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REVIEW 3 major objections 7 minor 256 references

A generalised microbial cell model for methane biosignature predictions

T0 review · 3 major / 7 minor · reviewed 2026-07-08 · glm-5.2

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Pith's one-line read Smaller, longer-lived alien microbes make stronger methane signals

desk verdict Solid methodological extension with an honest but load-bearing evolutionary argument read the letter →

arxiv 2607.06366 v1 pith:KXUP6J6W submitted 2026-07-07 astro-ph.EP

Arwen E. Nicholson , Nathan J. Mayne This is my paper · ORCID
classification astro-ph.EP
keywords exoplanetbiosignaturesmethanemethanogenesismicrobialcellmodeldiffusion-limiteduptakechemosyntheticlifeastrobiologybiosignatureprediction
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that if alien microbial life exists on exoplanets, we can predict the maximum strength of its methane biosignature from just a few cell parameters, because evolution should drive microbes toward traits that also happen to maximize atmospheric methane output. The central object is a generalised microbial cell model in which the rate at which a cell can take up its limiting nutrient (here, hydrogen) is capped by physical diffusion across the cell wall. This diffusion limit creates a feedback: a cell's size, death rate, and biomass synthesis cost determine how low the ocean hydrogen concentration must be held for the population to remain stable. Smaller cells, longer-lived cells, and cells with cheaper biomass synthesis can draw hydrogen down further, and drawing hydrogen down further produces more methane. The authors then invoke the evolutionary argument that competition for the limiting resource will favour exactly these traits — smaller, longer-lived, cheaper-to-build cells outcompete their rivals because they can sustain a population at lower nutrient concentrations. This convergence means the biosignature strength is not a free parameter: it is pushed toward a maximum by natural selection itself. The model is built around a methane-producing microbe (methanogen metabolism: CO2 + 4H2 → CH4 + 2H2O), with cell parameters grounded in laboratory measurements of Methanosarcina barkeri, but the approach is designed to generalise to other nutrient-limited chemosynthetic metabolisms and other planetary contexts. The planetary environment in this study is a simplified early-Earth-like setup (global ocean, N2 atmosphere, fixed volcanic outgassing of H2 and CO2); the authors are explicit that real biosignature predictions for a specific exoplanet would require bespoke planetary modelling.

What carries the argument

Diffusion-limited substrate uptake (Equation 5: F = 4πrDS∞, the Berg-Purcell flux to a spherical cell); Gibbs free energy for methanogenesis (ΔG = ΔG⁰ + RT log Q); H2 allocation ratio between energy generation and biomass synthesis (Equation 13); ocean-atmosphere gas exchange via stagnant boundary layer model; biotic regulation of ocean H2 at a limiting concentration set by the balance of cell birth and death rates.

What would settle it

If a multi-species ecosystem model (including grazers or competing metabolisms) were to show that the biosignature strength is dominated by ecological network structure rather than by the primary producer's cell parameters, then the upper-bound prediction from minimal cell parameters would not hold.

Watch

Extended reading notes

Core claim

When diffusion-limited substrate uptake is included in a microbial cell model, three cell parameters — cell radius, cell death rate, and biomass synthesis energy cost — each control how low the biosphere can draw down oceanic hydrogen, and lower drawdown produces more atmospheric methane. Smaller cells, lower death rates, and lower biomass synthesis costs all lead to lower residual ocean hydrogen and higher atmospheric methane. For biomass synthesis cost specifically, there is a peak: methane output first rises then falls as the cost increases, because the population decline eventually outweighs the increased per-cell methane production. The authors argue that since competition for the-limit

Load-bearing premise

The prediction that alien life will evolve to exploit limiting nutrients down to the minimal possible concentration — driving cells toward smaller size, longer lifespans, and cheaper biomass synthesis — is grounded in Earth-based evolutionary theory but is applied here to a single-species biosphere. In a real multi-species ecosystem, grazing, cross-feeding, and other ecological interactions could break the direct link between cell parameters and biosignature strength.

Editorial extensions

If this is right

  • If the evolutionary argument holds, observers searching for methane biosignatures on exoplanets can use minimum plausible cell size, low cell death rates, and peak biomass-synthesis cost as inputs to calculate an upper bound on methane abundance — narrowing the observational target space.
  • The model can be run in reverse: given a tentative methane detection on an exoplanet, it can compute the minimum volcanic H2 outgassing rate required to sustain that biosignature, yielding testable predictions about the planet's geological activity that can be checked against other atmospheric evidence.
  • The same cell-model architecture can be adapted to other nutrient-limited chemosynthetic metabolisms (e.g., sulphur-based or iron-based metabolisms) by swapping the limiting substrate and the metabolic reaction, extending biosignature predictions beyond methane.
  • The biomass output of the model links to independent biomass-plausibility frameworks, allowing cross-checks on whether the biomass required to produce a candidate biosignature is physically reasonable for the planet.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The model's prediction of maximum biosignature strength depends on single-species dynamics; in a multi-species ecosystem, grazers that recycle methanogen biomass could increase methane output beyond what this model predicts, while cross-feeding networks could divert substrate away from methanogenesis entirely. The upper-bound prediction may therefore be conservative in some ecological regimes and
  • The assumption that evolution drives cells toward minimum size and maximum nutrient exploitation is grounded in Earth biology, but the selective landscape on an exoplanet with different physics (e.g., different gravity affecting sinking rates, different ocean viscosity affecting diffusion) could favour different optima — the evolutionary convergence argument may not transfer cleanly to all planeta
  • The model currently fixes cell shape as spherical and neglects cell motility; both can alter effective diffusion-limited uptake. If alien cells evolve non-spherical morphologies or active transport strategies, the relationship between cell radius and nutrient drawdown could shift, changing the biosignature prediction.
  • The peak in methane output as a function of biomass synthesis cost suggests a natural sensitivity analysis: small errors in estimating the minimal energetic cost of building a cell could place a real biosphere on either side of the peak, making the upper-bound prediction sensitive to a parameter that is itself poorly constrained for alien life.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

3 major / 7 minor

Summary. This manuscript presents a generalised microbial cell model for methane biosignature predictions, extending a previous model (Nicholson et al. 2022) by incorporating diffusion-limited substrate uptake (Berg & Purcell 1977). The model tracks H2, CO2, and CH4 through a 0D atmosphere-ocean system populated by a single-species methanogen biosphere whose metabolic rate is governed by thermodynamics and diffusive flux across the cell wall. The authors systematically vary cell radius, death rate, and biomass synthesis cost, finding that smaller and longer-lived cells draw down ocean H2 further and produce more atmospheric CH4. They then argue from classical resource competition theory (the R* rule) that evolution should drive alien chemosynthetic life toward these traits, enabling upper-bound biosignature predictions from minimal cell parameters.

Significance. The inclusion of diffusion-limited substrate uptake is a well-motivated and meaningful extension over the previous model, where microbes could consume nutrients to zero concentration. The H2 allocation derivation (Eqs. 10–13) is clean and internally consistent. The approach of using evolutionary arguments to constrain the biological parameter space for biosignature predictions is interesting and falsifiable. The code is publicly available on GitHub, which supports reproducibility. The grounding in lab measurements of Methanosarcina barkeri provides a realistic biological anchor. The paper is transparent about its limitations as a baseline model.

major comments (3)
  1. Section 5, Conclusions: The statement 'decreasing cell death rates, cell sizes and cell biomass densities all lead to lower concentrations of H2 in the ocean and also higher abundances of CH4' appears to conflate biomass density (b0, which is not varied in any experiment) with biomass synthesis cost (ΔG_CH2O). For ΔG_CH2O, the relationship is explicitly non-monotonic — Figure 5a shows a peak in atmospheric CH4 at an intermediate synthesis cost, and Section 4 acknowledges this by recommending a parameter-space scan for the peak rather than a directional argument. The conclusion as written contradicts the paper's own handling of this parameter and should be corrected to reflect the non-monotonic behaviour.
  2. Section 4: The R* evolutionary argument is load-bearing for the upper-bound prediction framework. For cell size and death rate, the argument cleanly couples competitive advantage to biosignature maximisation (lower values are both competitively favoured and biosignature-maximising). However, for biomass synthesis cost, the evolutionary argument does not yield an upper bound because CH4 peaks at an intermediate cost (Fig. 5a). The paper handles this by scanning for the peak, but the resulting 'upper bound' is constructed differently for different parameters — by evolutionary argument for two parameters and by brute-force scan for the third. This asymmetry should be made explicit, and the phrase 'maximum biosignature strength' (Section 4, paragraph beginning 'The model discussed in this work...') should be qualified to note that the upper bound for the synthesis-cost dimension is an empiri
  3. Section 4: The upper-bound framing depends on the single-species assumption. The paper acknowledges in Section 5 that multi-species interactions (grazing, cross-feeding) could alter the relationship between cell parameters and biosignature strength, but the upper-bound prediction framework in Section 4 is presented without quantifying how sensitive the bound is to this assumption. A brief discussion of which specific multi-species interactions would break the upper-bound argument (as opposed to merely shifting it) would strengthen the paper's predictive claims. For instance, grazers keeping the competitive dominant below the density needed to draw H2 to its R* would decouple cell parameters from equilibrium ocean H2 — does the paper consider this a qualitative or merely quantitative concern?
minor comments (7)
  1. Table 2 caption: 'biomass density b0 = 3530 mol CH2O/m3' is listed with no sensitivity test values, yet the Conclusions (Section 5) refer to 'cell biomass densities' as a varied parameter. This should be clarified.
  2. Figure 3 caption: 'Marker colour saturation indicates the parameter value for the CH2O synthesis cost' — but Figure 3 varies cell death rate and cell radius, not CH2O synthesis cost. The caption appears to be copied from Figure 4 and is incorrect for Figure 3.
  3. Section 2.2.1, Eq. (6): The notation switches from F (Eq. 5) to F(r) without explicit comment on the relationship. A brief sentence clarifying that F(r0) in Eq. (9) recovers the form of Eq. 5 would help the reader.
  4. Section 3.1: The text refers to 'Figure 3a and 3b' but also mentions 'Figure 3' generically in places. The cross-references to sub-panels could be more precise throughout Section 3.
  5. Appendix A5, Figure A2c: The text states the peak occurs at 'ocean H2 ≈ 3×10^-5 mol/m3 as a function of ocean H2 ≈ 5×10^-5 mol/m3 with methane recycling' — this sentence is grammatically unclear and should be rephrased to clearly state the two peak locations.
  6. Section 2.1.1: The CH4 photolysis simplification (CH4 + 2H2O → CO2 + 4H2) is attributed to Kharecha et al. (2005), but the rate is described only as 'a fixed rate proportional to the quantity of methane.' The actual rate constant (0.001 yr^-1 from Table 1) should be stated in the text for clarity.
  7. The abstract contains formatting artefacts (missing spaces between words), e.g., 'Themajorityofpotentiallyhabitableplanets.' This appears to be a LaTeX compilation issue that should be fixed.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their careful reading and constructive comments. The referee correctly identifies an internal inconsistency in our conclusions regarding biomass synthesis cost, and we will revise the manuscript accordingly. We also agree that the asymmetry in how the upper bound is constructed across parameters should be made explicit, and we will add discussion of which multi-species interactions would qualitatively break the upper-bound framework. All three major comments are addressed below.

read point-by-point responses

  1. Referee: Section 5, Conclusions: The statement 'decreasing cell death rates, cell sizes and cell biomass densities all lead to lower concentrations of H2 in the ocean and also higher abundances of CH4' appears to conflate biomass density (b0, which is not varied in any experiment) with biomass synthesis cost (ΔG_CH2O). For ΔG_CH2O, the relationship is explicitly non-monotonic — Figure 5a shows a peak in atmospheric CH4 at an intermediate synthesis cost, and Section 4 acknowledges this by recommending a parameter-space scan for the peak rather than a directional argument. The conclusion as written contradicts the paper's own handling of this parameter and should be corrected to reflect the non-monotonic behaviour.

    Authors: The referee is correct on both counts. First, the phrase 'cell biomass densities' in the conclusion is a misnomer: the parameter we vary is the energetic cost of biomass synthesis (ΔG_CH2O), not the biomass density (b0), which is held fixed throughout all experiments. Second, even replacing 'biomass densities' with 'biomass synthesis costs' would not fix the statement, because the relationship between ΔG_CH2O and atmospheric CH4 is non-monotonic, as shown in Figure 5a and discussed in Section 3.2. The conclusion as written contradicts our own analysis. We will revise Section 5 to state that decreasing cell death rates and cell sizes each lead to lower ocean H2 and higher atmospheric CH4, while the effect of biomass synthesis cost is non-monotonic and must be scanned for a peak. We will also correct the terminology to refer to 'biomass synthesis cost' rather than 'biomass density' throughout the conclusions. revision: yes

  2. Referee: Section 4: The R* evolutionary argument is load-bearing for the upper-bound prediction framework. For cell size and death rate, the argument cleanly couples competitive advantage to biosignature maximisation (lower values are both competitively favoured and biosignature-maximising). However, for biomass synthesis cost, the evolutionary argument does not yield an upper bound because CH4 peaks at an intermediate cost (Fig. 5a). The paper handles this by scanning for the peak, but the resulting 'upper bound' is constructed differently for different parameters — by evolutionary argument for two parameters and by brute-force scan for the third. This asymmetry should be made explicit, and the phrase 'maximum biosignature strength' (Section 4, paragraph beginning 'The model discussed in this work...') should be qualified to note that the upper bound for the synthesis-cost dimension is an empiri

    Authors: We agree that the asymmetry in how the upper bound is constructed across the three parameters is not currently made explicit, and it should be. For cell size and death rate, the R* evolutionary argument and biosignature maximisation are aligned: competitively favoured trait values (smaller cells, lower death rates) also maximise CH4, so the evolutionary argument directly yields the upper bound. For biomass synthesis cost, the R* argument still applies — lower synthesis costs are competitively favoured because they require less H2 per unit biomass and thus allow cells to draw H2 to lower R* values — but competitive dominance at low synthesis cost does not coincide with peak CH4 output, because the biosignature depends on the balance between per-cell CH4 production and total population, which is non-monotonic. We therefore scan the parameter space for the CH4 peak. We will add a paragraph in Section 4 making this asymmetry explicit: the upper bound for cell size and death rate is set by evolutionary argument combined with physical/biological lower limits, while the upper bound for synthesis cost is set by an empirical scan of the model output. We will also qualify 'maximum biosignature strength' to clarify that the synthesis-cost dimension is scanned rather than evolutionarily constrained. revision: yes

  3. Referee: Section 4: The upper-bound framing depends on the single-species assumption. The paper acknowledges in Section 5 that multi-species interactions (grazing, cross-feeding) could alter the relationship between cell parameters and biosignature strength, but the upper-bound prediction framework in Section 4 is presented without quantifying how sensitive the bound is to this assumption. A brief discussion of which specific multi-species interactions would break the upper-bound argument (as opposed to merely shifting it) would strengthen the paper's predictive claims. For instance, grazers keeping the competitive dominant below the density needed to draw H2 to its R* would decouple cell parameters from equilibrium ocean H2 — does the paper consider this a qualitative or merely quantitative concern?

    Authors: This is a well-taken point. We agree that the sensitivity of the upper bound to the single-species assumption deserves more discussion than it currently receives. We will add a paragraph in Section 4 (and expand the relevant discussion in Section 5) addressing which multi-species interactions would qualitatively break the upper-bound argument versus merely shifting it quantitatively. Specifically: (1) Grazing that keeps the competitive dominant below the density needed to draw H2 to its R* would qualitatively break the link between cell parameters and equilibrium ocean H2, because the equilibrium concentration would be set by the grazer-prey balance rather than by the methanogen's R* alone. This is the most serious concern for the framework. (2) Cross-feeding or secondary consumers that recycle methanogen biomass into additional CH4 would shift the upper bound upward but would not break the qualitative relationship between cell parameters and biosignature strength — it would change the proportionality constant. (3) Competition from a different metabolism (e.g., sulphate reducers consuming H2) would reduce the CH4 biosignature but would not invalidate the upper bound for a methanogen-only biosphere; rather, it would mean the upper bound applies to a narrower regime of planetary conditions. We will clarify that the upper-bound framework is specifically for a single-species chemosynthetic biosphere and that grazing is the interaction most likely to qualitatively break it, while other interactions are more likely to shift the bound quantitatively. We cannot fully quantify this sensitivity within the current model, as it would require a multi-species extension, which we flag as future work. revision: partial

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity; one minor self-citation for the planetary model setup that is not load-bearing for the paper's core biological findings.

full rationale

The paper's core derivation chain is self-contained. The diffusion-limited uptake rate (Eq. 5) is derived from first principles following Berg & Purcell (1977), an independent external citation. The metabolic energy calculations use standard thermodynamics (Gibbs free energy, Eq. 2). The biological parameters are sourced from independent lab measurements of Methanosarcina barkeri (Lynch et al. 2019; Servais et al. 1985). The planetary model setup (Section 2.1) does cite Nicholson et al. (2022) for the abiotic environment and atmosphere/ocean parameters, but this is a framework citation—the abiotic model is an input to the biological analysis, not a claim being derived. The paper's central results (Section 3: how changing cell parameters affects CH4 output) emerge from the model equations, not from the prior paper's conclusions. The evolutionary R* argument in Section 4 is an external theoretical framework (Tilman's resource competition theory) applied to the model outputs, not a self-citation. The self-citation to Nicholson et al. (2022) is minor and not load-bearing for the novel contribution (the diffusion-limited uptake model and its impact on biosignatures). No step in the derivation chain reduces to its own inputs by construction. The paper is honest about its assumptions and limitations (Section 5). Score of 2 reflects the minor self-citation for the planetary setup, which does not undermine the independence of the central biological findings.

Assumptions & free parameters 12 free parameters · 7 assumptions · 0 invented entities

The model introduces no new physical entities, particles, or forces. All parameters are grounded in Earth-based measurements or standard physical chemistry. The 'generalised microbial cell' is a modeling construct, not a new entity. The free parameters are biological and planetary quantities sourced from literature or chosen for model convenience. The axioms are domain assumptions about alien life (thermodynamics, diffusion limitation, evolution) and simplifications for the 0D model (spherical cells, fixed weathering, simplified photolysis).

free parameters (12)
  • Cell radius r0 = 1e-6 m
    Default from Methanosarcina barkeri measurements; varied in sensitivity tests from 0.1r0 to 1.5r0
  • Cell death rate d0 = 0.02 h^-1
    Default from aquatic microbe mortality data (Servais et al. 1985); varied from 0 to 0.1 h^-1
  • Biomass synthesis cost (delta G_CH2O) = 97.5 kJ/mol CH2O
    Derived from Lynch et al. 2019 ATP cost data; varied from 0.5x to 20x default
  • Biomass density b0 = 3530 mol CH2O/m^3
    Calculated from dry cell mass of Methanosarcina barkeri assuming spherical cell of radius 1 micron
  • H2 outgassing rate = 1e13 mol/yr
    Fixed abiotic influx chosen to ensure habitable surface; not fitted to data but chosen for model convenience
  • CO2 outgassing rate = 1e15 mol/yr
    Fixed abiotic influx chosen to ensure habitable surface
  • CO2 removal rate = 0.001 * T(CO2) per year
    Simplified silicate weathering proxy
  • CH4 breakdown rate = 0.001 * T(CH4) per year
    Simplified photolysis proxy
  • Stagnant film thickness z_film = 40 micrometers
    Standard value from Kharecha et al. 2005, derived from Earth ocean data
  • Atmospheric pressure P_atmo = 1 atm
    Modern Earth value used for simplified early-Earth-like setup
  • Total atmospheric moles n_atmo = 1.73e20 mol
    Modern Earth value
  • Fixed delta G_CH4 scenario value = 30 kJ
    Used as comparison scenario against variable Gibbs free energy
assumptions (7)
  • domain assumption Alien life uses chemical potential gradients and Gibbs free energy for metabolism
    Section 1: 'We assume that alien-life would utilise chemical potential gradients in their environment and obtain energy following the Gibbs free energy equation.'
  • domain assumption Nutrient uptake by alien cells is limited by diffusion across cell membranes
    Section 1: 'We also assume that uptake of nutrients to an alien cell will be limited by diffusion uptake in some way.'
  • domain assumption Competition and evolution (natural selection) will occur in alien biospheres
    Section 4: 'On Earth competition for resources is a crucial driver of adaptation and evolution and most definitions of life include the ability to adapt via natural selection, therefore it is a process we expect to be present in an alien biosphere.'
  • ad hoc to paper Cells are spherical and act as perfect sinks for substrate molecules
    Section 2.2.1: 'We choose here to model a spherical cell to simplify both modelling diffusion into the cell and to enable changing the cell size with a single parameter.' Also assumes S0=0 (perfect sink).
  • ad hoc to paper Methane photolysis can be represented as CH4 + 2H2O -> CO2 + 4H2 at a fixed rate
    Section 2.1.1: 'we simplify it here for our 0-D representation of a planet's atmosphere and assume that methane breaks down according to CH4 + 2H2O -> CO2 + 4H2 following Kharecha et al. (2005).'
  • ad hoc to paper CO2 removal can be represented as a fixed percentage per year
    Section 2.1.1: 'Abiotic CO2 removal from Earth's atmosphere is far more complex than represented here... As we are primarily interested in the biotic component of our system a simplified removal of CO2 is suitable.'
  • domain assumption A single-species biosphere is a useful baseline model
    Section 2.2: 'We model a single-species single-celled chemosynthetic microbial biosphere.' Acknowledged as a simplification in Section 5.

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Cite this review

Pith. "Pith review of A generalised microbial cell model for methane biosignature predictions." pith.science (2026). https://pith.science/paper/KXUP6J6W

@misc{pith2026260706366,
  author       = {Pith},
  title        = {Pith review of: A generalised microbial cell model for methane biosignature predictions},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KXUP6J6W}},
  note         = {Machine review of arXiv:2607.06366}
}
read the original abstract

The majority of potentially habitable planets detected to date are likely quite different to Earth, for example, being larger in radius and mass, differing rotation rates and with host star spectra unlike the Sun. Therefore the first alien life detected will potentially be living in conditions not found on our planet. This necessitates a generalised approach to modelling biology that can be applied to numerous planetary scenarios, built on fundamental knowledge of life on Earth, but not limited by it. Here, we explore a generalised model of a microbial cell, whose metabolic rate is governed by thermodynamics and substrate diffusion across its cell wall. We model a single-species biosphere consisting of methane producing microbes and determine how changing the cell size, cell death rate and biomass synthesis cost influence the biosignature on the planet - in this case methane. We discuss approaches to predicting upper estimates for the biosignature gas abundance and the applicability of the model to other metabolisms. This tool adds to the body of work attempting to grapple with the complexity of potential alien biospheres.

Figures

Figures reproduced from arXiv: 2607.06366 by the authors.

Figure 1
Figure 1. A schematic showing the key abitoic (black dashed boxes) and biotic (white dashed box) processes occurring in the model (reproduced from Nicholson et al. 2022). α(CO2), and α(CH4) can be found in Table A1 and a(H2O) is as￾sumed to be 1 (Kharecha et al. 2005). We will explore two scenarios, one where ∆G is given by Equation 2 and another where we fix ∆G to be a constant value. Our model microbes build their biomass f… view at source ↗
Figure 2
Figure 2. Panels showing the concentration of H2 in the ocean, oceanH2 and the level of methane in the atmosphere atmoCH4 over time. Life is introduced to the system at t = 20,000 years. parameters of cell radius, cell death rate, and the energy cost of biomass synthesis impact the biotic CH4 output for scenarios where cell uptake of H2 is limited by diffusion, as given by Equation 5. In Section 3.1 we discuss the impact of c… view at source ↗
Figure 3
Figure 3. Panels showing data from experiments with different cell death rates, marked with an ×, and different cell radii, marked with an ◦, for experiments where ∆CH4 is given by Equation 2, shown in black, and for experiments with fixed ∆CH4 = 30 kJ, shown in blue. Marker colour saturation indicates the parameter value for the CH2O synthesis cost with a darker marker indicating a higher cost of CH2O synthesis. (a) Total po… view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Panels showing data from experiments with different cell death rates, marked with an ×, and different cell radii, marked with an ◦, for experiments where ∆CH4 is given by Equation 2, shown in black, and for experiments with fixed ∆CH4 = 30 kJ, shown in blue. Marker col…
Figure 5
Figure 5. Figure 5: Panels showing data from experiments with different CH2O synthesis costs for experiments where ∆CH4 is given by Equation 2, shown in black, and for fixed ∆CH4 = 30 kJ, shown in blue. Marker colour saturation indicates the parameter value for the CH2O synthesis cost wit…

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Pith tools

Reviewed July 8, 2026 · model on record in the stance chip above.