] [28].

Central to BLSS functionality is the effective closure of carbon loops, a process wherein carbon atoms are continuously cycled between different chemical states and biological entities. Carbon enters the system primarily as COâ‚‚ from crew respiration and is fixed into biomass by photosynthetic producers. This biomass then serves as food for consumers, with metabolic wastes subsequently broken down by microbial decomposers, ultimately regenerating COâ‚‚ and other carbon compounds to restart the cycle [29]. The efficacy of this carbon cycling determines the system's degree of closure and operational sustainability. This whitepaper examines the technical integration of plant and microbial systems as the cornerstone for achieving robust carbon loop closure in advanced life support systems.

System Architecture and Carbon Flow

A fully integrated BLSS creates a web of metabolic interactions where the waste products from one compartment become the resources for another. Understanding these interconnections is fundamental to system design and control.

Integrated System Compartments and Carbon Pathways

The following diagram illustrates the core architectural components and primary carbon flow pathways in a conceptual BLSS designed for carbon loop closure.

Figure 1: System Architecture and Primary Carbon Flow in a BLSS. The diagram depicts the three core biological compartments (Producer, Consumer, Decomposer) and the continuous cycling of carbon (highlighted in dark gray). The microbial compartment is critical for closing the loop by converting solid and liquid wastes into forms usable by plants.

Functional Roles of System Components

• Plant Compartment (Producers): Higher plants and microalgae serve dual purposes. Through photosynthesis, they fix inorganic COâ‚‚ into organic biomass (food) and release oxygen for crew respiration [29] [30]. They also contribute to water purification through transpiration. For long-duration missions, staple crops (e.g., wheat, potato) are essential for providing calories, while leafy greens and fruits offer nutritional variety and phytonutrients [29].

• Microbial Compartment (Decomposers/Biotransformers): Microorganisms are the engine of nutrient recycling. They perform critical functions such as the anaerobic digestion of solid human waste and inedible biomass to produce volatile fatty acids (VFAs), COâ‚‚, and other precursors, rather than methane, for downstream processes [7]. Nitrifying bacteria convert ammonia to nitrate for plant fertilization, while other strains can be engineered to produce high-value compounds like polyhydroxyalkanoate (PHA) bioplastics [7].

• Crew (Consumers): The human element drives system demand, consuming oxygen, water, and food, while producing the waste streams (COâ‚‚, urine, feces) that fuel the regenerative processes. The metabolic rates of the crew are used to size the required photosynthetic and waste processing capacities [28].

Quantitative System Performance

The viability of a BLSS depends on the efficient performance of its biological subsystems. The following tables summarize key quantitative data from recent research on plant and microbial components.

Table 1: Performance Metrics of Plant Compartment Candidates

Plant Species	Cultivation Type	Key Quantitative Yield	Primary Function	Mission Relevance
Proso Millet (Panicum miliaceum L.)	Staple Crop (Phytotron)	Yield: 0.31 kg/m²; Weight of 1000 seeds: 8.61 g [31]	Carbohydrate & Protein Source	Long-duration, planetary outposts
Lettuce, Kale	Leafy Greens	Fast-growing, high nutritive value [29]	Vitamin & Phytonutrient Source	Short-duration, dietary supplement
Tomato, Peppers	Fruit-bearing Vegetables	~100-day growth cycle [29]	Food Variety & Nutrition	Medium/Long-duration missions
Wheat, Potato	Staple Crops	High carbohydrate yield [29]	Caloric Base of Diet	Long-duration, planetary outposts

Table 2: Performance Metrics of Microbial and Hybrid Systems

System / Process	Scale / Conditions	Performance Metric & Result	Reference
Anaerobic Digestion (AD ASTRA)	Laboratory-scale bioreactors	Conversion of human waste to Volatile Fatty Acids (VFAs) and COâ‚‚; suppression of methane production [7]	[7]
Hybrid PBR-Photocatalysis-MFC	60L Cylindrical PBR, 0.8% v/v COâ‚‚	COâ‚‚ sequestration rate increased from 12% to 22% with integrated photocatalytic framework; simultaneous electricity generation [32]	[32]
Chlorella vulgaris PBR	Various	Typical microalgae carbon sequestration efficiency: 4% to 7% (vs. 1-4% for green plants) [32]	[32]

Experimental Protocols for Core Processes

Robust, reproducible experimental methods are essential for characterizing and optimizing BLSS components. Below are detailed protocols for two critical processes: testing plant resilience and operating a hybrid carbon sequestration system.

Protocol: Hypergravity Stress Testing on Plant Germination

Objective: To assess the resilience of candidate crop seeds to hypergravity stress, simulating forces during launch, and to develop predictive models for biomass accumulation [31].

Workflow:

Figure 2: Workflow for Hypergravity Stress Testing on Plants. This protocol tests seed resilience to launch-like forces and collects data for predictive yield modeling.

Materials and Steps:

• Seed Preparation: Randomly select and size-calibrate seeds (e.g., Proso millet). Treat with a fungicide solution (e.g., 25 g/L fludioxonil) for sterilization, then rinse thoroughly with distilled water [31].
• Hypergravity Exposure: Place the prepared seeds in 10 mL centrifuge tubes filled with water. Expose them to hypergravity using a centrifuge (e.g., MPW-310) for a standard duration of 3 hours. Test a range of gravity levels, typically including 800 g, 1200 g, 2000 g, 3000 g, with a control group (1 g) that is soaked but not centrifuged [31].
• Cultivation: Sow the treated seeds in a standardized substrate (e.g., peat mixed with perlite and slow-release fertilizer) within a controlled phytotron environment. Maintain constant conditions: 24-hour LED lighting (50 W/m²), temperature of 24–28°C, and relative humidity of 30–50% [31].
• Data Collection:
  • Early Growth: On the 10th and 20th days after sowing, destructively sample seedlings to measure fresh shoot mass and length.
  • Final Yield: At full grain ripeness (Zadoks scale), measure yield components: total above-ground biomass, number of productive inflorescences, grain weight per plant, and weight of 1000 seeds [31].
• Analysis: Use statistical analysis (e.g., ANOVA) to determine the effects of hypergravity. Develop predictive regression equations for biomass accumulation and final yield based on early growth metrics and linear plant traits [31].

Protocol: Operation of a Hybrid Photobioreactor for Enhanced Carbon Sequestration

Objective: To integrate a photocatalytic porous framework with a microalgae photobioreactor (PBR) and microbial fuel cell (MFC) for enhanced COâ‚‚ sequestration under low-concentration conditions (e.g., <1% v/v) typical of confined spaces, with simultaneous electricity generation [32].

Materials and Steps:

• Photocatalytic Framework Preparation: Synthesize a composite photocatalyst (e.g., g-C₃Nâ‚„/TiOâ‚‚). Coat this catalyst onto a porous framework (e.g., polyurethane foam) to prevent direct contact with and damage to microalgae cells while allowing for catalytic activity [32].
• System Integration: Assemble a cylindrical photobioreactor (PBR) containing a culture of Chlorella vulgaris or similar microalgae. Integrate the prepared photocatalytic frameworks into the PBR, either by immersing them directly in the algae suspension or connecting them in an external loop. Connect the effluent from the PBR to a continuous-flow microbial fuel cell (MFC) [32].
• Operational Parameters: Feed the system with a gas mixture containing ~0.8% v/v COâ‚‚ to simulate a spacecraft atmosphere. Provide visible light illumination to drive both photosynthesis and photocatalysis. Maintain the PBR with standard nutrient media, potentially derived from processed wastewater [32].
• Monitoring and Data Collection:
  • COâ‚‚ Sequestration: Monitor the COâ‚‚ concentration at the PBR inlet and outlet to calculate the sequestration rate.
  • Water Quality: Measure the chemical oxygen demand (COD) in the PBR to quantify organic compounds produced by photocatalysis.
  • Electrical Output: Record the voltage and current generated by the MFC, which consumes organics from the PBR effluent [32].
• Analysis: Compare the COâ‚‚ sequestration efficiency of the baseline PBR versus the hybrid PBR-photocatalysis system. Correlate the COD increase with the electrical power output from the MFC [32].

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 3: Key Research Reagents and Materials for BLSS Experimentation

Item Name	Specification / Example	Primary Function in BLSS Research
g-C₃N₄/TiO₂ Photocatalyst	Coated on porous framework (e.g., polyurethane foam)	Enhances CO₂ conversion to organic acids under visible light in photobioreactors, increasing overall sequestration rate without harming microbes [32].
Volatile Fatty Acid (VFA) Production Bioreactor	Anaerobic Digestion (AD) system with controlled microbial communities	Converts solid human waste into useful VFAs (e.g., acetate) and COâ‚‚ for downstream biomanufacturing, instead of methane [7].
Molecular Biology Kits	Sequencing-based techniques (e.g., 16S rRNA sequencing)	Monitors and characterizes the microbial communities responsible for waste processing and nutrient cycling, ensuring system stability [7].
Controlled Environment Chambers (Phytotrons)	LED lighting, precise temperature & humidity control	Simulates growth environments for plants and algae, allowing for study of growth parameters and yield optimization under standardized conditions [31].
Microbial Fuel Cell (MFC)	Continuous-flow design, anode/cathode chambers	Generates electricity from organic waste in PBR effluent, while also helping to manage dissolved oxygen levels to promote algal growth [32].
Hypergravity Simulator	Centrifuge (e.g., MPW-310) with programmable g-levels	Tests the resilience of biological components (seeds, microbes) to launch and potential spaceflight conditions [31].
Antiproliferative agent-17	Antiproliferative agent-17, MF:C26H28N2OS, MW:416.6 g/mol	Chemical Reagent
Irak4-IN-24	IRAK4-IN-24|Potent IRAK4 Inhibitor|For Research

The successful integration of plant and microbial systems presents a viable path toward achieving the closed carbon loops essential for humanity's future in space. While significant progress has been made in ground-based demonstrators like China's Lunar Palace 1 [28] and the AD ASTRA consortium [7], the transition from Earth-based simulation to operational space-based BLSS remains a critical challenge. Future research must prioritize experimentation in the space environment itself, particularly on the Lunar surface, to study the integrated effects of microgravity, radiation, and confined pressures on these complex ecosystems. Closing the carbon loop is not merely a technical objective; it is the fundamental prerequisite for enduring, sustainable, and self-sufficient human exploration beyond Earth.

In-Situ Resource Utilization (ISRU) represents a paradigm shift in space exploration, encompassing the "collection, processing, storing, and use of materials found or manufactured on other astronomical objects that replace materials that would otherwise be brought from Earth" [33]. For advanced life support systems, ISRU is critical for achieving sustainable exploration by minimizing Earth resupply requirements and enabling long-duration human presence beyond low Earth orbit. The concept of closing the carbon loop is particularly crucial, as it involves recycling carbon dioxide (COâ‚‚) exhaled by crew members back into breathable oxygen and other useful compounds, thereby creating a regenerative ecosystem that dramatically reduces consumable mass [23].

The fundamental challenge ISRU addresses is the prohibitive cost and mass of launching all necessary resources from Earth's deep gravity well. As missions extend to lunar and Martian surfaces, the resupply model becomes increasingly unsustainable. NASA's current life support systems aboard the International Space Station (ISS) demonstrate partial closure, recovering approximately 90% of water through advanced processing systems [34]. However, oxygen recovery remains limited, with the ISS's Sabatier technology recovering only about 50% of oxygen from carbon dioxide due to methane venting [35]. ISRU technologies aim to bridge this gap by leveraging local resources, with NASA investing in regolith-based volatiles processing, Mars atmosphere utilization, and in-space manufacturing to achieve higher degrees of system closure [36].

Carbon Dioxide Processing Technologies

Closing the carbon loop begins with efficient carbon dioxide capture and processing. Several competing technologies have been developed with varying degrees of maturity and efficiency for converting waste COâ‚‚ into valuable oxygen and other resources.

Current Flight-Proven Systems

The Sabatier process represents the current state-of-the-art in flown CO₂ processing technology. This system reacts carbon dioxide with hydrogen (typically from water electrolysis) to produce methane and water: CO₂ + 4H₂ → CH₄ + 2H₂O [33]. The water is then electrolyzed to produce oxygen for crew consumption and hydrogen that is recycled back into the Sabatier reactor. On the ISS, this system has demonstrated operational capability but suffers from a fundamental limitation: approximately half of the carbon dioxide cannot be processed due to hydrogen limitations, resulting in only 50% oxygen recovery efficiency [35]. The methane byproduct is typically vented overboard, representing a loss of both carbon and hydrogen atoms.

The Advanced Closed Loop System (ACLS), developed by ESA, represents a significant improvement over basic Sabatier technology. This integrated system uses an amine scrubber to concentrate COâ‚‚ from cabin air, then processes 50% of it through a Sabatier reactor to produce water and methane. The water is electrolyzed to produce oxygen and hydrogen, with the latter fed back to the Sabatier reactor [37]. The remaining 50% of COâ‚‚ is reduced to carbon and water using a separate Carbon Dioxide Reprocessing Assembly, achieving higher overall oxygen recovery rates. The ACLS can regenerate enough oxygen for three astronauts and reduces water resupply needs by 400 liters annually [37].

Next-Generation Technologies Under Development

Bosch technology represents a promising alternative for achieving near-complete carbon loop closure. Unlike the Sabatier process, the Bosch reaction catalytically reduces carbon dioxide with hydrogen to produce solid carbon and water: CO₂ + 2H₂ → 2H₂O + C [35]. This process has a theoretical maximum oxygen recovery of 100% and eliminates the methane venting issue. The primary technical challenges include managing the accumulation of solid carbon within the reactor system and maintaining catalyst efficiency.

Methane pyrolysis offers a complementary approach by addressing the hydrogen limitation of conventional Sabatier systems. This technology processes the methane produced by the Sabatier reactor, thermally decomposing it into hydrogen and solid carbon: CH₄ → C + 2H₂ [35]. The recovered hydrogen can then be used to process additional carbon dioxide, potentially increasing oxygen recovery to nearly 100%. NASA's Next Generation Life Support (NGLS) project is currently developing both Bosch and methane pyrolysis technologies to advance beyond current capabilities [35].

Table 1: Comparison of Carbon Dioxide Processing Technologies

Technology	Chemical Process	Oxygen Recovery Efficiency	Byproducts	Technology Readiness
Sabatier	CO₂ + 4H₂ → CH₄ + 2H₂O	≤50% [35]	Methane (vented)	Flight-proven (ISS) [37]
Advanced Closed Loop System	Combines Sabatier with COâ‚‚ reduction	>50% [37]	Methane (partially vented), water	Flight-demonstrated (ISS) [37]
Bosch	CO₂ + 2H₂ → 2H₂O + C	≤100% (theoretical) [35]	Solid carbon	Technology development [35]
Methane Pyrolysis	CH₄ → C + 2H₂	≤100% (combined with Sabatier) [35]	Solid carbon	Technology development [35]

Water Recovery and Management

Water represents the largest mass requirement for crewed space missions, with each astronaut consuming 2.27-3.63 kg of potable water and 1.36-9 kg of hygiene water daily [23]. Closing the water loop is therefore essential for sustainable exploration, with ISRU providing both recycling technologies and local sourcing options.

Current Water Recovery Systems

The ISS Water Recovery System demonstrates state-of-the-art in water recycling, recovering approximately 90% of onboard water through a sophisticated multi-stage process [34]. The system consists of two primary subsystems: the Urine Processor Assembly and the Water Processor Assembly. The Urine Processor uses vacuum distillation with centrifugal phase separation to compensate for microgravity, initially designed to recover 85% of water content from urine but currently operating at 70% efficiency due to precipitation issues with calcium sulfate in the free-fall environment [37]. The distilled urine is then combined with other wastewaters and fed to the Water Processor Assembly, which employs multi-filtration beds and a high-temperature catalytic reactor to remove organic contaminants and microorganisms [34]. Electrical conductivity sensors continuously monitor water purity, with unacceptable water recycled through the processor [34].

ISRU Water Sourcing Technologies

Beyond recycling, ISRU aims to extract water from local extraterrestrial sources. On the Moon, water ice deposits in permanently shadowed polar regions represent a valuable resource, with the Lunar Reconnaissance Orbiter detecting signals indicative of water ice buried under lunar regolith [36]. The upcoming Volatiles Investigating Polar Exploration Rover mission will characterize the concentration and distribution of these deposits at the lunar South Pole [36].

For Mars, potential water sources include subsurface ice deposits, hydrated minerals, and atmospheric moisture. Orbital observations have revealed that ice makes up at least half of an underground layer covering a large Martian region midway between the equator and north pole, with a total water volume comparable to Lake Superior [36]. Hydrated sulfate deposits offer another potential resource, containing water molecules bound within their crystalline structure that can be released through heating [38].

Table 2: Water Requirements and Recovery Rates for Crewed Missions

Water Type	Crewmember Daily Requirement	ISS Recovery Rate	Recovery Technology
Potable Water	2.27-3.63 kg [23]	~90% [34]	Water Processor Assembly: multi-filtration beds + catalytic reactor [34]
Hygiene Water	1.36-9 kg [23]	~90% [34]	Combined processing with other wastewaters
Urine	N/A (waste)	70% (current operational) [37]	Urine Processor Assembly: vacuum distillation + centrifugation [37]

Food Production and Bioregenerative Systems

While physical/chemical systems can recycle water and oxygen, bioregenerative life support represents the ultimate frontier in closing the carbon loop through biological processes of photosynthesis and transpiration.

Current Capabilities and Research

Current space missions rely entirely on pre-packaged food transported from Earth, representing the largest expected non-propulsion consumable mass for long-duration missions [35]. NASA's NGLS project includes limited investigation of pick-and-eat food production systems using crop plants that can contribute to atmosphere revitalization and water recycling through their natural biological processes [35]. These systems would not only provide fresh food but also assist in closing the carbon loop by fixing carbon dioxide into biomass while producing oxygen.

Research into bioregenerative life support systems demonstrates their potential for reducing logistics requirements from Earth to Mars, with several regions on Mars identified as having large exploitable resource potential for supporting such systems [38]. Hydrated mineral deposits on Mars could potentially serve as fertilizer for food production, further enhancing the synergy between ISRU and bioregenerative systems [38].

Experimental Protocols for Bioregenerative Systems

Plant Growth Optimization Methodology:

• Selection of candidate species based on nutritional value, growth efficiency, and environmental tolerance
• Development of growth substrates using simulated regolith amended with recycled nutrients
• Implementation of controlled environment agriculture with optimized light spectra, COâ‚‚ enrichment, and temperature regulation
• Integration with life support systems to manage atmospheric gases and water transpiration cycles
• Harvest and waste processing to return unused biomass to the nutrient cycle

Implementation Challenges and Future Directions

Technical and Operational Hurdles

The path to implementing robust ISRU systems faces several significant challenges. Dust mitigation remains a critical issue, particularly in the context of planetary dust interfering with mechanical systems and potentially contaminating processed resources [35]. The low-temperature environments at potential resource sites, such as the lunar poles, present engineering challenges for equipment operation and resource extraction [36]. Additionally, uncertainties regarding the precise form, concentration, and distribution of resources like water ice necessitate further characterization missions before full-scale implementation [36].

From a systems perspective, the integration of multiple ISRU processes into a cohesive, reliable system represents a substantial engineering challenge. The ISS experience with repeated failures and maintenance of the Elektron oxygen generation system highlights the reliability requirements for long-duration missions where emergency returns are not feasible [37].

ISRU Technology Demonstration Missions

Several upcoming missions will demonstrate critical ISRU technologies in relevant environments. The Mars Oxygen ISRU Experiment aboard the Perseverance rover will demonstrate oxygen production from the Martian atmosphere, providing essential data for future scaled-up systems [36]. VIPER, the Volatiles Investigating Polar Exploration Rover, will characterize water ice deposits at the lunar South Pole, informing future extraction technologies [36]. Additionally, NASA is developing lunar CubeSat missions aimed at better locating and quantifying available water ice resources [36].

The Researcher's Toolkit: Essential ISRU Technologies

Table 3: Key Research Technologies for ISRU and Carbon Loop Closure

Technology/Component	Function	Current Status	Research Applications
Sabatier Reactor	Converts COâ‚‚ and Hâ‚‚ to CHâ‚„ and Hâ‚‚O [33]	Flight-proven (ISS) [37]	COâ‚‚ reduction, oxygen recovery studies
Solid Oxide Electrolysis	Splits COâ‚‚ to CO and Oâ‚‚ [33]	Technology development	Mars atmosphere utilization, propellant production
Molecular Sieve	Concentrates COâ‚‚ from cabin air	Flight-proven (ISS) [37]	Atmospheric revitalization, COâ‚‚ collection
Bosch Reactor	Converts COâ‚‚ to solid carbon and Hâ‚‚O [35]	Technology development [35]	High-efficiency oxygen recovery, carbon loop closure
Vapor Compression Distillation	Urine and wastewater processing	Flight-demonstrated [37]	Water recovery efficiency studies
Amine Scrubber	COâ‚‚ concentration from cabin air	Flight-demonstrated (ACLS) [37]	Carbon capture and concentration
Methane Pyrolysis Assembly	Decomposes CHâ‚„ to C and Hâ‚‚ [35]	Technology development [35]	Hydrogen recovery, complete carbon loop closure
Regolith Volatiles Extractor	Heats lunar/martian soil to extract water	Prototype development [36]	In-situ water extraction, resource characterization
Anti-inflammatory agent 33	Anti-inflammatory agent 33, MF:C22H15N3O5S, MW:433.4 g/mol	Chemical Reagent	Bench Chemicals
T-F-Q-A-Y-P-L-R-E-A	T-F-Q-A-Y-P-L-R-E-A, MF:C55H82N14O16, MW:1195.3 g/mol	Chemical Reagent	Bench Chemicals

The integration of ISRU technologies with advanced life support systems represents a critical pathway toward sustainable human exploration beyond low Earth orbit. By closing the carbon loop through a combination of physical/chemical processing and bioregenerative systems, future missions can dramatically reduce dependence on Earth resupply while enabling longer duration missions. Current technologies aboard the ISS demonstrate the feasibility of partial closure, with water recovery rates of 90% and developing oxygen recovery systems. The ongoing development of Bosch reactors, methane pyrolysis, and bioregenerative systems promises progressively higher degrees of closure, moving toward the ultimate goal of self-sufficient habitats on the Moon and Mars. As ISRU technologies mature, they will transform space exploration from an expeditionary model to a sustainable presence throughout the solar system.

A Digital Twin is a dynamic, virtual representation of a physical object or system that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning, and reasoning to enable decision-making [39]. Unlike traditional static simulations, digital twins maintain continuous, bidirectional connections with their physical counterparts through data streams, allowing them to accurately reflect current conditions and evolve alongside the physical asset [39]. This technology has evolved from its origins in NASA's Apollo program, where physical replicas of spacecraft systems were used to troubleshoot missions in real-time, into a cornerstone of modern industrial and environmental strategy [39]. The global digital twin market, valued at $23.4 billion in 2024, is projected to reach $219.6 billion by 2033, reflecting a compound annual growth rate (CAGR) of 25.08% and underscoring its transformative potential across sectors [39].

Within the specific context of advanced life support systems and carbon loop closure research, digital twins offer a paradigm shift from static environmental models to dynamic, predictive systems. They enable researchers to simulate complex carbon capture, utilization, and storage processes in near real-time, optimize resource allocation with unprecedented precision, and conduct risk-free experimentation on system interdependencies. This capability is critical for designing robust, circular systems where carbon outputs from one process become inputs for another, ultimately supporting the development of net-zero environmental control systems.

Digital Twins vs. Traditional Simulation: Fundamental Distinctions

While both digital twins and traditional simulations create virtual models of real-world entities, their operational capabilities and applications differ substantially. Understanding these distinctions is crucial for selecting the appropriate technology for carbon loop research and system design.

Static vs. Dynamic Modeling

Traditional simulations have functioned as fundamental engineering tools for decades, but they typically depend on historical data and predefined scenarios to examine system behavior under controlled conditions [39]. These static models utilize fixed data, mathematical formulas, and scenario-based inputs, requiring substantial manual updates and recalibration to reflect changing system conditions [39]. Once established, traditional simulations remain largely unchanged unless manually modified by designers.

In contrast, digital twins represent a marked shift toward dynamic modeling capabilities. They are "living" entities that continuously evolve through ongoing data exchange with their physical counterparts [39]. This fundamental difference transforms organizational approaches to virtual modeling—shifting from theoretical possibilities to actual, specific conditions that can be monitored and analyzed in real-time. Where a simulation replicates what could happen to a product in a hypothetical scenario, a digital twin replicates what is happening to an actual specific product in the real world at any given moment [39].

The Real-Time Feedback Loop

The most significant advantage digital twins offer over traditional simulations lies in their continuous feedback loop with physical assets. This bidirectional communication creates what McKinsey describes as "a risk-free digital laboratory for testing designs and options" [39]. Digital twins maintain this connection through several synchronized mechanisms:

• Sensor Integration: Internet of Things (IoT) devices continuously transmit operational data from physical assets to their digital counterparts [39]
• Synchronization: Updates occur automatically as conditions change, maintaining an accurate virtual representation [39]
• Bidirectional Data Flow: Changes in either the physical or digital environment can influence the other, creating adaptive feedback loops [39]

This continuous information exchange enables digital twins to perform what traditional simulations cannot—immediate adaptation to changing conditions without manual recalibration. For carbon loop research, this means that environmental parameters, resource flows, and system performance can be monitored and adjusted dynamically rather than through periodic analysis.

Table 1: Comparative Analysis: Digital Twins vs. Traditional Simulation

Characteristic	Digital Twin	Traditional Simulation
Data Source	Real-time sensor data, continuous updates	Historical data, predefined scenarios
Update Mechanism	Automatic, continuous synchronization	Manual recalibration required
Temporal Dimension	Operates in current time, predictive capability	Typically static or time-sliced analysis
Interaction Capability	Bidirectional data flow, feedback loops	Typically unidirectional, no direct physical connection
Primary Application	Operational monitoring, predictive maintenance, dynamic optimization	Design validation, theoretical scenario planning

Digital Twin Implementation Framework for System Design

Implementing an effective digital twin requires a structured approach that integrates technological components, data infrastructure, and analytical capabilities. This framework is particularly relevant for designing advanced life support systems with integrated carbon management.

Core Technological Infrastructure

The functional capability of a digital twin depends on a layered technological stack that enables real-time synchronization between physical and virtual entities:

• IoT Infrastructure and Sensor Integration: IoT devices and sensors establish vital connections between physical assets and their digital counterparts [39]. These networks capture diverse parameters including temperature, pressure, vibration, position, operational status, and environmental conditions [39]. In carbon loop systems, this might include CO~2~ sensors, biomass tracking systems, and energy monitoring devices.

• Edge Computing Architecture: Edge computing addresses critical concerns in IoT-based digital twin implementations, including network partitioning challenges in unreliable connections, latency reduction for time-sensitive applications, and data privacy protection for sensitive information [39]. This is particularly important in distributed environmental control systems where reliable connectivity cannot be assumed.

• Data Processing Infrastructure: Real-time processing demands require specialized architecture. Research indicates that "real-time digital twins require scalable software architecture so they can analyze streaming data on the fly and provide faster responses" [39]. This infrastructure must handle the multi-dimensional data streams characteristic of complex biological and chemical processes in life support systems.

Digital Twin Taxonomy and Application Scope

Digital twins can be categorized based on their scope and application focus, each requiring different implementation approaches and providing distinct value propositions for system design:

• Component Twins: Track individual parts (like a specific sensor or filter). Interfaces must support high-resolution inspection, possibly with AR overlays for pinpoint diagnostics [40].

• Asset Twins: Monitor entire assets (like a carbon sequestration unit). Interfaces must show performance, failure predictions, and maintenance schedules in intuitive ways [40].

• System or Unit Twins: Simulate how multiple assets work together (e.g., an integrated carbon processing system). User experience here requires workflow-based visualization, drag-and-drop simulations, and layered views [40].

• Process Twins: Mirror full processes like carbon loop operations or energy grid behavior. These require real-time dashboards, alerts, and decision-support features for large-scale orchestration [40].

For carbon loop closure research, this taxonomy enables researchers to implement digital twins at appropriate scales—from molecular-level processes to facility-wide carbon management systems—while maintaining interoperability between hierarchical levels.

Implementation Methodology: The Azure Digital Twins Framework

Microsoft's Azure Digital Twins provides a representative framework for implementing digital twins in research environments. The platform requires several core components:

• Azure Digital Twins Instance: Serves as the core digital twin environment, requiring data owner or reader access permissions for implementation [41]
• Azure Storage Account: Provides repository functionality for twin data and model assets [41]
• Private Storage Container: Secures sensitive research data while enabling access to authorized systems [41]
• 3D Scenes Studio: Enables immersive visualization of digital twins through 3D model integration (GLTF/GLB formats) [41]

The implementation workflow involves initial environment configuration, 3D scene creation with linked digital models, element definition connecting virtual components to physical assets, and behavior implementation that defines scenario responses [41]. For carbon loop research, this framework can be adapted to model carbon flows, sequestration processes, and resource utilization patterns with high fidelity.

Digital Twin-Driven Carbon Management: Experimental Protocols and Case Studies

The integration of digital twins into carbon management systems enables unprecedented capabilities for dynamic life cycle assessment and emissions optimization. Recent research demonstrates both methodological approaches and quantifiable outcomes in this domain.

The Building Life-cycle Digital Twin (BLDT) Framework

The Building Life-cycle Digital Twin (BLDT) framework represents a novel methodology that combines real-time data from Internet of Things (IoT) devices, machine learning algorithms, and semantic interoperability to deliver dynamic, predictive, and high-resolution Life Cycle Assessment (LCA) for construction and infrastructure systems [42]. This framework, developed within the Computational Urban Sustainability Platform (CUSP), addresses the limitations of traditional static LCA by enabling continuous, data-driven sustainability assessments [42].

The BLDT implementation follows a structured experimental protocol:

• IoT Sensor Deployment: Installation of distributed sensors to monitor energy consumption, material flows, and environmental conditions in real-time
• Data Integration Pipeline: Establishment of data streams from physical systems to the digital twin environment through edge computing infrastructure
• Predictive Model Calibration: Machine learning algorithms are trained on historical data to forecast system behavior under varying conditions
• Dynamic LCA Implementation: Continuous environmental impact assessment based on actual operational data rather than theoretical projections
• Optimization Feedback Loop: The digital twin identifies efficiency opportunities and tests potential interventions virtually before implementation

In a validation case study conducted at the Port of Grimsby, the BLDT framework facilitated a 25% reduction in energy consumption while enhancing operational efficiency, achieving an annual carbon reduction of 618.5 tCOâ‚‚ [42]. These results demonstrate the model's potential to support decarbonisation strategies, regulatory compliance, and long-term planning in complex operational environments.

Central Air Conditioning Ecosystem Optimization

A separate study focused on Digital Twin-driven low-carbon service design in Central Air Conditioning (CAC) ecosystems developed a novel framework for systematic low-carbon service design and modularization [43]. The methodology incorporated:

• Three-dimensional Energy Scenario Intelligence model for comprehensive demand analysis [43]
• Quantitative analysis of co-intelligence relationships between system components [43]
• Improved Girvan-Newman algorithm for service module generation and optimization [43]
• Interval Type-2 Fuzzy TOPSIS to handle high-level uncertainties in decision-making [43]

When implemented in an intelligent office building, this digital twin framework achieved a 74.29% integrated energy saving rate along with significant carbon reductions [43]. The study explicitly elucidated DT's pivotal role in enabling predictive and systemic low-carbon capabilities, providing a replicable methodology for environmental control systems in advanced life support applications.

Table 2: Digital Twin Performance in Carbon Management Applications

Application Domain	Implementation Framework	Key Performance Indicators	Results
Port Infrastructure	Building Life-cycle Digital Twin (BLDT)	Energy consumption, Operational efficiency, Carbon emissions	25% energy reduction, 618.5 tCOâ‚‚ annual reduction [42]
Central Air Conditioning Systems	DT-driven Low-carbon Service Design	Integrated energy saving rate, Carbon reduction	74.29% energy saving rate [43]
Manufacturing Systems	AI-powered Predictive Digital Twins	Equipment efficiency, Maintenance costs, Production downtime	70% of industrial enterprises projected to adopt digital twins by 2025 [40]

The Researcher's Toolkit: Essential Components for Digital Twin Implementation

Implementing digital twins for carbon loop research requires specific technological components and analytical tools. The following table summarizes key research reagent solutions and their functions in constructing digital twin environments for environmental control systems.

Table 3: Essential Research Components for Digital Twin Implementation

Component Category	Specific Tools/Technologies	Research Function	Application Notes
IoT Sensor Platforms	Temperature, CO~2~, humidity sensors; Vibration and acoustic monitors; Position and acceleration trackers [39]	Real-time data acquisition from physical environments	Critical for establishing data streams between physical systems and digital models; Requires calibration for research-grade accuracy
Data Processing Infrastructure	Edge computing nodes; Cloud data platforms (Azure Digital Twins, AWS IoT TwinMaker); Time-series databases [39] [41]	Handling real-time data streams and synchronization	Enables scalable architecture for analyzing streaming data on the fly and providing faster responses
Modeling & Simulation Environments	3D Scenes Studio; ANSYS Twin Builder; Siemens Process Simulate [41] [40]	Creating virtual representations of physical systems	Supports GLTF/GLB formats for 3D model integration; Enables immersive visualization and interaction
AI/ML Analytical Tools	Predictive maintenance algorithms; Pattern recognition systems; Optimization engines [39] [43]	Enabling predictive capabilities and pattern detection	Allows digital twins to forecast system behavior and identify optimization opportunities
Integration Frameworks	Azure Digital Twins; IBM Watson IoT Platform; PTC ThingWorx [39] [41]	Connecting physical and digital environments	Provides authorization systems, data mapping, and synchronization mechanisms
Influenza antiviral conjugate-1	Influenza Antiviral Conjugate-1|RUO|Fc-Conjugate		Bench Chemicals

Digital twin technology represents a transformative approach to system design and optimization, with particular relevance for carbon loop closure in advanced life support systems. By enabling real-time synchronization between physical and virtual environments, digital twins facilitate dynamic life cycle assessment, predictive optimization, and risk-free scenario testing that exceeds the capabilities of traditional simulation approaches.

The experimental protocols and case studies presented demonstrate that digital twin implementation can drive substantial efficiency improvements and carbon reductions—up to 74.29% energy savings in climate control applications [43]. For researchers focused on advanced life support systems, these technologies offer unprecedented capabilities for modeling complex carbon flows, testing circular economy strategies, and optimizing resource utilization in near real-time.

Future research directions should focus on enhancing the integration of digital twins with emerging artificial intelligence capabilities, developing standardized data models for environmental systems, and creating more intuitive user interfaces that make these powerful tools accessible to domain experts without specialized computational backgrounds. As the technology continues to mature, digital twins will play an increasingly central role in achieving the precise, adaptive control required for sustainable, closed-loop environmental systems.

The quest for long-duration human spaceflight beyond Low Earth Orbit (LEO) is fundamentally constrained by the mass, volume, and cost of launching consumables from Earth. Life support systems, which provide astronauts with breathable air and potable water, have historically been partially open-loop, treating these vital resources as expendable. Closing the carbon loop—the process of recovering oxygen from the carbon dioxide (CO₂) exhaled by crew members—is a critical technological challenge for sustainable exploration [35].

Framed within the broader thesis of carbon loop closure in advanced life support systems research, this case study provides a performance analysis of the European Space Agency's (ESA) Advanced Closed Loop System (ACLS). Demonstrated on the International Space Station (ISS), the ACLS represents a significant advancement in closing the atmosphere revitalization loop. This paper details its core technology, operational protocols, and quantitative performance, serving as a foundational reference for researchers and engineers developing the next generation of life support systems for the Moon, Mars, and beyond.

The ACLS is an integrated technology rack designed to recycle carbon dioxide into breathable oxygen, thereby reducing the constant resupply mass of water from Earth [1]. Its primary objective is to demonstrate the performance and reliability of a more closed-loop life support system in the microgravity environment of the ISS.

Installed in the US Destiny module, the ACLS is built as an International Standard Payload Rack, measuring approximately 2 meters high, 1 meter wide, and 85.9 cm deep [1]. The system was designed to operate for at least one year over a two-year demonstration period and is capable of producing enough oxygen for three astronauts [1].

Core Subsystem Functions

The ACLS integrates three major assemblies to achieve its core function, each playing a distinct role in the carbon-oxygen cycle, as illustrated in the diagram below.

Diagram 1: Logical workflow of the ACLS's core process of oxygen recovery.

• Carbon Dioxide Concentration Assembly (CCA): This sub-system is responsible for removing COâ‚‚ from the cabin air. It uses a unique amine developed by ESA for human spaceflight, which is coated onto small beads [1]. As cabin air passes through these beads, the amine selectively traps COâ‚‚ molecules, thereby concentrating them and maintaining acceptable COâ‚‚ levels in the cabin atmosphere. The concentrated COâ‚‚ is then released using steam for further processing.

• Carbon Dioxide Reprocessing Assembly (CRA): Also known as the Sabatier reactor, this is the core recycling unit. It facilitates a chemical reaction between the concentrated COâ‚‚ from the CCA and hydrogen (Hâ‚‚) over a catalyst. This reaction, known as the Sabatier process, produces water (Hâ‚‚O) and methane (CHâ‚„) as byproducts [1]. The water is condensed and separated for use in the oxygen generator, while the methane is vented into space [1].

• Oxygen Generation Assembly (OGA): This assembly uses an electrolyser to split the water recovered from the Sabatier reactor, as well as water supplied from other sources, into its constituent elements: breathable oxygen (Oâ‚‚) and hydrogen (Hâ‚‚) [1]. The oxygen is returned to the cabin for the crew, and the hydrogen is fed back to the Sabatier reactor to fuel the COâ‚‚ reduction process.

Performance Analysis and Quantitative Data

The performance of the ACLS is measured by its ability to close the oxygen loop and reduce resupply demands. The system marks a substantial improvement over previous open-loop systems but does not achieve full closure due to the loss of hydrogen in the form of methane.

Key Performance Metrics

Table 1: Key quantitative performance data for the ESA ACLS [1].

Performance Parameter	Value	Context and Significance
Oxygen Production Capacity	Enough for 3 astronauts	Supports a standard ISS crew complement, demonstrating operational relevance.
COâ‚‚ Recovery Rate	50%	Half of the recovered COâ‚‚ is processed; the other half is vented with methane, limiting maximum oxygen recovery.
Water Savings	~400 liters per year	Reduces the mass of water that needs to be launched from Earth annually, a key cost-saving metric.
Water Production	~50% of OGA needs	The Sabatier reactor provides approximately half of the water required by the Oxygen Generation Assembly.

The 50% recovery rate of COâ‚‚ is a direct consequence of the stoichiometry of the Sabatier reaction and the decision to vent the methane byproduct. As noted in NASA's Next Generation Life Support (NGLS) project, the ISS's current Sabatier technology results in the loss of about half the carbon dioxide, which equates to a loss of oxygen [35]. This inherent limitation of the Sabatier process defines the current performance ceiling of the ACLS and highlights a key area for further research.

Experimental and Operational Protocols

The deployment of the ACLS on the ISS can itself be viewed as a long-duration, in-situ experiment to validate the technology's reliability and performance under real microgravity conditions. The operational methodology follows a structured verification and demonstration plan.

Methodology for System Demonstration

• Installation and Commissioning: The ACLS rack was launched to the ISS aboard Japan's HTV-7 vehicle and installed in the US Destiny module [1]. Initial activities involved mechanical installation, power and data connectivity checks, and leak checks of fluid systems to ensure structural and functional integrity.

• Operational Demonstration Phase: The core objective was to operate the system for at least one year within a two-year period [1]. This extended duration test was designed to:

  • Assess Reliability: Monitor system performance for degradation or failure over time.
  • Verify Performance Metrics: Collect quantitative data on COâ‚‚ capture efficiency, water production rate, oxygen generation rate, and power consumption to validate pre-launch models.
  • Evaluate Microgravity Effects: Confirm the proper functioning of fluid separation, gas-liquid interactions, and thermal control in a sustained microgravity environment.

• Integration with ISS Life Support: While a demonstrator, the ACLS was also integrated to function as part of the station's active life support system, contributing oxygen to the cabin atmosphere [1]. This provided invaluable data on the interaction between the ACLS and other station systems.

The workflow for this operational demonstration is summarized in the diagram below.

Diagram 2: High-level experimental workflow for the ACLS technology demonstration on the ISS.

The Researcher's Toolkit: Essential Materials and Reagents

The functionality of the ACLS depends on several key materials and reagents that enable its core chemical and physical processes. The table below details these critical components, which are essential for research and development in the field of closed-loop life support.

Table 2: Key research reagents and materials used in the ESA ACLS [1] [35].

Material/Reagent	Function in the System	Research Significance
ESA-developed Amine	COâ‚‚ sorbent, coated onto beads for the CCA. Selectively captures COâ‚‚ from cabin air.	A specialized material enabling efficient gas separation in microgravity. Represents a key area for research into capacity, longevity, and regenerability.
Sabatier Catalyst	Facilitates the reaction CO₂ + 4H₂ → CH₄ + 2H₂O at operational temperatures and pressures.	The heart of the recycling process. Research focuses on improving efficiency, lifetime, and resistance to poisoning.
Water Electrolysis Cell	The core component of the OGA, electrically splitting water (Hâ‚‚O) into oxygen (Oâ‚‚) and hydrogen (Hâ‚‚).	Critical for oxygen production. Research aims to improve efficiency, reduce mass, and increase reliability.
Hydrogen (Hâ‚‚)	A reactant fed into the Sabatier reactor to reduce COâ‚‚. A product of the OGA.	Its management is the key to closing the oxygen loop. Loss of Hâ‚‚ (as in methane venting) limits system closure.

Future Research Directions and Next-Generation Systems

While the ACLS is a major step forward, achieving the near-complete carbon loop closure required for deep space missions necessitates technologies beyond the current Sabatier-based architecture. The venting of methane represents a net loss of hydrogen, which in turn limits the maximum oxygen recovery to around 50% [1] [35]. Research is therefore focused on alternative processes that can fully recover the oxygen from COâ‚‚.

Two primary technological pathways are under investigation, as highlighted by NASA's NGLS project:

• Bosch Reaction: This technology catalytically reduces carbon dioxide with hydrogen gas, resulting in the production of water and solid carbon [35]. The Bosch reaction has a theoretical maximum oxygen recovery of 100%, as it does not produce a gaseous waste product containing hydrogen. The main engineering challenge is the management and removal of the solid carbon, which can foul the reactor.
• Methane Pyrolysis: This approach is proposed as a complement to the Sabatier reactor. The methane produced by the Sabatier process is pyrolyzed (thermally decomposed in the absence of oxygen) to recover hydrogen and solid carbon. This recovered hydrogen can then be used to process the remaining COâ‚‚, potentially increasing total oxygen recovery to 100% [35].

The logical relationship between current technology and these future research paths is illustrated below.

Diagram 3: Research pathways beyond the current Sabatier process to achieve higher oxygen recovery.

The ESA Advanced Closed Loop System represents a pivotal achievement in the roadmap toward sustainable human space exploration. Its successful demonstration on the ISS proves the viability of integrated COâ‚‚-to-oxygen recycling in a operational space habitat. By halving the water resupply needs for oxygen production, the ACLS provides a tangible solution to a critical mass constraint.

However, its performance is bounded by the fundamental chemistry of the Sabatier process. The case of the ACLS powerfully frames the central thesis challenge in advanced life support research: achieving full carbon loop closure. The path forward, as charted by projects like NASA's NGLS, lies in developing and maturing technologies such as the Bosch reaction and Methane Pyrolysis. These systems aim to overcome the 50% recovery barrier, moving from a partially closed to a fully regenerative life support system capable of sustaining human life indefinitely on the journey to the Moon, Mars, and beyond.

Overcoming Challenges and Enhancing System Efficiency

The pursuit of sustained human presence in space and the implementation of closed-loop carbon management systems on Earth hinge on a common, critical challenge: ensuring the reliability of complex systems during long-duration operation. This whitepaper provides a technical guide for identifying and analyzing failure points within these systems. By integrating principles from reliability engineering with current research on carbon loop closure in advanced life support, this document outlines quantitative failure metrics, details experimental protocols for system characterization, and proposes robust mitigation strategies. The frameworks presented are designed to equip researchers and engineers with the methodologies necessary to build resilient, self-sustaining ecosystems for future missions and climate initiatives.

Closed-loop systems, whether for advanced life support or terrestrial carbon management, are designed to operate as self-sustaining ecosystems. Their core function is the continuous recycling of resources, such as carbon, waste, and water, through biological and physicochemical processes. The reliability of these integrated processes is the cornerstone of system viability during long-duration missions. A single point of failure in a subsystem can cascade, leading to a catastrophic breakdown of the entire life-support system [7].

Understanding the potential failure mechanisms is the first step toward building reliable systems. These mechanisms can be broadly categorized as follows [44]:

• Mechanical Failures: These include fracture, fatigue, and corrosion of physical components such as pipes, valves, and reactors, often instigated by repeated stress cycles or abrasive materials.
• Material Failures: This involves the degradation or incompatibility of materials used in construction, such as the corrosion of iron pipes or the breakdown of plastics exposed to specific chemicals or UV light.
• Process-Related Failures: These are failures due to deviations in operational protocols, such as incorrect calibration of sensors, improper sterilization, or fluctuations in key process parameters like temperature and pressure.
• Human-Induced Failures: Errors in operation, maintenance, or design, often stemming from insufficient training, fatigue, or oversight, can introduce unforeseen failure points.
• Biological Process Failures: In systems reliant on microbial communities, such as the anaerobic digestion consortium in the AD ASTRA project, imbalances in microbial populations can halt the conversion of waste into volatile fatty acids (VFAs), disrupting the entire resource recovery chain [7].

Quantitative Framework for Reliability Assessment

A data-driven approach is essential for predicting and preventing failures. Reliability engineering provides key metrics to quantify system performance and component lifespan.

Key Reliability Metrics

Table 1: Key Quantitative Metrics for Reliability Assessment

Metric	Definition	Calculation	Application in Closed-Loop Systems
Mean Time To Failure (MTTF)	The average time a non-repairable component or system functions until its first failure [45].	MTTF = Total Operating Time / Number of Failures	Predicting the lifespan of critical, non-repairable components like specific sensors or microbial bioreactors in a space habitat [45].
Mean Time Between Failures (MTBF)	The average time between consecutive failures of a repairable system [45].	MTBF = Total Operating Time / Number of Failures	Scheduling preventive maintenance for repairable subsystems like pumps or compressors in a COâ‚‚ processing unit [45].
Failure Rate	The frequency with which a component or system fails, often expressed as failures per unit of time.	Derived from MTTF/MTBF data and statistical models.	Identifying components with unacceptably high failure rates for re-engineering or redundancy planning.

For example, if 20 anaerobic digestor units accumulate 350,000 hours of total operation before all fail, the MTTF would be 17,500 hours per unit. This data is critical for planning mission durations and spare parts inventories [45].

System Dynamics Modeling

For complex, interdependent systems like a closed-loop carbon ecosystem, System Dynamics (SD) modeling provides a powerful quantitative framework. SD modeling uses causal loop diagrams and computer simulation to map the dynamic, non-linear relationships between subsystems. This allows researchers to simulate how a failure in one area (e.g., a drop in photosynthetic biomass production) propagates through the entire system (affecting oxygen production, food supply, and carbon sequestration), thereby identifying vulnerable feedback loops before they cause system-wide collapse [46].

Experimental Protocols for Failure Point Identification

Rigorous, ground-based experimentation is vital for characterizing potential failures. The following protocols provide methodologies for stress-testing systems and their components.

Protocol: Microbial Community Stability under Simulated Microgravity

This protocol is derived from the research objectives of the NASA-funded AD ASTRA consortium, which aims to develop a closed-loop biological system for space [7].

• 1. Objective: To understand how simulated low gravity affects the stability and metabolic output of microbial communities central to anaerobic digestion and phototrophic biosystems.
• 2. Materials and Reagents:
  • Anaerobic Chamber: To maintain an oxygen-free environment for the digestion process.
  • Bioreactors: For housing the microbial communities.
  • Centrifuge with Microgravity Simulation Capabilities: Such as a clinostat or random positioning machine.
  • Molecular Biology Kits: For DNA/RNA extraction and purification to monitor microbial populations.
  • Sequencing Reagents: For 16S rRNA amplicon sequencing to characterize community structure.
  • Analytical Chemistry Equipment: HPLC or GC-MS for quantifying metabolic products (VFAs, COâ‚‚, etc.).
• 3. Methodology:
  • Setup: Inoculate parallel bioreactors with a defined microbial consortium for anaerobic digestion. Maintain control reactors under normal gravity.
  • Stress Application: Expose test reactors to continuous simulated microgravity.
  • Monitoring: Periodically sample from all reactors.
    • Use molecular and sequencing-based techniques to monitor population dynamics and the expression of key functional genes (e.g., the mcrA gene for methanogenesis) [7].
    • Quantify the conversion of waste feedstock into VFAs (e.g., acetate) and COâ‚‚.
    • Monitor system parameters (pH, temperature, pressure) continuously.
• 4. Data Analysis: Compare the rates of VFA production, community structure stability, and gene expression profiles between control and test groups. A significant shift in the microbial balance or a drop in VFA production in the test group indicates a critical failure point induced by the operational environment.

Protocol: COâ‚‚ Processing Loop Integrity under Transient Conditions

This protocol is based on the development of a COâ‚‚ visualization loop experimental device for carbon transport systems [47].

• 1. Objective: To identify failure points in COâ‚‚ processing hardware, such as pipelines and seals, during transient events like pressure release and phase changes.
• 2. Materials and Reagents:
  • COâ‚‚ Visualization Loop Apparatus: A closed-loop pipeline system with high-precision temperature, pressure, and flow rate sensors [47].
  • High-Speed Camera: To capture phase transitions and flow regimes.
  • Data Acquisition System: Software for real-time monitoring and control of pump frequency, pressure, and temperature.
• 3. Methodology:
  • Steady-State Baseline: Establish stable supercritical or gaseous COâ‚‚ flow through the loop.
  • Induce Transient: Initiate a rapid depressurization event at a release valve to simulate a leak or controlled venting.
  • Data Capture: Record in-situ temperature and pressure at multiple axial nodes along the pipeline. Simultaneously, use high-speed imaging to visualize phase transitions (e.g., the formation of dry ice) and fluid behavior.
• 4. Data Analysis: Analyze the data for localized temperature drops due to the Joule-Thomson effect, which can embrittle materials and cause seal failure. Correlate pressure response curves with visual data to identify locations prone to hazardous phase transitions that could block flow or damage components [47].

The workflow for a comprehensive failure analysis, integrating both biological and physicochemical testing, is outlined below.

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 2: Key Research Reagent Solutions for Closed-Loop System Experimentation

Item	Function / Application
Anaerobic Chamber	Creates an oxygen-free environment essential for cultivating and experimenting with anaerobic microbial communities used in waste digestion [7].
Molecular Biology Kits (DNA/RNA Extraction)	Enable the monitoring of microbial community structure and functional gene expression (e.g., for methanogenesis) to diagnose biological failure points [7].
High-Precision COâ‚‚ Sensors	Critical for monitoring carbon flow and sequestration efficiency in both life support and terrestrial carbon management systems [3] [47].
Volatile Fatty Acid (VFA) Standards	Used with analytical instruments like HPLC to calibrate and quantify the output of anaerobic digestion processes, a key metric for system health [7].
Clinostat / Random Positioning Machine	Laboratory equipment used to simulate microgravity conditions for ground-based testing of biological and physical systems [7].
Polyhydroxyalkanoate (PHA) Assay Kits	Used to measure the production of biopolymers by engineered cyanobacteria, quantifying the success of a key biomanufacturing output in closed-loop systems [7].

Mitigation Strategies and Path Forward

Identifying failure points is futile without actionable strategies for mitigation. A multi-pronged approach is required:

• Proactive Maintenance and Design: Implement Failure Modes and Effects Analysis (FMEA) during the design phase to anticipate failures. Incorporate redundancy for critical components and schedule predictive maintenance based on MTTF data [44] [45].
• Systemic Monitoring and Control: Deploy continuous, real-time monitoring of key performance indicators, including microbial community balance via molecular tools and hardware integrity via distributed sensor networks. This allows for the early detection of deviations before they lead to failure [7] [47].
• Integrated Policy and Economic Frameworks: For terrestrial carbon loop systems, success depends on coupling technical solutions with supportive policies. This includes creating balanced carbon markets, providing carbon credit incentives, and developing utilization-driven byproducts to ensure economic viability alongside technical reliability [3] [46].

The path toward reliable long-duration operation for closed-loop systems lies in the interdisciplinary integration of reliability engineering, microbiology, chemical engineering, and systems analysis. By adopting the quantitative frameworks, experimental protocols, and mitigation strategies outlined in this whitepaper, researchers can systematically identify and address failure points, thereby accelerating the development of robust systems essential for humanity's future on Earth and in space.

Optimizing Mass and Energy Balances for Maximum Efficiency

In the context of advanced life support systems for space exploration, optimizing mass and energy balances is not merely an engineering exercise but a fundamental requirement for enabling long-duration human missions beyond Earth's orbit. These systems aim to create a tightly controlled, regenerative ecosystem where waste streams are converted into vital resources, thereby minimizing resupply needs from Earth. The European Space Agency's (ESA) Advanced Closed Loop System (ACLS), for instance, demonstrates this principle by recycling carbon dioxide from the cabin atmosphere into breathable oxygen, saving approximately 400 liters of water annually that would otherwise need launch and transport to the International Space Station [1]. Achieving maximum efficiency in such systems requires a meticulous approach to quantifying all mass and energy inputs, outputs, and internal flows, ensuring the system can operate sustainably with minimal losses.

Framed within the broader thesis of carbon loop closure, this guide details the principles and methodologies for optimizing these balances. It draws on current research from leading consortia, including the NASA-funded AD ASTRA project, which seeks to develop a closed-loop biological system for converting human waste into useful materials for in-space biomanufacturing [7], and the MELiSSA Consortium, which focuses on creating a closed ecosystem for air, water, and food recycling [8]. The following sections provide a technical guide for researchers, outlining fundamental principles, experimental protocols for data acquisition, system modeling, and advanced optimization strategies.

Fundamental Principles of Mass and Energy Closure

A closed-loop life support system is fundamentally a network of interconnected processes that recover and regenerate resources. Efficiency is measured by the degree of closure achieved for key element cycles (e.g., carbon, oxygen, hydrogen, nitrogen) and the overall energy required to maintain these cycles.

The Mass Balance Equation

The general mass balance for any system component or the entire system is defined as: Input + Generation = Output + Consumption + Accumulation

In a closed-loop system, the "Generation" and "Consumption" terms are often internal, representing chemical or biological conversions. The goal is to minimize "Output" (losses) and "Accumulation" (which can indicate inefficiency or system instability). For carbon loop closure, this means tracking carbon atoms from their source (e.g., COâ‚‚ in the cabin, waste products) through various conversion processes (e.g., Sabatier reaction, photosynthesis) to their final form (e.g., Oâ‚‚, food, methane vented to space) [48] [1].

The Energy Balance Equation

Concurrently, the energy balance must be considered: Energy Input = Energy Output + Energy Accumulation

Energy inputs can include electricity, light for plant growth, and heat for chemical reactors. Outputs include work, heat loss, and the energy content of vented gases. Optimizing the energy balance often involves heat integration between exothermic and endothermic processes and selecting highly efficient conversion technologies.

Table 1: Key Performance Indicators for Closed-Loop System Efficiency

KPI	Definition	Calculation Example	Target Value
Carbon Closure Rate	Percentage of carbon atoms recycled within the system.	(1 - (Carbon Vented / Carbon Input)) × 100%	>95% [49]
Mass Closure	Percentage of initial mass accounted for in products.	(Total Mass Recovered / Total Mass Input) × 100%	>90% [49]
Specific Energy Consumption	Energy required per unit of resource recovered.	Total Energy Input (kJ) / Mass of Product (kg Oâ‚‚)	Minimized
Cascade Efficiency	Utilization of a waste stream in multiple processes.	∑(Useful Output from each process / Total Waste Input)	Maximized

Experimental Protocols for System Characterization

Rigorous experimental data is the foundation of an accurate mass and energy balance. Inconsistent or incomplete product quantification can lead to significant carbon balance deficits, compromising reported yields and selectivities [49]. The following protocols outline methodologies for characterizing system inputs and outputs.

Protocol for Carbon Mass Closure in Vapor-Rich Systems

This protocol is critical for processes like plastic hydrocracking or waste gasification, which produce a large fraction of gaseous products that are challenging to capture completely [49].

• Reactor Setup: Conduct batch reactions in a sealed, pressurized reactor (e.g., a 25 mL Parr reactor) equipped with temperature and pressure monitoring, a stirring mechanism, and sampling ports [49].
• Vapor-Phase Product Capture: Upon reaction completion and system quenching, use a continuous inert gas sweep (e.g., helium) to transfer the entire headspace volume through a transfer line into one or more gas sampling bags (e.g., Supel-Inert Multi-Layer Foil bags). Using multiple bags or a large-volume bag (e.g., 2-3 L) prevents over-pressurization and ensures complete capture [49].
• Quantification with Internal Standard: Inject a known quantity of an external standard (e.g., 1-10 mL of pure propylene) into the gas sampling bag using a gastight syringe. Analyze the gas composition via Gas Chromatography with Flame Ionization Detection (GC-FID). The internal standard allows for the calculation of the absolute abundance of all hydrocarbon species in the vapor phase [49].
• Liquid and Solid Product Recovery: Solubilize the remaining reactor contents in a suitable solvent. Separate the supernatant from solid catalyst and unreacted material via centrifugation (e.g., 11,000 rpm for 10 minutes). Add a known mass of an external standard (e.g., 1,3,5-tritertbutyl benzene) to the liquid product for quantification via GC-FID. Dry and mass the residual solids to determine unreacted polymer and catalyst content [49].
• Carbon Balance Calculation: Quantify carbon in each phase (vapor, liquid, solid) and calculate the total carbon recovered. Compare this to the initial carbon input to determine the carbon mass closure percentage. This method has been shown to achieve closure rates of 96 ± 9.2% [49].

Protocol for Characterizing a Sabatier Reactor Loop

This protocol characterizes a key unit process for closing the carbon and oxygen loops, as used in ESA's ACLS [1].

• System Integration: The Sabatier reactor (Carbon Dioxide Reprocessing Assembly, CRA) must be integrated with a COâ‚‚ concentration unit (Carbon dioxide Concentration Assembly, CCA) and an electrolysis unit (Oxygen Generation Assembly, OGA) [1].
• Steady-State Operation: Operate the integrated system at a steady state, maintaining constant temperature, pressure, and flow rates for the input streams (concentrated COâ‚‚ and Hâ‚‚ from OGA).
• Input Stream Analysis: Precisely measure the volumetric or molar flow rates of the input COâ‚‚ and Hâ‚‚ streams using flow meters.
• Output Stream Analysis: Condense and measure the mass flow rate of the produced water. Analyze the composition and flow rate of the vented gas stream (containing methane and any excess COâ‚‚) using GC or a similar method.
• Mass and Energy Balance Calculation:
  • Mass Balance: Verify that the input carbon and hydrogen atoms equal the output atoms in water, methane, and vented COâ‚‚. The ACLS, for example, achieves approximately 50% COâ‚‚ recovery, with the remaining carbon vented as methane [1].
  • Energy Balance: Measure the electrical energy input to the OGA and the thermal energy required to maintain the Sabatier reactor's temperature. Calculate the overall energy efficiency of the oxygen regeneration process.

Modeling and Optimization Strategies

With reliable experimental data, researchers can build models to simulate, analyze, and optimize the entire system.

Closed-Loop Control Theory

Moving from open-loop (scenario-based) to closed-loop (feedback-based) control is a critical paradigm shift for managing complex systems like the carbon-climate system or a life support system [48]. In a closed-loop strategy, observations are continuously taken to adapt control actions, correcting for perturbations and model uncertainties [48]. This can be formalized as a network congestion control problem, where the goal is to allocate "emission" flows (e.g., of carbon) through different "paths" (e.g., Sabatier reactor, plant growth chamber) without over-saturating any sink capacity [48]. Key concepts include:

• Observability: The ability to infer the internal state of the system (e.g., carbon stocks in all compartments) from external measurements (e.g., COâ‚‚ levels in the cabin).
• Controllability: The ability to steer the system from any initial state to a desired state (e.g., stable Oâ‚‚ partial pressure) in a finite time using appropriate manipulated variables (e.g., power to the OGA).

Workflow for System Integration and Analysis

A systematic workflow is essential for integrating individual unit processes into a coherent and optimized whole.

• Unit Process Characterization: As detailed in Section 3, conduct experiments to define the input-output relationships and key parameters (e.g., conversion efficiency, reaction rate, energy demand) for each subsystem (e.g., COâ‚‚ concentrator, Sabatier reactor, anaerobic digester, plant growth chamber) [7] [1].
• System-Wide Modeling: Develop a mechanistic model that connects all unit processes via their mass and energy flows. This model should be based on the physical connection network and mass-energy balance equations [8]. Tools like digital twins can be used to create a virtual replica of the life support system for simulation and analysis [8].
• Multi-Criteria Evaluation: Evaluate the integrated system model using multiple criteria, including mass-energy balance, crew time for operation and maintenance, safety, reliability, and sustainability [8]. This step identifies bottlenecks and inefficiencies in the integrated system.
• Control Strategy Implementation: Design and implement a control strategy to ensure the performance and reliability of critical functions like oxygen, water, and food supply [8]. This may involve the use of artificial intelligence to complement knowledge models and manage complex, non-linear interactions [8].
• Validation and Refinement: Continuously validate the model predictions and control system performance against data from ground demonstrators or the flight system. Use deviations to refine the models and improve the control laws, closing the feedback loop [8].

The Researcher's Toolkit for Carbon Loop Experiments

Table 2: Essential Research Reagent Solutions and Materials

Item	Function/Application	Specific Example
H-ZSM-5 Zeolite Catalyst	A solid acid catalyst for hydrocracking and reforming reactions; crucial for breaking down complex waste hydrocarbons into simpler molecules.	Commercial H-ZSM-5 (Si/Al = 11.5, e.g., Zeolyst CBV2314) used for polyethylene hydrocracking [49].
Calcium Oxide (CaO) Sorbent	Used in calcium looping (CaL) processes for COâ‚‚ capture via reversible carbonation-calcination reactions; a promising post-combustion capture technology.	Natural limestone, a low-cost and abundant CaO-based sorbent, used in dual fluidized bed reactors [50].
Amine-Functionalized Beads	For selective COâ‚‚ capture from cabin air by trapping COâ‚‚ molecules as it passes through; a key step in concentration before reduction or recycling.	Unique amine beads developed by ESA for the Carbon dioxide Concentration Assembly (CCA) in the Advanced Closed Loop System [1].
Anaerobic Microbial Consortia	Mixed cultures of microorganisms that thrive without oxygen and can digest organic waste, producing volatile fatty acids (VFAs) and other useful precursors.	Cultures optimized for anaerobic digestion of human waste, converting it to organic acids for downstream biomanufacturing [7].
Cyanobacterial Strains	Phototrophic organisms that can use COâ‚‚ and processed wastewater to produce oxygen, protein-rich biomass, and biopolymers, closing multiple loops.	Engineered strains used in coculture with heterotrophs to consume AD-processed wastewater and produce oxygen and food [7].
External Analytical Standards	Critical for accurate quantification of reaction products in complex mixtures using gas chromatography (GC).	Propylene (for vapor phase) and 1,3,5-tritertbutyl benzene (for liquid phase) used as external standards for GC-FID calibration [49].

Optimizing mass and energy balances is the cornerstone of developing feasible advanced life support systems for long-duration space missions. By employing rigorous experimental protocols, such as the continuous sweep-gas method for carbon closure, and adopting advanced modeling and closed-loop control strategies, researchers can achieve the high levels of efficiency required for sustainability. The integration of biological systems (like anaerobic digestion and phototrophic cultures) with physical-chemical systems (like the Sabatier process and calcium looping) presents a powerful pathway toward closing the carbon loop. As research conducted by consortia like MELiSSA and AD ASTRA continues to mature, these principles and protocols will enable the transition from ground-based demonstrators to the reliable, autonomous life support systems that will sustain humanity on its journey to the Moon, Mars, and beyond.

Addressing Trace Gas Contaminant Buildup and Control

In advanced life support systems, the closure of the carbon loop is paramount for creating sustainable and resilient environments. Within this framework, the effective management of trace gas contaminants represents a critical challenge. These gases, often present in minute concentrations, can accumulate to hazardous levels in closed or confined systems, posing risks to system integrity and occupant health. This whitepaper examines the mechanisms of trace gas contaminant buildup and outlines advanced control methodologies, positioning this management as an essential component of broader carbon cycle control strategies. The insights provided are particularly relevant for applications in spacecraft, sealed laboratories, and other controlled ecological life support systems (CELSS).

Trace Gas Contaminants in Closed Systems

Nature and Origins of Trace Gases

Trace gas contaminants encompass a variety of gaseous species present in low concentrations but with potentially significant impacts. In the context of life support, the Earth's atmosphere provides a key reference point; its composition is approximately 78% nitrogen, 21% oxygen, 0.9% argon, and 0.1% other gases, including carbon dioxide (COâ‚‚), methane (CHâ‚„), nitrous oxide (Nâ‚‚O), and others [51]. These trace gases can exhibit a powerful influence on the environment through phenomena like the greenhouse effect, wherein they absorb and re-emit infrared radiation, thereby trapping heat [51].

In closed artificial systems, trace contaminants originate from multiple sources:

• Equipment off-gassing: Materials, electronics, and plastics can release volatile organic compounds (VOCs).
• Human metabolism: Respiration and metabolic processes produce COâ‚‚, methane, and other gases.
• System processes: In advanced life support, waste processing and resource recovery technologies can generate trace contaminants as by-products [52].

The Carbon Loop and Contaminant Interactions

The management of trace contaminants is intrinsically linked to the closure of the carbon loop. A functioning carbon cycle involves processes that capture, recycle, and reuse carbon, minimizing losses and avoiding the accumulation of waste products like COâ‚‚ and other carbon-based trace gases. Contaminant buildup disrupts this cycle by introducing chemical species that can inhibit key processes such as photosynthesis or chemical reduction, thereby compromising the entire system's sustainability [3]. Effective control of these contaminants is therefore not merely a clean-up operation but a fundamental aspect of maintaining the delicate balance of a closed-loop carbon cycle.

Monitoring and Detection Technologies

Accurate detection and quantification are the foundation of effective trace gas control. Recent advancements have focused on developing sensitive, real-time monitoring solutions.

Sensor Technologies and Performance

Table 1: Performance Metrics of Low-Cost Electrochemical Gas Sensors (EGSs) for Trace Gas Monitoring

Target Gas	Calibration Method	Pearson Correlation (R)	Slope vs. Reference	Mean Bias (ppbv)	Root Mean Square Error	Application Context
Carbon Monoxide (CO)	Manufacturer Parameters	0.82	1.12	Not Significant	290 ppbv	Urban air quality, Arctic winter boundary layer [53]
Nitric Oxide (NO)	Artificial Neural Network	> 0.95	0.93 - 1.04	3 ± 12	N/S	Vertical profiling in Arctic boundary layer [53]
Nitrogen Dioxide (NO₂)	Artificial Neural Network	> 0.95	0.93 - 1.04	1 ± 3	N/S	Vertical profiling in Arctic boundary layer [53]
Ozone (O₃)	Artificial Neural Network	> 0.95	0.93 - 1.04	0 ± 4	N/S	Vertical profiling in Arctic boundary layer [53]
Ethanol	In-situ cross-calibration	N/S	N/S	N/S	N/S	Indoor air movement and contaminant transport [54]

N/S: Not Specified in the source material.

Electrochemical gas sensors (EGSs) have proven highly effective for mapping the distribution of trace gases. Their performance, however, is highly dependent on robust calibration procedures due to sensitivities to environmental factors like temperature and relative humidity [53]. As illustrated in Table 1, machine learning techniques, particularly artificial neural networks, have successfully calibrated sensors for NO, NO₂, and O₃, achieving high correlation with reference analyzers [53].

Another critical monitoring approach uses tracer gases to understand system dynamics. For example, ethanol has been used as a non-toxic tracer to study real-time air movement and mixing in rooms, using a network of fast-response metal oxide sensors [54]. This method can quantify how quickly contaminants disperse, which is vital for designing effective control systems. Similarly, carbonyl sulfide (OCS) is being validated as a tracer molecule for quantifying carbon uptake by plants during photosynthesis, as it is taken up by plants but not respired, helping to disentangle the gross fluxes of photosynthesis and respiration in the carbon cycle [55].

Experimental Protocol: Tracer Gas Method for Air Movement Analysis

This protocol, adapted from a novel method for investigating indoor air mixing, provides a methodology to assess contaminant transport [54].

• Tracer Release: Vaporized ethanol is released in controlled pulses (e.g., 20 seconds) at a source location within the environment under test.
• Sensor Grid Deployment: A network of low-cost, fast-response (2s) metal oxide sensors is deployed throughout the space. The grid should be three-dimensional, covering:
  • Upper room (e.g., 0.3 m from the ceiling).
  • Occupied zone (e.g., 1.1 - 1.4 m height).
  • Near-floor level (e.g., 0.1 - 0.4 m from the floor).
• In-situ Cross-Calibration: Sensors are cross-calibrated on-site against a reference to ensure quantitative measurement of relative concentrations.
• Data Acquisition and Analysis: Tracer concentration is measured at high frequency across all sensor nodes. The data is analyzed to determine metrics such as mixing time throughout the room and transport rates between specific zones.

Control and Removal Strategies

Once detected, trace gas contaminants must be efficiently removed to maintain a safe and operationally stable environment. Strategies can be broadly categorized as adsorption-based or process-integrated.

Advanced Sorbent Materials

Conventional activated carbon has been a staple for gas adsorption, but new materials offer superior performance. Metal-impregnated single-walled carbon nanotubes (SWCNTs) represent a significant advancement. Their effectiveness stems from:

• High Surface Area: Provides a vast area for contaminant adsorption.
• Controlled Pore Size: Allows for selective uptake of specific gaseous species based on molecular size.
• Functionalizable Structure: The ordered chemical structure enables functionalization and serves as an excellent catalyst support for the targeted removal of toxic contaminants [52]. These nanotubes are particularly promising for gas clean-up systems in life support applications where minimizing expendable mass is critical [52].

Integrated Carbon Management and Contaminant Control

The most sustainable strategies integrate contaminant control directly into the broader carbon management loop. This aligns with the concept of closed-loop carbon management [3].

Electrolytic Seawater Mineralization (ESM) is one such process. While its primary goal is carbon dioxide removal, it operates as a closed system that inherently controls gaseous streams. The protocol involves:

• Electrolysis: An electric current is passed through seawater, splitting it into an alkaline stream and an acidic stream.
• Carbonation: Atmospheric COâ‚‚ is added to the alkaline stream, where it dissolves or precipitates as carbonate minerals.
• Neutralization: The acidic stream is neutralized with alkaline rock.
• Recombination and Output: The streams are recombined and processed to an environmentally safe quality, resulting in dissolved inorganic carbon and solid carbonates that sequester carbon for over 10,000 years [56].

This process not only removes COâ‚‚ but also produces green hydrogen, a carbon-free fuel, demonstrating how waste gas management can be coupled with resource recovery [56].

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Key Reagents and Materials for Trace Gas Contaminant Research

Item	Function / Application	Key Characteristics
Ethanol (Vaporized)	Non-toxic tracer gas for studying air movement and contaminant transport [54].	Fast-evaporating, detectable by metal-oxide sensors, safe for occupied spaces.
Metal-impregnated Single-Walled Carbon Nanotubes (SWCNTs)	High-efficiency sorbent for toxic gas contaminant control [52].	High surface area, selective pore size, functionalizable surface, catalytic support.
Carbonyl Sulfide (OCS)	Tracer molecule for quantifying gross primary production (photosynthesis) in carbon cycle studies [55].	Taken up by plant stomata during photosynthesis but not released through respiration.
Low-Cost Electrochemical Gas Sensors (EGSs)	Detection and monitoring of specific trace gases (e.g., CO, O₃, NO, NO₂) in field experiments [53].	Affordable, portable, requires field calibration for reliable data.
Artificial Neural Network Calibration Models	Software tool to correct for environmental interference (humidity, temperature) on sensor data [53].	Improves accuracy and reliability of low-cost sensor outputs in complex field conditions.

System Integration and Workflow

Implementing a robust trace gas control system requires the integration of monitoring, analysis, and removal technologies into a cohesive workflow.

Trace Gas Control in Carbon Loop Workflow

The diagram above outlines the core logical workflow for managing trace gas contaminants within a closed carbon loop. The process begins with the introduction of contaminants, which are continuously tracked by a sensor network. Data from these sensors is processed, often using machine learning models, to create a real-time picture of contaminant distribution and movement. This information feeds into a decision point. If contaminant levels exceed a predefined threshold, targeted control systems, such as advanced sorbents or integrated processes like electrolytic seawater mineralization, are activated. Once controlled, the system supports the broader biological or chemical processes (e.g., photosynthesis, which can be monitored using OCS tracer) that close the carbon loop, resulting in a stable atmosphere and recovered resources.

The control of trace gas contaminants is a critical, enabling technology for achieving stable, long-duration advanced life support systems. It is not a standalone discipline but is deeply integrated with the overarching goal of closing the carbon loop. The advent of sophisticated monitoring tools, like machine-learning-calibrated sensor networks and tracer gases, provides the necessary data to understand complex system dynamics. Simultaneously, advanced materials like metal-impregnated carbon nanotubes and integrated processes like electrolytic seawater mineralization offer effective and sustainable removal pathways. Future research must continue to fuse these areas, developing smart systems that dynamically respond to trace gas threats, thereby ensuring the viability of closed-loop life support for Earth-based applications and the future of human space exploration.

Strategies for Improving Oxygen Recovery Beyond 50%

Achieving high rates of oxygen recovery is a critical objective in the development of advanced, closed-loop life support systems for long-duration space missions. Moving beyond the 50% recovery milestone represents a significant step toward reducing reliance on Earth-based resupply and enabling sustainable human presence in space. This whitepaper synthesizes current technological strategies and experimental approaches for enhancing oxygen recovery efficiency, with particular focus on integrating carbon dioxide reprocessing and oxygen generation subsystems. We present quantitative performance data, detailed methodological protocols, and visualizations of system workflows to guide researchers in optimizing these essential life support functions.

Current State of Oxygen Recovery Technology

Presently, the most advanced operational system demonstrating substantial oxygen recovery is the Advanced Closed Loop System (ACLS) developed by the European Space Agency (ESA) and installed on the International Space Station (ISS). The ACLS recycles carbon dioxide from the cabin atmosphere to produce oxygen, thereby reducing the need for water resupply from Earth by approximately 400 liters annually [1].

The system operates through a coordinated process: it first concentrates COâ‚‚ from the cabin air, then reacts it with hydrogen in a Sabatier reactor to form water and methane. This water is subsequently electrolyzed to generate breathable oxygen. A key limitation of current Sabatier-based technology is that only about 50% of the recovered COâ‚‚ is ultimately converted to oxygen; the methane byproduct is vented overboard, carrying away hydrogen atoms that could otherwise be used for further oxygen production [1]. Surpassing this 50% threshold requires innovative approaches to either utilize the methane or bypass the Sabatier process altogether.

Table 1: Performance Metrics of the Advanced Closed Loop System (ACLS)

System Parameter	Performance Metric	Technical Significance
COâ‚‚ Recovery Rate	50% of recovered COâ‚‚ is converted to Oâ‚‚	Limits maximum oxygen recovery efficiency due to methane venting
Water Savings	~400 liters/year	Reduces mass and launch frequency for resupply missions
Oxygen Production	Supports 3 astronauts	Demonstrates scalability for crewed missions
Technology Readiness	TRL 8 (Operational on ISS)	Validated in real microgravity environment

Technical Strategies for Enhanced Oxygen Recovery

Bosch Reaction System for Carbon Loop Closure

The Bosch reaction presents a promising alternative by converting carbon dioxide into solid carbon and water, thereby completely closing the carbon loop without venting methane. The produced water is then electrolyzed for oxygen recovery. Theoretical models indicate this system could achieve near 100% oxygen recovery from COâ‚‚.

The fundamental reaction is: CO₂ + 2H₂ → C (solid) + 2H₂O → O₂ + 2H₂ (through electrolysis)

However, practical implementation faces significant challenges, including catalyst deactivation due to carbon deposition and system mass/volume constraints. Current research focuses on developing continuous Bosch reactor designs with efficient carbon removal mechanisms and robust catalysts resistant to fouling.

Table 2: Comparison of Oxygen Recovery Technologies

Technology	Maximum Theoretical Oâ‚‚ Recovery	Current Demonstrated Efficiency	Key Challenges
Sabatier Process	50%	50% (ACLS on ISS)	Hydrogen loss via methane venting
Bosch Reaction	~100%	<50% (experimental)	Catalyst fouling; system complexity
Solid Oxide Electrolysis	~100%	Laboratory scale	High temperature operation; durability

Hybrid Systems and Biological Approaches

Integrating biological components with physicochemical systems offers a complementary pathway. Photobioreactors containing algae or cyanobacteria can simultaneously consume COâ‚‚ and produce Oâ‚‚ through photosynthesis. While biological systems typically have lower volumetric efficiency than compact physicochemical systems, they offer valuable multifunctionality, including water purification and potential food production.

Hybrid approaches might employ biological air revitalization for baseline COâ‚‚ removal/Oâ‚‚ production, with physicochemical systems handling peak loads. Genetic engineering of photosynthetic microorganisms aims to enhance their gas exchange rates and resilience to space environmental factors.

Experimental Protocols for System Validation

Bench-Scale Sabatier Reactor Optimization

Objective: To optimize catalyst formulation and operating parameters for maximizing COâ‚‚ conversion efficiency in a Sabatier reactor.

Materials & Equipment:

• Fixed-bed flow reactor (stainless steel, 1" diameter)
• Ruthenium on alumina catalyst (2% wt) or Nickel-based catalyst
• Mass Flow Controllers for COâ‚‚ and Hâ‚‚ (4:1 Hâ‚‚:COâ‚‚ ratio)
• Steam generator for in-situ catalyst regeneration
• Online Gas Chromatograph (GC-TCD) for product analysis
• Tube furnace with temperature control (200-400°C range)

Methodology:

• Catalyst Reduction: Pre-treat the catalyst under hydrogen flow (100 sccm) at 350°C for 4 hours to activate metallic sites.
• Reaction Phase: Introduce reactant gases at a stoichiometric Hâ‚‚:COâ‚‚ ratio of 4:1 with a total flow rate of 500 sccm.
• Parameter Optimization: Systematically vary reactor temperature (200-400°C) and pressure (1-10 atm) while monitoring COâ‚‚ conversion via GC analysis.
• Stability Testing: Conduct continuous operation for 100+ hours at optimal conditions to assess catalyst deactivation rates.
• Regeneration Protocol: Introduce steam (10% vol) in nitrogen carrier gas at 300°C for 2 hours to remove carbon deposits and restore activity.

Data Analysis: Calculate CO₂ conversion as: [1 - (CO₂ outlet/CO₂ inlet)] × 100%. Monitor methane selectivity to ensure >99% to minimize byproduct formation. The optimal condition typically achieves >80% CO₂ conversion at 300°C and elevated pressure.

Oxygen Restriction for Metabolic Load Reduction

Objective: To investigate the effects of controlled oxygen restriction on mammalian physiology as a complementary approach to reducing overall system oxygen demands.

Materials & Equipment:

• Environmental chambers with precise Oâ‚‚ control (capable of maintaining 11% Oâ‚‚)
• Mouse model (C57BL/6 or accelerated aging model)
• Oxygen sensors and data logging system
• Food and water intake monitoring equipment
• Behavioral assessment apparatus (e.g., rotarod for neurological function)

Methodology:

• Acclimatization: House mice at normal atmospheric oxygen (21%) for one week baseline period.
• Oxygen Restriction: At 4 weeks of age, transition experimental group to hypoxic environment (11% Oâ‚‚) over a 7-day gradual adaptation period.
• Monitoring: Record daily food and water intake to control for dietary effects on lifespan.
• Assessment: Conduct weekly neurological evaluations using standardized motor function tests.
• Lifespan Tracking: Monitor survival rates and document onset of age-related pathologies.

Data Analysis: Compare median lifespan between normoxic and hypoxic groups using Kaplan-Meier survival curves. The referenced study demonstrated a 50% extension in median lifespan (from 15.7 to 23.6 weeks) in a progeria mouse model under chronic continuous hypoxia [57].

System Integration and Data Management

Effective integration of oxygen recovery subsystems requires careful data management to ensure system reliability and performance optimization. The ODAM (Open Data for Access and Mining) framework provides a structured approach for managing experimental data tables associated with system performance monitoring [58]. This methodology emphasizes:

• Structural Metadata: Precisely defining how data tables are organized and linked
• Standardized Vocabularies: Using community-approved ontologies for system parameters
• Frictionless Data Packaging: Employing open standards for data dissemination

Adhering to FAIR Data Principles (Findable, Accessible, Interoperable, Reusable) ensures that performance data from oxygen recovery systems can be effectively shared and analyzed across the research community, accelerating technology development [58].

Research Reagent Solutions

Table 3: Essential Research Materials for Oxygen Recovery System Development

Reagent/Material	Specification	Research Function
Sabatier Catalyst	Ruthenium on Alumina (2-5% wt) or Nickel-based	Accelerates COâ‚‚ hydrogenation to CHâ‚„ and Hâ‚‚O
Solid Oxide Electrolysis Cell	Yttria-stabilized Zirconia electrolyte	High-temperature electrolysis of COâ‚‚ to CO and Oâ‚‚
Amine Sorbent Beads	Polyethylenimine on porous silica support	COâ‚‚ concentration from cabin air
Algal Cultures	Chlorella vulgaris or Spirulina	Biological COâ‚‚ sequestration and Oâ‚‚ production
Bosch Reaction Catalyst	Iron-based with trace potassium promoter	Facilitates COâ‚‚ reduction to solid carbon

System Workflow Visualization

Current ACLS Oxygen Recovery Workflow (50% Efficiency)

Enhanced Oxygen Recovery Workflow (Targeting >90% Efficiency)

Achieving oxygen recovery rates beyond the current 50% threshold requires integrated approaches that address the fundamental limitations of existing systems. The most promising near-term strategies include optimizing Sabatier reactor efficiency with advanced catalysts, while longer-term solutions will necessitate the development of Bosch reaction systems or hybrid physicochemical-biological approaches that effectively close the carbon loop.

Critical research priorities include:

• Developing continuous Bosch reactor systems with efficient carbon removal mechanisms
• Engineering robust solid oxide electrolysis cells for co-electrolysis of COâ‚‚ and Hâ‚‚O
• Optimizing photobioreactor designs for efficient biological gas exchange
• Implementing advanced process control and monitoring for integrated system operation

As space agencies prepare for missions beyond low Earth orbit, developing these enhanced oxygen recovery technologies will be essential for establishing sustainable life support systems that minimize resupply requirements and enable long-duration human exploration of space.

Spatial Optimization and Multi-Scenario Planning for Carbon Storage

The closure of the carbon loop is a fundamental principle in advanced life support systems, where the carbon dioxide (CO2) exhaled by crew members must be efficiently captured, recycled, and converted back into breathable oxygen and, ideally, biomass for food [1] [13]. In these confined systems, such as those developed by ESA and NASA, physico-chemical technologies like the Sabatier reactor are employed to convert CO2 into water and methane, while Biological Life Support Systems (BLSS) explore the use of plants and microorganisms to regenerate air and produce food [1] [13]. The overarching goal is to create a sustainable, closed-loop system that minimizes the need for resupply from Earth, a challenge that becomes exponentially critical for long-duration missions to the Moon or Mars [1].

This whitepaper posits that the spatial optimization and multi-scenario planning of terrestrial carbon storage represents an analogous, planet-scale endeavor to these life support systems. Just as engineers design closed-loop systems for spacecraft, land-use planners and policymakers must design landscapes that maximize the carbon sequestration functions of ecosystems to mitigate atmospheric CO2 levels and contribute to global carbon loop closure. Land use and land cover change (LUCC) is a dominant factor influencing the carbon storage capacity of terrestrial ecosystems [59] [60]. The conversion of natural landscapes like forests and grasslands to built-up land releases stored carbon and reduces future sequestration potential, thereby disrupting the natural carbon cycle [59] [61]. Consequently, simulating future land-use patterns under different policy scenarios and optimizing spatial configurations to protect and enhance carbon stocks is a critical strategy for supporting the "carbon loop closure" of our planetary life support system.

Core Methodologies for Assessment and Simulation

The Integrated PLUS-InVEST Modeling Framework

The most prevalent and robust methodological framework for this field combines the Patch-generating Land Use Simulation (PLUS) model and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model [59] [61] [60]. This integrated approach allows researchers to first project future land-use patterns and then quantify their impact on ecosystem services, specifically carbon storage.

• The PLUS Model: The PLUS model is used to simulate and project future land-use changes under various scenarios. Its core advantage lies in its ability to simulate the generation of fine-scale land-use patches based on an analysis of the driving factors behind historical land-use changes. The model uses a land expansion analysis strategy (LEAS) to extract the areas and driving factors of land-use expansion between two historical periods. It then employs a cellular automata (CA) model based on multi-class random patch seeds (CARS) to simulate the iterative evolution of patch-level changes under the influence of development probabilities and spatial constraints [60]. This makes it superior for modeling complex transitions in heterogeneous landscapes.

• The InVEST Model: The InVEST model's Carbon Storage module quantifies the carbon storage in a landscape based on land use/cover maps and carbon density data. The model calculates total carbon storage by summing four fundamental carbon pools for each land use type [59]:

  • Aboveground biomass: All living plant material above the soil.
  • Belowground biomass: The root systems of living plants.
  • Soil organic matter: Carbon stored in organic soils.
  • Dead organic matter: Includes litter and woody debris.

The total carbon storage (C~total~) is calculated using the formula: C~total~ = Σ (A~i~ * C~i~) where A~i~ is the area of land use type i, and C~i~ is its total carbon density, summed from the four carbon pools [59].

Carbon Density Data Compilation

The accuracy of the InVEST model is contingent on reliable carbon density data for different land use types. These values are typically derived from a combination of local field measurements, literature reviews, and established scientific datasets, such as "A Dataset of Carbon Density in Chinese Terrestrial Ecosystems" [59]. The table below provides an example of the carbon density data used in such assessments.

Table 1: Example Carbon Density Values for Different Land-Use Types (Mg C/ha)

Land Use Type	Aboveground Biomass	Belowground Biomass	Soil Organic Matter	Dead Organic Matter	Total Carbon Density
Forest Land	High	High	Medium-High	Medium	Very High
Cropland	Low-Medium	Low-Medium	Medium	Low	Medium
Grassland	Low	High	Medium-High	Low	Medium-High
Wetland	Variable	Variable	High	Variable	High
Water Body	Very Low	Very Low	Very Low	Very Low	Very Low
Built-up Land	Very Low	Very Low	Very Low	Very Low	Very Low

Note: Specific values are region-dependent and must be sourced from local studies or adjusted using empirical relationships with climate and soil data [59].

Designing and Implementing Multi-Scenario Simulations

Defining Plausible Future Scenarios

A core strength of the PLUS-InVEST framework is its ability to project land use and carbon storage outcomes under alternative futures. These scenarios are built by integrating different spatial policies and constraints into the PLUS model's simulation parameters. Commonly adopted scenarios include [59] [61] [60]:

• Natural Development (ND): Extends historical trends of land-use change into the future without policy intervention. This typically results in continued expansion of built-up land at the expense of cropland and ecological spaces, leading to significant carbon loss [60].
• Cropland Protection (CP): Prioritizes the protection of prime agricultural land from conversion to other uses. This scenario can indirectly protect carbon stocks by preventing the loss of cropland, which has a medium carbon density [59] [60].
• Ecological Protection (EP): Focuses on the conservation and restoration of forests, grasslands, and wetlands. This scenario typically results in the highest future carbon storage, as it directly protects and enhances high-carbon-density ecosystems [59] [61].
• Economic Development-Prioritized (ED): Accelerates the conversion of land for urban and industrial expansion. This scenario consistently projects the greatest decline in regional carbon storage due to the sealing of soil and loss of vegetation [61].
• Balanced Development (BD): Aims for a synergistic pathway that integrates food security, ecological conservation, and moderate economic growth. When well-designed, this scenario can mitigate carbon storage losses more effectively than the ND or ED scenarios [60].

Workflow for Multi-Scenario Simulation

The following diagram illustrates the integrated workflow for conducting a multi-scenario simulation and assessment of carbon storage.

Diagram Title: Workflow for Carbon Storage Simulation and Optimization

Spatial Optimization of Carbon Storage Patterns

Spatial optimization translates the results of scenario simulations into actionable land-use zoning plans. The goal is to identify which specific geographic areas should be prioritized for conservation, restoration, or controlled development to maximize regional carbon storage and achieve carbon neutrality goals [59].

One advanced method for this optimization is the use of a Bayesian Belief Network (BBN). A BBN is a probabilistic graphical model that represents variables and their conditional dependencies. In this context, it can integrate key variables influencing carbon storage—such as land use type, net primary productivity (NPP), soil type, and slope—to determine the optimal functional zone for each parcel of land [59].

A study on the Jiangsu section of the Yangtze River Basin successfully used a BBN to divide the territory into four optimal zones [59]:

• Ecological Protection Areas: Prioritize conservation of forests and wetlands for high carbon sequestration.
• Cropland Protection Areas: Maintain productive farmland to secure medium carbon storage and food supply.
• Water Conservation Areas: Protect water bodies and riparian zones for their carbon and hydrological functions.
• Economic Construction Areas: Designate zones for urban expansion where carbon loss is minimized and offset.

This approach provides a scientifically rigorous, spatially explicit basis for territorial spatial planning, enabling the integration of carbon storage objectives into land-use decision-making.

The Scientist's Toolkit: Essential Reagents and Materials

Table 2: Key Research Reagent Solutions and Computational Tools

Item / Tool Name	Type	Primary Function in Research
PLUS Model	Software / Algorithm	Simulates future land-use change patterns under different scenarios at a fine patch level.
InVEST Model	Software / Algorithm	Quantifies and maps ecosystem services, including carbon storage, based on land use/cover data.
Landsat/Sentinel Imagery	Geospatial Data	Provides multi-spectral satellite imagery for creating historical and current land use/cover maps.
Carbon Density Dataset	Reference Data	Provides benchmark values for carbon in aboveground, belowground, soil, and dead organic matter pools for various land cover types. Critical for running the InVEST model.
Digital Elevation Model (DEM)	Geospatial Data	Provides topographical driving factors (elevation, slope) for land use simulation models.
ArcGIS / QGIS	Software Platform	Used for data pre-processing, spatial analysis, cartography, and visualization of results.
Bayesian Belief Network (BBN)	Analytical Model / Algorithm	Supports spatial optimization decisions by handling complex variable relationships under uncertainty.

The methodologies of spatial optimization and multi-scenario planning for carbon storage provide a powerful, spatially explicit toolkit for managing terrestrial ecosystems as critical infrastructure for planetary life support. The integrated PLUS-InVEST framework, grounded in geospatial data and scenario analysis, allows researchers and policymakers to move from reactive assessments to proactive, evidence-based planning. By identifying pathways that balance development needs with the protection of vital carbon sinks, this approach directly supports the broader mission of closing the global carbon loop. Just as the Advanced Closed Loop System on the Space Station recycles CO2 to sustain astronauts [1], strategic land-use planning can help regulate Earth's atmosphere, ensuring the long-term sustainability of our planetary life support system.

Performance Validation, Comparative Analysis, and Future Benchmarks

Ground Analogue Testing and In-Situ Performance Metrics

In the pursuit of long-duration human space exploration, the development of robust Advanced Life Support Systems (ALSS) is paramount. These systems must reliably regenerate vital resources—most critically, oxygen—by closing the carbon loop, wherein the carbon dioxide (CO₂) exhaled by crew members is captured and converted back into breathable oxygen. The European Space Agency's (ESA) Advanced Closed Loop System (ACLS), a technology demonstrated on the International Space Station (ISS), serves as a leading benchmark for such systems [1]. This whitepaper details the essential frameworks of ground analogue testing and the critical in-situ performance metrics required to validate and mature these complex systems within the broader thesis of achieving full carbon loop closure.

Core Technologies for Carbon Loop Closure

At the heart of any ALSS is the integrated process of COâ‚‚ concentration, processing, and oxygen generation. The following diagram illustrates the logical workflow and component interdependence of a closed-loop carbon system, modeled after the ACLS.

Diagram 1: Closed-loop carbon system workflow.

The core technological process involves three major assemblies [1]:

• Carbon dioxide Concentration Assembly (CCA): Removes COâ‚‚ from the cabin atmosphere.
• Carbon dioxide Reprocessing Assembly (CRA): A Sabatier reactor that converts COâ‚‚ and hydrogen into water and methane.
• Oxygen Generation Assembly (OGA): An electrolyser that splits the product water into oxygen for the crew and hydrogen for recycling.

Key Performance Metrics for System Validation

Validating the performance and reliability of a closed-loop life support system requires tracking a set of quantitative in-situ metrics. These metrics, derived from system operations like the ACLS, provide a basis for comparing technologies and assessing progress toward closure goals [1].

Table 1: Essential In-Situ Performance Metrics for Carbon Loop Closure Systems

Metric Category	Specific Parameter	Target/Exemplar Value	Measurement Methodology
Oxygen Production	Oxygen Generation Rate	Sufficient for 3 astronauts [1]	Flow meters; mass spectrometry of output gas
CO2 Management	CO2 Capture Rate	50% recovery of exhaled COâ‚‚ [1]	In-line CO2 sensors at CCA inlet/outlet
Resource Efficiency	Water Savings	~400 liters/year vs. open-loop [1]	Precise mass tracking of water input/output
System Reliability	Continuous Operational Duration	≥1 year of demonstrated operation [1]	System uptime/logs under controlled conditions

Ground Analogue Testing: Protocols and Methodologies

Ground testing in simulated space environments is a critical precursor to orbital deployment. The following workflow outlines a standardized protocol for testing a closed-loop carbon system in a ground analogue facility.

Diagram 2: Ground analogue testing protocol.

Detailed Experimental Protocol

• Test Article Integration and Sealing: The system under test (e.g., an ACLS rack) is integrated into the analogue chamber's infrastructure. The entire volume is sealed and checked for leaks to ensure no uncontrolled exchange of gases with the external environment [1].
• Baseline Parameter Measurement: Prior to system activation, initial concentrations of Oâ‚‚, COâ‚‚, and other trace gases within the chamber atmosphere are measured using integrated gas chromatographs or mass spectrometers to establish a baseline [62].
• Controlled Metabolic Load Simulation: Human metabolic activity is simulated by introducing precise mixtures of COâ‚‚, water vapor, and other trace contaminants into the chamber atmosphere at rates equivalent to those of a target crew size (e.g., 3 astronauts) [1].
• In-Situ Performance Data Acquisition: The closed-loop system is activated. Sensors continuously log the parameters listed in Table 1 throughout the test duration. This includes monitoring the COâ‚‚ level at the CCA outlet, the water production rate from the CRA, and the Oâ‚‚ production rate from the OGA [1].
• System Stress Testing and Fault Introduction: To assess robustness, the system is subjected to operational extremes, such as variable metabolic loads, simulated component failures (e.g., a pump shutdown), or power fluctuations. The system's ability to maintain stable internal atmospheric conditions is recorded.
• Data Analysis and Closure Fraction Calculation: Post-test, data is analyzed to calculate the overall Carbon Loop Closure Fraction—the percentage of inhaled carbon that is successfully captured and converted back into oxygen. The ACLS, for example, demonstrated a 50% closure fraction by this methodology [1].

The Scientist's Toolkit: Research Reagent Solutions

The experimental development and validation of carbon management technologies rely on specific materials and reagents.

Table 2: Key Reagents and Materials for Carbon Loop Research

Reagent/Material	Function in Research Context	Technical Notes
Amine-based Sorbents	COâ‚‚ capture from air in concentration assemblies. Selective adsorption onto solid sorbent beds.	ESA-developed amines are used for their durability and selectivity in human spaceflight [1].
Sabatier Catalyst	Facilitates the CO₂ methanation reaction (CO₂ + 4H₂ → CH₄ + 2H₂O) in reprocessing assemblies.	Typically nickel- or ruthenium-based catalysts optimized for high conversion efficiency and longevity [1].
Bifunctional Materials	For integrated capture & conversion; materials that separate COâ‚‚ and catalyze its conversion to chemicals.	Emerging area for simplifying system design; often derived from industrial solid waste [63].
Calcium/Magnesium Silicates	Used in studies of permanent carbon storage via mineral carbonation, a parallel closure pathway.	Basalt and other reactive rocks are used for in-situ COâ‚‚ mineralization, providing permanent storage [64].
Alkaline Solvents (e.g., KOH)	Liquid solvents for highly efficient COâ‚‚ capture from air in direct air capture systems.	Requires significant energy for solvent regeneration; integrated electrolysis is an active research area [65].

The path to sustainable deep-space exploration hinges on closing the carbon loop. Through rigorous ground analogue testing employing standardized protocols and the continuous tracking of key in-situ performance metrics, researchers can de-risk technology, iterate designs, and progressively advance the Carbon Loop Closure Fraction toward 100%. The demonstrated success of systems like the ACLS on the ISS provides a critical validation of these approaches and a foundation for the next generation of life support systems required for missions to the Moon, Mars, and beyond.

The Role of Earth System Models in Validating Carbon Flux Estimates

Earth System Models (ESMs) are indispensable tools for simulating the complex interplay of physical, chemical, and biological processes that govern the Earth's climate [66]. They integrate the interactions of the atmosphere, ocean, land, ice, and biosphere to estimate the state of regional and global climate under a wide variety of conditions. A primary application of ESMs is the simulation and validation of carbon fluxes—the exchanges of carbon between the atmosphere, land, and ocean. These fluxes are critical for understanding the global carbon cycle and predicting future climate scenarios. The validation of these fluxes against observational data is a fundamental step in ensuring model reliability. Within the context of advanced life support systems research, particularly those aimed for long-duration space missions, understanding and validating these terrestrial carbon processes provides the foundational science for achieving closed-loop carbon cycling, where carbon dioxide is captured and recycled into oxygen and biomass, mimicking Earth's natural systems [1] [6].

Earth System Models: Structure and Components for Carbon Cycle Representation

ESMs are composed of interconnected model components that simulate individual parts of the climate system, such as the atmosphere, ocean, land, and sea ice, along with the exchanges of energy and mass between them [66]. What distinguishes ESMs from simpler climate models is their explicit simulation of the global carbon cycle and other biogeochemical processes [66].

• Atmospheric Component: This component simulates the large-scale movement of mass and energy. It resolves processes like atmospheric transport of CO2 and uses parameterizations for sub-grid-scale phenomena such as cloud formation. These processes are crucial for modeling the distribution and concentration of greenhouse gases.
• Land Surface Component: This module simulates hydrology, vegetation dynamics, and soil processes. It includes representations of plant photosynthesis, which absorbs CO2, and ecosystem respiration, which releases it. The growth and distribution of vegetation types are often simulated, making this component directly responsible for estimating land-based carbon fluxes like Gross Primary Productivity (GPP) and Net Ecosystem Productivity (NEP) [67] [66].
• Ocean Component: The ocean model simulates circulation, thermodynamics, and marine biogeochemistry. It includes representations of the biological pump, where phytoplankton absorb CO2 through photosynthesis and transport carbon to the deep ocean, and the solubility pump, which involves the physical dissolution of CO2 in seawater.
• Sea Ice and Cryosphere Component: While not always directly involved in carbon cycling, sea ice affects albedo and insulates the atmosphere from the ocean, influencing heat and gas exchanges.
• Carbon Cycle Integration: ESMs incorporate carbon cycling by coupling the land and ocean components. On land, models predict vegetation distribution and carbon exchange with the soil. In the ocean, components simulate the marine biosphere and chemistry, tracking nutrients that limit biological productivity [66]. This allows ESMs to answer critical questions about carbon sinks, such as why a significant portion of human-emitted CO2 is absorbed by land and ocean reservoirs instead of remaining in the atmosphere [66].

Methodologies for Validating Carbon Flux Estimates with ESMs

Validating the carbon flux estimates generated by ESMs is a multi-faceted process that involves comparing model outputs against a variety of observational data across different spatial and temporal scales. This process is essential for quantifying model uncertainty and building confidence in future projections [67] [66].

A combination of in situ measurements and remote sensing data is required for a robust validation.

• In Situ Measurements: Direct measurements provide ground truth data at specific locations. This includes:
  • Eddy Covariance Flux Towers: These installations directly measure the turbulent fluxes of CO2, water vapor, and energy between a terrestrial ecosystem and the atmosphere, providing direct estimates of NEP.
  • Forest Inventories and Soil Carbon Measurements: Field surveys that quantify carbon stocks in biomass and soils, used to validate model predictions of carbon storage.
  • Atmospheric CO2 Concentration Data: Measurements from ground-based stations (e.g., the Mauna Loa observatory) provide a record of atmospheric carbon accumulation, which integrates global flux information [66].
• Remote Sensing Data: Satellite observations offer extensive spatial coverage.
  • Leaf Area Index (LAI) and Solar-Induced Chlorophyll Fluorescence (SIF): Satellite-derived products that serve as proxies for photosynthetic activity, allowing for the evaluation of a model's simulation of GPP over large areas.
  • Active and Passive Microwave Sensors: Used to estimate above-ground biomass and soil moisture, which are key state variables in the carbon cycle.

Model Intercomparison Projects

A key protocol for assessing model performance and variability is participation in coordinated intercomparison projects like the Coupled Model Intercomparison Project (CMIP). In such exercises, multiple modeling groups run their ESMs under the same set of historical and future scenarios. The resulting spread in predictions, such as the 17–50% range for South America's contribution to global Net Biome Productivity (NBP) found in CMIP6, quantifies model uncertainty and highlights areas where processes are not well-constrained [67]. Analyzing this ensemble helps identify common biases and strengths across different model architectures.

Performance Metrics and Trend Analysis

Model outputs are quantitatively compared to observations using standardized metrics. Common metrics include the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and correlation coefficients. Beyond mean climate states, models are also evaluated on their ability to simulate temporal trends and responses to extreme events. For example, the temporal evolution of NBP in South America from CMIP6 models showed a slight decreasing trend in the 20th century (likely from land-use change emissions) shifting to positive values after 1990 (likely from CO2 fertilization), a pattern that can be checked against historical records [67]. Similarly, models can be tested on their simulation of carbon flux responses to widespread droughts, which cause higher heterotrophic respiration and disturbances [67].

Table 1: Key Carbon Flux Variables and Their Validation Data Sources

Carbon Flux/Stock Variable	Description	Primary Validation Data Sources
Gross Primary Productivity (GPP)	Total carbon fixed by plants via photosynthesis.	Satellite-derived SIF and LAI; eddy covariance tower data (as part of NEP).
Net Primary Productivity (NPP)	GPP minus plant respiration (autotrophic respiration).	Biomass inventory plots; satellite data.
Net Ecosystem Productivity (NEP)	NPP minus heterotrophic respiration (from soils).	Eddy covariance tower measurements.
Net Biome Productivity (NBP)	NEP minus carbon losses from disturbances (e.g., fires, harvest).	Regional carbon budget analyses; forest inventories.
Soil Carbon Stocks	Amount of carbon stored in organic matter in soils.	Soil core sampling and databases.

Advanced Techniques: Artificial Intelligence and Machine Learning

The field of Earth system modeling is being transformed by advances in artificial intelligence (AI) and machine learning (ML). These technologies offer promising avenues for enhancing ESMs by harnessing diverse data sources and overcoming limitations inherent in traditional parameterization techniques [68].

• Data-Driven Modeling: ML models can learn complex, non-linear relationships directly from data. For instance, the Aurora foundation model, pretrained on over one million hours of diverse geophysical data, demonstrates that AI can outperform complex operational forecasting systems in domains like weather and atmospheric chemistry at a fraction of the computational cost [69]. This approach can be adapted to learn and predict carbon fluxes from observed variables.
• Hybrid Modeling: A powerful approach is the harmonious amalgamation of physics-based and data-driven methodologies [68]. ML can be used to improve specific parameterizations within a traditional ESM, such as those for soil respiration or photosynthetic efficiency, leading to models that are both physically consistent and more accurate.
• Model Emulation: ML-based emulators can learn to approximate the input-output behavior of a full-complexity ESM. These emulators run thousands of times faster, enabling rapid exploration of different emission scenarios or parameter spaces, which is invaluable for uncertainty quantification and integrated assessment.

Application to Closed-Loop Life Support Systems

The principles of validating carbon fluxes with ESMs have a direct parallel in the development of advanced closed-loop life support systems for space exploration. The goal of these systems is to achieve "carbon loop closure," where astronauts' exhaled CO2 is recycled back into oxygen and, potentially, food.

• The Advanced Closed Loop System (ACLS): Deployed on the International Space Station, the ACLS is a technological analogue to the natural processes simulated by ESMs [1]. It concentrates CO2 from the cabin air, then uses a Sabatier reactor to combine CO2 with hydrogen (from water electrolysis) to produce water and methane. The water is then electrolyzed to produce oxygen for the crew [1]. This process mirrors the Earth's carbon cycle but in a highly engineered, miniature form.
• Validation in Life Support Systems: Just as ESMs are validated against atmospheric and ecosystem data, life support systems like the ACLS require rigorous monitoring and validation of their internal "carbon fluxes." This involves:
  • Tracking Mass Balances: Precisely measuring the inputs (astronaut-derived CO2, water) and outputs (O2, methane vented) of the system to ensure the loop is functioning as designed [1] [6].
  • Performance and Reliability Testing: Operating the system for extended periods (e.g., one year over two years for ACLS) to demonstrate its performance and reliability under real-world conditions [1].
  • Integration with Other Subsystems: The ultimate closed-loop system would integrate air revitalization (ACLS) with water recovery and food production systems. Research into plant growth chambers for space (a form of bioregenerative life support) directly uses knowledge of GPP and NPP validated by terrestrial ESMs to optimize carbon assimilation and biomass production in a controlled environment.

Table 2: Research Reagent Solutions for Carbon Cycle and Life Support Research

Reagent / Material	Function in Research
Amine-based Sorbents	Traps and concentrates CO2 from air streams for subsequent processing or measurement; used in both ACLS [1] and experimental setups.
Sabatier Reactor Catalyst	Facilitates the chemical reaction between CO2 and H2 to produce CH4 and H2O; core component of engineered closed-loop systems [1].
Electrolyzer Cell	Splits water (H2O) into oxygen (O2) and hydrogen (H2); provides O2 for crew and H2 for the Sabatier process [1].
Licor LI-850 CO2/H2O Analyzer	Precisely measures CO2 and water vapor concentrations in gas streams; essential for validating carbon flux measurements in both eddy covariance towers and life support system prototypes.
Soil and Plant Nutrient Solutions	In bioregenerative life support research, these solutions sustain plant growth for food production and carbon assimilation, closing the carbon loop.

Earth System Models play a critical role in validating carbon flux estimates by providing a comprehensive, physics-based framework to integrate observations and test our understanding of the global carbon cycle. The methodologies of model intercomparison, multi-source data validation, and trend analysis are essential for quantifying uncertainties and improving projections. The emergence of AI and foundation models like Aurora presents a transformative opportunity to enhance the precision and efficiency of these models. The knowledge gained from validating terrestrial carbon cycles is not only vital for climate science but also provides the foundational principles for engineering closed-loop life support systems. As we strive to sustain human life in the isolated and resource-limited environment of space, the lessons from Earth's complex carbon system, encapsulated and validated within our most advanced models, will light the way.

Diagrams

ESM Carbon Flux Validation Workflow

Closed-Loop Carbon Cycling in a Life Support System

Benchmarking Against NASA CMS and GEDI for Carbon Monitoring

For researchers in advanced life support systems, achieving carbon loop closure is a fundamental challenge. It requires precise, real-time monitoring and management of carbon stocks and fluxes to create sustainable, regenerative environments for long-duration space missions. Earth observation technologies developed by NASA provide critical benchmarking frameworks for these efforts. The Carbon Monitoring System (CMS) and the Global Ecosystem Dynamics Investigation (GEDI) mission offer sophisticated methodologies and data products that enable researchers to quantify, model, and verify carbon sequestration and emissions with scientific rigor [70]. These systems establish the gold standard for carbon accounting in closed ecological systems, providing the monitoring capabilities essential for managing life support systems where atmospheric regeneration and food production depend on precise carbon cycling.

The integration of CMS and GEDI methodologies creates a powerful paradigm for carbon loop closure research. GEDI's spaceborne lidar delivers unprecedented three-dimensional data on vegetation structure, which serves as a proxy for carbon storage in biological systems [70]. Meanwhile, NASA CMS integrates these structural measurements with multi-source satellite data and models to create comprehensive carbon budgets [70]. For life support research, these approaches can be adapted to monitor carbon distribution between plant biomass, air, water, and waste streams—the core compartments of any closed loop system. This technical guide provides the experimental protocols and benchmarking frameworks necessary to apply these advanced carbon monitoring capabilities to advanced life support systems research.

NASA Carbon Monitoring System (CMS)

NASA's Carbon Monitoring System is a research program that leverages NASA's satellite data, modeling, and emerging technologies to develop accurate and reliable carbon monitoring capabilities. The program focuses on creating scientifically robust data products that can support policy, regulation, and management activities related to carbon cycle science. A primary objective includes developing Measurement, Reporting, and Verification (MRV) systems that deliver transparent data products meeting the precision and accuracy requirements of carbon trading protocols [70]. The CMS actively engages with both U.S. and international stakeholders to advance carbon monitoring science and applications, making it particularly valuable for establishing standardized protocols in life support system research.

GEDI Mission Status and Post-Hibernation Operations

The GEDI instrument, mounted on the International Space Station (ISS), is the first spaceborne lidar mission specifically dedicated to mapping vegetation structure and its changes over time [70]. After a temporary hibernation period from March 2023 to April 2024, the GEDI instrument was successfully reinstalled on the ISS and has resumed collecting high-resolution observations of Earth's three-dimensional vegetation structure [71]. As of November 2024, the mission had collected 33 billion Level-2A land surface returns, with approximately 12.1 billion passing quality filters [71].

The instrument's three lasers are currently operating nominally, with each having logged over 22,000 hours in firing mode as of March 2025, collecting more than 20 billion shots each [71]. Approximately 72% of operational time has been dedicated to collecting data directly over land surfaces, with 95,346 hours of science data downlinked by April 2025, averaging 51.21 GB of data per day [71]. The mission has significantly expanded its forest structure and biomass database (FSBD), which now contains 27,876 simulated footprints to support improved algorithm calibration for biomass estimation [71].

Table: GEDI Mission Operational Status (2024-2025)

Parameter	Status	Relevance to Carbon Monitoring
Operation Period	Post-hibernation (since April 2024)	Ensures continuity of carbon time series data
Laser Performance	3 lasers operating nominally	Maintains data quality and coverage for structure metrics
Data Collection	33 billion L2A returns; 12.1 billion quality-filtered	Provides robust dataset for carbon model calibration
Coverage	72% over land surfaces	Enables comprehensive terrestrial carbon assessment
Data Products	V2.1 released; V3.0 in development	Continuously improved accuracy for carbon estimation

Core Technical Capabilities and Data Products

GEDI Data Products and Specifications

GEDI's data processing pipeline generates multiple data products that progress from fundamental measurements to derived biogeophysical parameters essential for carbon monitoring. The latest product releases (Version 2.1) incorporate post-storage data through November 2024 and include L1B (geolocated waveform data), L2A (ground elevation and canopy height metrics), L2B (canopy cover and vertical profile metrics), and L4A (aboveground biomass density) data products [71]. In January 2025, the team also released the new L4C footprint-level Waveform Structural Complexity Index (WSCI) product using pre-storage data [71]. The upcoming V3.0 release will incorporate both pre- and post-storage data, with anticipated improvements in quality filtering, geolocation accuracy, and algorithm performance [71].

For carbon loop closure research, the L4A aboveground biomass density product is particularly valuable as it provides direct estimates of carbon stored in vegetation when combined with appropriate carbon conversion factors. The L2A elevation and height metrics enable tracking of carbon stock changes over time, while the newly introduced L4C WSCI product offers insights into structural complexity that correlates with ecosystem function and carbon sequestration potential. These data products provide the essential benchmarks for quantifying carbon storage in plant-based life support systems.

Table: GEDI Data Products for Carbon Monitoring

Product Level	Key Metrics	Application in Carbon Monitoring
L1B	Geolocated waveform data	Fundamental lidar return signal for custom processing
L2A	Ground elevation, canopy top height, relative height metrics	Canopy structure assessment; growth monitoring
L2B	Canopy cover fraction, leaf area index, vertical profile metrics	Photosynthetic capacity estimation; carbon uptake potential
L4A	Aboveground biomass density (AGBD)	Direct carbon stock quantification in vegetation
L4C	Waveform Structural Complexity Index (WSCI)	Ecosystem complexity assessment; habitat quality

NASA CMS Integration Capabilities

The NASA Carbon Monitoring System excels at integrating diverse data sources to create comprehensive carbon monitoring solutions. The system synergistically combines data from multiple NASA satellite assets—including Landsat, GEDI, and MODIS—to track historical forest cover changes and attribute underlying drivers of those changes [70]. This multi-sensor approach enables more accurate monitoring of carbon stocks and fluxes than any single data source could provide. For life support system applications, this integration paradigm can be adapted to combine data from multiple sensor types monitoring different components of the carbon cycle.

A key innovation within CMS is the development of the Allometric Scaling and Resource Limitation (ASRL) model, which synergistically uses biophysical theory with spaceborne and airborne remote sensing data, including foundational GEDI lidar altimetry data, to generate large-scale continuous patterns of forest height and aboveground biomass [70]. This approach represents a significant advancement beyond purely statistical models by incorporating physiological principles that govern plant growth and carbon allocation. For closed loop system research, such mechanistic models could be adapted to predict carbon sequestration rates in controlled environment agriculture based on environmental parameters and plant functional types.

Experimental Protocols for Carbon Monitoring

Aboveground Biomass Estimation Protocol

Accurate estimation of aboveground biomass density represents a cornerstone of carbon monitoring for both terrestrial ecosystems and plant-based life support systems. The following protocol outlines the standardized methodology employed by GEDI to generate its L4A biomass product, which can be adapted for controlled environment applications:

• Waveform Processing: Begin with GEDI L1B waveforms that provide the full waveform energy profile for each footprint. Apply quality filters to remove signals affected by noise, clouds, or off-nadir pointing [71]. The quality flags within the GEDI data products identify suitable waveforms for biomass estimation.

• Metric Extraction: From each quality-filtered waveform, extract relative height (RH) metrics that characterize the vertical distribution of canopy elements. These metrics include the energy quantiles (e.g., RH50, RH75, RH90, RH95) representing the height at which specified percentiles of waveform energy occur [71].

• Stratification Approach: Stratify the data according to plant functional types and geographic regions to account for structural differences in vegetation. GEDI employs a 1-km stratification layer based on plant functional type and geographic world region, though research is ongoing to replace this with a 30-m product derived from Landsat for improved precision [71].

• Model Application: Apply pre-developed biomass estimation models that relate waveform metrics to aboveground biomass density. These models are calibrated using the Forest Structure and Biomass Database (FSBD), which contains forest inventory and airborne laser scanning data from thousands of locations globally [71]. The FSBD currently includes 27,876 simulated footprints with paired field measurements and airborne lidar data.

• Uncertainty Quantification: Generate uncertainty estimates for each biomass prediction using the model's error propagation framework. This provides essential information on the reliability of carbon stock estimates for decision-making processes.

For life support applications, this protocol can be modified to use terrestrial laser scanning or simpler depth sensors in controlled environments, with calibration using destructive harvesting of plants to develop system-specific allometric equations.

Data Fusion Protocol for Enhanced Carbon Monitoring

Addressing coverage gaps and enhancing spatial continuity requires sophisticated data fusion techniques. The following protocol outlines methodologies being advanced by the GEDI science team for combining lidar with complementary data sources:

• Multi-Sensor Alignment: Precisely co-register GEDI waveforms with complementary datasets including Synthetic Aperture Radar (SAR) from missions such as NASA-ISRO SAR (NISAR), DLR's TerraSAR-X, and TanDEM-X, as well as optical imagery from Landsat and Sentinel-2 [71]. This geometric alignment is crucial for pixel-level data fusion.

• Synergistic Mapping: Employ machine learning approaches, particularly random forests and gradient boosting machines, to model the relationship between GEDI's direct structural measurements and the spectral/polarimetric responses from other sensors. Research has demonstrated successful pantropical forest height mapping by integrating GEDI lidar with TanDEM-X InSAR data [72].

• Gridded Product Generation: Apply the trained models to wall-to-wall satellite data to create continuous maps of vegetation structure and carbon stocks at regional to global scales. The GEDI team is developing gridded products specifically tailored to end-user needs that provide complete spatial coverage [71].

• Error Assessment and Validation: Quantify map accuracy using independent validation data from airborne laser scanning and field measurements. The GEDI team continuously assesses error and bias in their products and works to improve algorithmic performance through iterative refinement [71].

For closed loop system research, this protocol can be adapted to fuse data from multiple sensor types—including spectral sensors, gas analyzers, and plant growth monitors—to create comprehensive carbon budgets that track carbon flow through all system compartments.

Diagram: Carbon Monitoring with NASA CMS and GEDI

Implementing carbon monitoring protocols based on NASA CMS and GEDI methodologies requires specific data resources, analytical tools, and computational infrastructure. The following toolkit outlines essential components for establishing a robust carbon monitoring framework for advanced life support systems research.

Table: Essential Research Toolkit for Carbon Monitoring

Tool Category	Specific Resources	Function in Carbon Monitoring
Data Access Portals	ORNL DAAC, NASA CMS Data Portal, GEDI Data Portal	Primary sources for downloading GEDI waveforms, CMS carbon products, and associated metadata
Processing Software	NASA's Goddard LiDAR Analysis Software (GLASt), R, Python	Specialized tools for waveform processing, metric extraction, and biomass model implementation
Reference Databases	Forest Structure and Biomass Database (FSBD)	Calibration/validation data containing paired field measurements and airborne lidar
Ancillary Datasets	Landsat, Sentinel-2, TanDEM-X, NISAR	Complementary data sources for filling coverage gaps and improving spatial continuity
Modeling Frameworks	Allometric Scaling and Resource Limitation (ASRL) model	Theory-based approach for mapping forest height, biomass, and carbon sequestration potential

Application to Carbon Loop Closure in Advanced Life Support Systems

The integration of CMS and GEDI methodologies provides a transformative framework for addressing carbon loop closure in advanced life support systems. By adapting these Earth observation technologies to controlled environments, researchers can achieve unprecedented monitoring and management of carbon flows through all system compartments. The three-dimensional structural data from GEDI analogues can quantify carbon storage in plant biomass, while the integrated assessment approaches from CMS can track carbon distribution between atmospheric, aqueous, and solid waste streams [1].

For spaceflight applications, these monitoring capabilities directly support the operation of systems like the Advanced Closed Loop System (ACLS), which recycles carbon dioxide into oxygen through a combination of concentration, Sabatier reaction, and electrolysis processes [1]. The ACLS currently recovers about 50% of the carbon dioxide from cabin air, producing oxygen for three astronauts while reducing water resupply needs by approximately 400 liters annually [1]. Incorporating sophisticated carbon monitoring analogous to CMS and GEDI approaches could optimize these processes further by providing real-time data on carbon stocks and fluxes throughout the system.

The data fusion protocols advanced by the GEDI science team, particularly the integration of lidar with SAR and optical data [72], offer a powerful paradigm for multi-sensor integration in life support systems. By combining information from gas analyzers, biomass sensors, and water chemistry monitors, researchers could develop comprehensive carbon balance models that predict system behavior and identify potential points of failure before they compromise crew safety. Furthermore, the benchmarking capabilities enabled by these technologies allow for direct comparison between different system configurations and operational strategies, accelerating innovation in life support system design.

Diagram: Carbon Loop Closure with Monitoring

Future Directions and Research Opportunities

The ongoing development of NASA CMS and GEDI technologies presents multiple opportunities for enhancement of carbon monitoring in advanced life support systems. The GEDI mission is currently exploring data fusion opportunities with other missions—including NASA-ISRO SAR (NISAR), DLR's TerraSAR-X and TanDEM-X, and the European Space Agency's Biomass mission—to address coverage gaps in tropical regions [71]. These multi-sensor approaches can be adapted to life support systems where redundant monitoring enhances system reliability.

The upcoming V3.0 GEDI data release will incorporate both pre- and post-storage data with improved quality filtering, geolocation accuracy, and algorithm performance [71]. For life support applications, this continuous improvement philosophy should be embraced through regular calibration and refinement of carbon monitoring protocols. Additionally, the development of gridded products tailored to end-user needs [71] provides a model for creating customized carbon monitoring dashboards for life support system operators.

Future research should focus on adapting the mechanistic modeling approaches used in CMS, such as the Allometric Scaling and Resource Limitation (ASRL) model [70], to predict carbon sequestration rates in controlled environment agriculture based on light, water, and nutrient availability. Furthermore, the integration of near-real-time monitoring capabilities, analogous to those being developed by Carbon Monitor [73], could enable dynamic control of carbon flows in life support systems, optimizing the balance between food production, atmospheric regeneration, and waste recycling.

Defining Key Performance Indicators for Next-Generation Systems

In the pursuit of long-duration space missions, the development of robust closed-loop life support systems is paramount. These systems are designed to maintain a sustained human presence in space by revitalizing air, recovering water, and recycling waste, thereby creating a self-sustaining ecosystem. Within this context, Key Performance Indicators (KPIs) emerge as the essential signposts that guide research and development, transforming a sea of operational data into a clear strategic direction [74]. For next-generation systems focused on carbon loop closure, KPIs move beyond mere metrics to become actionable guides that steer scientific inquiry and technological innovation toward mission-critical objectives.

The inherent complexity of advanced life support systems, which integrate biological, chemical, and physical processes, demands a disciplined approach to measurement. Effective KPIs illuminate the path from fundamental research to applied engineering, ensuring that resources and efforts are aligned with the overarching goal of system closure and sustainability. This guide provides a structured framework for researchers and scientists to define, implement, and utilize KPIs that accurately capture the performance and potential of these next-generation systems, with a specific focus on carbon cycling and closure.

Foundational KPI Frameworks

The SMART Framework for KPI Development

A transformative framework for crafting effective KPIs is encapsulated by the SMART acronym: Specific, Measurable, Achievable, Relevant, and Time-bound. This framework offers a robust blueprint to ensure that KPIs are not just numbers on a dashboard but are powerful tools that drive progress and decision-making [74].

• Specific: A KPI must be precise and unambiguous. For a carbon loop system, a goal like "improve carbon recovery" is too vague. A specific alternative would be: "Increase the carbon conversion efficiency of the anaerobic digestion process for human waste by 15%." This pinpoints the what, where, and how much of the objective.
• Measurable: A KPI must be quantifiable. Introducing a tangible metric, such as "carbon conversion efficiency" or "volatile fatty acid (VFA) yield," allows for tracking progress over time and enables objective assessment [74].
• Achievable: While ambition is crucial, KPIs must be grounded in reality. Targets should be challenging yet within the realm of scientific and engineering possibility, considering current technological readiness levels. Setting an unattainable goal to "achieve 100% carbon closure within one year" can lead to demoralization and wasted resources [74].
• Relevant: Every KPI must align directly with the core strategic objectives of the research. For instance, measuring the growth rate of a specific cyanobacterial strain is relevant if that strain is integral to consuming COâ‚‚ and producing oxygen and biomass within the closed system [74] [7].
• Time-bound: A defined time horizon creates urgency and enables periodic assessment. A time-bound KPI, such as "Characterize the microbial community responsible for waste conversion to VFAs within the next two quarters," provides a clear window for execution and review [74].

The Measure-Perform-Review-Adapt (MPRA) Cycle

For implementing a KPI system, the Measure-Perform-Review-Adapt (MPRA) framework provides a disciplined, practical approach for development and long-term management. This cycle ensures that measurement is not a static activity but a dynamic process of continuous improvement [75].

The following diagram illustrates the iterative, four-phase MPRA cycle:

The phases of this cycle are:

• Measure: This initial phase involves articulating strategic intent and selecting the right measures. The development of meaningful measures starts with clear objectives, which are qualitative, continuous improvement actions critical to strategy success. The process involves identifying objectives, understanding alternative measures, selecting the final KPIs, and rigorously documenting their definitions to ensure consistent calculation [75].
• Perform: In this phase, the organization sets specific targets and thresholds for its KPIs and implements improvement initiatives. Performance is evaluated against these targets, with thresholds establishing the points where an indicator displays as being on-track (e.g., green), requiring attention (yellow), or off-track (red) [75].
• Review: This phase transforms data into evidence-based knowledge. It involves collecting and visualizing performance data to identify trends, followed by analysis to draw conclusions about the effectiveness of improvement actions [75].
• Adapt: The final phase explores whether improvement strategies were effective and assumptions were valid. This can lead to a reforecasting of targets, new initiatives, or even a recalibration of the strategy itself, thus feeding back into the "Measure" phase and closing the loop [75].

Next-Generation KPIs for Carbon Loop Closure

Moving beyond traditional metrics requires KPIs that capture the complex, interconnected nature of closed-loop systems. The following tables categorize and define next-generation KPIs relevant to carbon closure research, integrating concepts from modern customer experience measurement adapted for a research context [76].

Table 1: Process Efficiency & Closure KPIs

KPI	Definition & Measurement	Research Objective
Carbon Conversion Efficiency	Percentage of inbound carbon (from waste/COâ‚‚) converted into target outputs (VFAs, biomass). Measured via elemental analysis and mass balance.	To maximize the primary carbon conversion process and minimize carbon loss.
System Closure Index	Ratio of resources (C, O, Hâ‚‚O) regenerated internally to total crew consumption. A composite metric derived from system-wide mass balance models.	To quantify progress toward full system self-sufficiency and reduce Earth dependence.
Volatile Fatty Acid (VFA) Yield	Mass of VFAs (e.g., acetate) produced per unit mass of carbon input to anaerobic digestion. Measured via chromatography.	To optimize the upstream production of key carbon substrates for downstream biomanufacturing [7].
Carbon Retention in Biomass	Percentage of carbon input captured in harvested microbial or plant biomass. Measured via dry weight and carbon content analysis.	To evaluate the efficiency of biological systems in capturing and sequestering carbon for food or material production.

Table 2: Biological & Functional Performance KPIs

KPI	Definition & Measurement	Research Objective
Microbial Community Stability	Temporal variance in the relative abundance of key functional taxa in the bioreactor. Measured via 16S rRNA sequencing and metagenomics.	To ensure the reliability and functional resilience of the core waste-processing bioprocess [7].
Cyanobacterial Productivity	Growth rate and oxygen evolution rate of engineered cyanobacterial strains using COâ‚‚ and processed wastewater.	To optimize the integration of phototrophic systems for simultaneous air revitalization and biomass production [7].
Plant Growth Efficiency	Biomass yield (edible portion) per unit of input (light, COâ‚‚, recycled nutrients). Measured in controlled environment agriculture studies.	To characterize and select plant species for maximum food output with minimal resource input in space environments [8].

Table 3: Operational & System-Level KPIs

KPI	Definition & Measurement	Research Objective
Resource Per Interaction (RPI)	Average mass of a key resource (e.g., carbon, water) recovered or produced per unit of energy or crew time invested.	To shift focus from pure efficiency to the value and impact of system operations, justifying investment [76].
Functional Resilience Score	Measure of system performance recovery time after a simulated fault (e.g., pump failure, contamination).	To assess the robustness and fault tolerance of the integrated life support system.
Technology Readiness Level (TRL) Progression Rate	Time or resources required to advance a subsystem from one TRL to the next.	To track the pace of innovation and de-risk technology integration for mission planning.

Experimental Protocols for KPI Validation

Protocol: Quantifying Carbon Conversion Efficiency in Anaerobic Digestion

This protocol provides a detailed methodology for establishing the Carbon Conversion Efficiency KPI, a critical metric for evaluating the core waste valorization process.

1. Hypothesis: The anaerobic microbial community, when optimized, will convert over 60% of the carbon present in a standardized synthetic human waste stream into target products (VFAs and COâ‚‚), with less than 10% lost to methane production.

2. Materials:

• Bioreactors: Bench-scale (e.g., 2L) anaerobic bioreactors with pH, temperature, and mixing control.
• Inoculum: A well-characterized methanogenic consortium, with protocols for suppressing methanogens (e.g., via chemical inhibition with 2-bromoethanesulfonate) in test reactors.
• Substrate: Synthetic human waste formulation with a known, standardized carbon content.
• Analytical Equipment: HPLC system for VFA quantification, Gas Chromatograph for CHâ‚„ and COâ‚‚ analysis, TOC Analyzer for total carbon, and DNA sequencer for microbial community analysis.

3. Procedure:

• Step 1: System Setup. Set up triplicate test reactors (methane-suppressed) and control reactors (active methanogens). Flush with Nâ‚‚ to ensure anaerobiosis.
• Step 2: Feeding Regime. Operate in fed-batch mode, adding a precise mass of synthetic waste substrate daily. Record all inputs.
• Step 3: Sampling. Take daily liquid and gas samples from each reactor.
• Step 4: Analysis.
  • Analyze liquid samples for VFA concentration (HPLC) and total carbon (TOC).
  • Analyze gas composition for CHâ‚„ and COâ‚‚ percentage (GC).
  • Weekly, extract DNA from biomass for microbial community analysis.
• Step 5: Data Integration & Calculation. Conduct the experiment over a period equivalent to three hydraulic retention times to achieve steady-state. Calculate Carbon Conversion Efficiency using the formula: Carbon Conversion Efficiency (%) = (Mass of C in VFAs + Mass of C in COâ‚‚) / Total Carbon Input * 100

4. Interpretation: A successful outcome will show a significantly higher Carbon Conversion Efficiency in the test reactors compared to controls, demonstrating effective redirection of carbon toward valuable intermediate products instead of methane. Microbial data will correlate community shifts (e.g., reduction in methanogens) with the measured performance change [7].

Workflow for Carbon Loop KPI Validation

The experimental process for validating carbon conversion KPIs is a multi-stage endeavor, as visualized below.

The Scientist's Toolkit: Research Reagent Solutions

The rigorous measurement of advanced KPIs depends on a suite of specific reagents and analytical tools. The following table details key materials essential for the experimental protocols cited in this guide.

Table 4: Essential Research Reagents and Materials

Item	Function / Application in Carbon Loop Research
2-Bromoethanesulfonate (BES)	A chemical inhibitor used to selectively suppress methanogenic archaea in anaerobic digestion experiments, allowing for the study of carbon diversion to volatile fatty acids [7].
Synthetic Human Waste Formulation	A standardized, chemically defined substrate that simulates astronaut waste. It provides a consistent carbon and nutrient source for reproducible bioreactor experiments, eliminating the variability of real waste.
VFA Standard Mix	A high-purity chemical standard containing acetate, propionate, butyrate, etc. It is used to calibrate HPLC systems for the accurate quantification of VFAs, the key metrics in anaerobic process performance [7].
DNA Extraction Kit (for Complex Samples)	A commercial kit optimized for extracting high-quality microbial DNA from complex, difficult-to-lyse samples like bioreactor sludge, enabling subsequent sequencing and community analysis.
16S rRNA Sequencing Primers	Specific primer sets that target conservative regions of the bacterial and archaeal 16S rRNA gene, allowing for the characterization and quantification of microbial community structure via amplicon sequencing [7].
Elemental Analyzer	Instrument used for the precise determination of carbon, hydrogen, and nitrogen content in solid and liquid samples (e.g., biomass, waste substrate), which is critical for performing system mass balances.

Data Visualization and Reporting for Research KPIs

Effective communication of KPI outcomes is critical for aligning research teams and informing stakeholders. Data visualization transforms raw performance data into an accessible, evidence-based narrative.

• Principles for Data Tables: When presenting specific data points, tables should be designed for clarity. This includes including only the data the audience needs to focus on, using intentional titles and conditional formatting (e.g., color-coding based on performance thresholds) to emphasize key takeaways, and ensuring the typography is consistent with the surrounding report [77].
• Trend Analysis with Sparklines: Incorporating small, simple line charts known as sparklines within tables can provide a quick graphical summary of a row of data, showing a performance trend over time without occupying significant space [77].
• Visualizing Associations: Two-way tables are highly effective for examining associations between two categorical variables. For example, a table could be used to show the relationship between different carbon source inputs and the resulting VFA profiles, with color used to highlight significant correlations [77].

Adopting these frameworks and metrics will equip research teams with the evidence-based clarity needed to drive innovation, secure funding, and ultimately achieve the breakthrough of sustainable, closed-loop life support for the future of space exploration.

Conclusion

Closing the carbon loop is a foundational challenge for achieving sustainable advanced life support, with critical implications for both space exploration and terrestrial environmental management. The integration of physical-chemical systems with emerging bioregenerative methods offers a promising path toward near-complete oxygen recovery and resource independence. However, overcoming reliability issues, optimizing for mass and energy efficiency, and validating systems through rigorous ground-based testing remain paramount. Future progress hinges on interdisciplinary collaboration, leveraging insights from Earth system science, advancements in modeling and AI, and innovations in materials and biological systems. For the biomedical and research community, these closed-loop technologies present a paradigm for managing isolated clinical environments and contribute to the broader goal of developing resilient, carbon-neutral systems for human health and habitation. The ongoing research, highlighted in forums like the 2025 MELiSSA Conference, will be crucial for paving the way to a sustainable future both on Earth and beyond.

References

• 1. ESA - Advanced Closed Loop System [https://www.esa.int/Science_Exploration/Human_and_Robotic_Exploration/Research/Advanced_Closed_Loop_System]
• 2. Design Status of the Advanced Closed Loop System ACLS ... [https://ttu-ir.tdl.org/items/6df5bcfc-a444-4f08-b8bc-7c8a06f8e2fa]
• 3. Closed-loop carbon management strategies for climate ... [https://www.sciencedirect.com/science/article/pii/S2589004225017705]
• 4. How to close the carbon with CO2 conversion loop [https://www.elsevier.com/connect/how-to-close-the-carbon-loop-with-co2-conversion]
• 5. Sustainable Life for Deep Space Missions Support [https://www.numberanalytics.com/blog/sustainable-life-support-deep-space-missions]
• 6. Subsystems - NASA Life Support [https://www.nasa.gov/reference/jsc-life-support-subsystems/]
• 7. Saint Louis University Joins NASA-Funded Consortium to ... [https://www.slu.edu/news/2025/november/nasa-consortium.php]
• 8. 2025 MELiSSA Conference [https://2025melissaconference.org/]
• 9. Global Carbon Budget 2024 - ESSD Copernicus [https://essd.copernicus.org/articles/17/965/2025/essd-17-965-2025.html]
• 10. Micro/Nano-Reconfigurable Robots for Intelligent Carbon ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12583435/]
• 11. A brief review for chemical looping combustion as ... [https://www.sciencedirect.com/science/article/abs/pii/S0048969720364226]
• 12. Developing an alternative medium for in-space ... [https://www.nature.com/articles/s41467-025-56088-2]
• 13. BLSS: a contribution to future life support [https://pubmed.ncbi.nlm.nih.gov/11537782/]
• 14. Terrestrial carbon cycle: tipping edge of climate change ... [https://pubs.rsc.org/en/content/articlehtml/2022/ea/d1ea00102g]
• 15. ESSDD - Global Carbon Budget 2025 [https://essd.copernicus.org/preprints/essd-2025-659/]
• 16. Evaluation of 11 terrestrial carbon–nitrogen cycle models ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC4288990/]
• 17. Inter- and Multi-agency Research and Funding Opportunities [https://www.carboncyclescience.us/inter-multi-agency-research-funding-opportunities]
• 18. Launching NASA and Humanity into Deep Space [https://www.nasa.gov/missions/station/iss-research/international-space-station-launching-nasa-and-humanity-into-deep-space/]
• 19. Celebating 25 years of continuous human presence in orbit [https://issnationallab.org/press-releases/international-space-station-marks-25-years-of-continuous-human-presence-a-legacy-of-global-collaboration-and-innovation/]
• 20. Crew Works on Spacesuits, Life Support as Human ... [https://www.nasa.gov/blogs/spacestation/2025/05/29/crew-works-on-spacesuits-life-support-as-human-research-continues/]
• 21. HabSim: testing deep space habitat resilience [https://engineering.purdue.edu/ME/News/2025/habsim-testing-deep-space-habitat-resilience-]
• 22. Closing carbon cycles: Evaluating the performance of multi ... [https://www.sciencedirect.com/science/article/pii/S2212982017305589]
• 23. Chapter: 2 ADVANCED LIFE SUPPORT SYSTEMS [https://www.nationalacademies.org/read/5826/chapter/4]
• 24. A membrane Sabatier system for water recovery and rocket ... [https://www.nature.com/articles/s41467-025-63667-w]
• 25. Electrolyzer cell-methanation/Sabatier reactors integration ... [https://www.sciencedirect.com/science/article/abs/pii/S0306261922015252]
• 26. Biogas upgrading through CO2 methanation in a multiple- ... [https://www.sciencedirect.com/science/article/pii/S2212982025000228]
• 27. The Basics of Electrochemical Cells [https://www.msesupplies.com/blogs/news/the-basics-of-electrochemical-cells?srsltid=AfmBOoqnUbedsg0lMIkwUxjddWpjt1dp7Ir4z6hbvbd2Cp4dSGIvFoZL]
• 28. Review of research into bioregenerative life support system ... [https://www.sciencedirect.com/science/article/abs/pii/S2214552421000663]
• 29. Plant and microbial science and technology as ... [https://www.nature.com/articles/s41526-023-00317-9]
• 30. Prototype Lunar/Mars Greenhouse [https://ssel.arizona.edu/missions/prototype-lunarmars-greenhouse]
• 31. Hypergravity Resilience and Predictive Yield Models - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC12387554/]
• 32. Advanced carbon sequestration by the hybrid system of ... [https://www.sciencedirect.com/science/article/abs/pii/S0960852421005216]
• 33. In situ resource utilization [https://en.wikipedia.org/wiki/In_situ_resource_utilization]
• 34. Environmental Control and Life Support Systems (ECLSS) [https://www.nasa.gov/reference/environmental-control-and-life-support-systems-eclss/]
• 35. Next Generation Life Support (NGLS) [https://www.nasa.gov/directorates/stmd/game-changing-development-program/next-generation-life-support-ngls/]
• 36. Overview: In-Situ Resource Utilization [https://www.nasa.gov/overview-in-situ-resource-utilization/]
• 37. ISS ECLSS [https://en.wikipedia.org/wiki/ISS_ECLSS]
• 38. Prospecting in-situ resources for future crewed missions to ... [https://www.sciencedirect.com/science/article/pii/S0094576524003710]
• 39. A Comprehensive Guide to Digital Twin Simulation for ... [https://www.simio.com/a-comprehensive-guide-to-digital-twin-simulation-for-beginners/]
• 40. UX for Digital Twins: 6 Interface Strategies That Will ... [https://www.aufaitux.com/blog/ux-digital-twins-smart-interface-strategies/]
• 41. Use 3D Scenes Studio (preview) - Azure Digital Twins [https://learn.microsoft.com/en-us/azure/digital-twins/how-to-use-3d-scenes-studio]
• 42. Digital twins for dynamic life cycle assessment in the built ... [https://www.sciencedirect.com/science/article/pii/S0048969725015700]
• 43. Digital Twin-Driven Low-Carbon Service Design and ... [https://www.mdpi.com/2071-1050/17/21/9877]
• 44. Understanding Failure Mechanisms and Components [https://reliability.com/resources/articles/failure-mechanism-and-components/]
• 45. Understanding mean time to failure (MTTF) for measuring ... [https://www.atlassian.com/incident-management/kpis/mttf]
• 46. Developing a quantitative analytical framework for carbon ... [https://www.nature.com/articles/s41599-025-05145-8]
• 47. Development of CO 2 visualization loop experimental ... [https://www.sciopen.com/article/10.16791/j.cnki.sjg.2025.01.021]
• 48. Closed-loop and congestion control of the global carbon- ... [https://link.springer.com/article/10.1007/s10584-021-03040-0]
• 49. Methods for Carbon Mass Closure in Polyolefin Hydrocracking [https://pmc.ncbi.nlm.nih.gov/articles/PMC12381696/]
• 50. Experimental Development of Calcium Looping Carbon ... [https://www.mdpi.com/1996-1073/16/9/3623]
• 51. Basics of the Carbon Cycle and the Greenhouse Effect [https://www.noaa.gov/basics-of-carbon-cycle-and-greenhouse-effect]
• 52. Development of Metal-impregnated Single Walled... : Internet Archive [https://archive.org/details/nasa_techdoc_20040013502]
• 53. Surface distributions and vertical profiles of trace gases ... - AMT [https://amt.copernicus.org/articles/18/1163/2025/]
• 54. Demonstration of a novel tracer gas method to investigate ... [https://www.sciencedirect.com/science/article/pii/S2950362025000104]
• 55. Can This Tracer Molecule Help Us Understand the Carbon ... [https://www.neonscience.org/impact/observatory-blog/can-tracer-molecule-help-us-understand-carbon-cycle]
• 56. Certified protocol for carbon removal via Electrolytic ... [https://isometric.com/writing-articles/a-new-protocol-for-electrolytic-seawater-mineralization]
• 57. Unlocking Healthy Longevity: Researchers Find Oxygen Restriction... [https://scitechdaily.com/unlocking-healthy-longevity-researchers-find-oxygen-restriction-extends-lifespan/]
• 58. Making experimental data tables in the life sciences more FAIR [https://pmc.ncbi.nlm.nih.gov/articles/PMC7736789/]
• 59. Multi-scenario simulation and spatial optimization of carbon ... [https://cbmjournal.biomedcentral.com/articles/10.1186/s13021-025-00350-z]
• 60. Multi-scenario land use change simulation and spatial ... [https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0316255]
• 61. Multi-scenario simulation and carbon storage assessment ... [https://www.sciencedirect.com/science/article/abs/pii/S0264837725000638]
• 62. Toward an adaptive monitoring design for leakage risk [https://www.sciencedirect.com/science/article/abs/pii/S1750583617309271]
• 63. Chemical looping CO2 capture and in-situ conversion as a ... [https://www.sciencedirect.com/science/article/pii/S2772656823000738]
• 64. Review on in-situ CO2 mineralization sequestration [https://link.springer.com/article/10.1007/s40789-025-00755-8]
• 65. Closing the Loop: Unexamined Performance Trade-Offs of ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11091874/]
• 66. Studying and Projecting Climate Change with Earth ... [https://www.nature.com/scitable/knowledge/library/studying-and-projecting-climate-change-with-earth-103087065/]
• 67. Assessing terrestrial carbon fluxes and stocks in South ... [https://egusphere.copernicus.org/preprints/2025/egusphere-2025-942/]
• 68. Evolution and prospects of Earth system models [https://www.sciencedirect.com/science/article/pii/S0012825224003143]
• 69. A foundation model for the Earth system - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC12119322/]
• 70. Global Ecosystem and Carbon Monitoring [https://www.nasa.gov/nasa-earth-exchange-nex/gddp/carbon-monitoring-system-cms/]
• 71. Summary of the 2025 GEDI Science Team Meeting [https://science.nasa.gov/science-research/earth-science/summary-of-the-2025-gedi-science-team-meeting/]
• 72. Monthly Update [https://carbon.nasa.gov/files/monthlies/2025_8.html]
• 73. Carbon monitor [https://carbonmonitor.org/]
• 74. KPIs using the SMART framework - Intrafocus [https://www.intrafocus.com/2023/09/kpis-using-the-smart-framework/]
• 75. HOW TO DEVELOP KPIS / PERFORMANCE MEASURES - KPI.org [https://www.kpi.org/kpi-basics/kpi-development/]
• 76. Next-gen KPIs That Will Redefine How You Measure CX [https://www.telusdigital.com/insights/customer-experience/article/next-gen-kpis-cx]
• 77. Data Visualization Toolkit: Data Tables Design Principles [https://dasycenter.org/datavis-toolkit/data-tables/]