Photochemistry of Fe (II) and carbonate-bearing waters and the influence on Greenhouse Gas production in Early Mars
Bibliographic record
Abstract
Aqueous iron UV photooxidation, a proposed pathway for early Martian iron deposits produced in surface environments, produces hydrogen gas, a potentially important greenhouse gas for early Mars. This experimental study focused on iron photooxidation in the presence of dissolved inorganic carbon (DIC) at a range of concentrations that represent partial pressures of CO 2 (0.1–1 bar), consistent with estimated atmospheric pressures and compositions on early Mars. We report in-situ sampling of gas headspace to understand the extent to which DIC impacts the production of hydrogen gas that may be produced from aqueous iron photooxidation. The experiments demonstrate that ferrous iron photooxidation does not occur at any of the tested DIC concentrations (3.4–35 mM). Ferrous iron carbonate minerals were precipitated at pH conditions above 6, while maintaining relatively high dissolved iron concentrations at siderite saturation. We observed hydrogen and methane gas production at varying initial conditions, but no clear relationships to either iron loss to oxidation and/or precipitation or DIC concentrations could be inferred. Instead, we suggest that hydrogen is a byproduct of methane photolysis. The methane itself was sourced from an unknown background organic carbon compound from residual organic impurities in the deionized water source utilized in the experiments and is unrelated to iron photochemistry. These observations indicate that iron photooxidation is an unlikely mechanism to have produced hydrogen and precipitated iron oxide minerals at near-neutral pH in the presence of 0.1–1 bar CO 2 on early Mars. • Fe (II) photooxidation is minimized in the presence of dissolved inorganic carbon. • Limiting DIC to 3.4–17 mM did not permit photooxidation to occur. • CH 4 and H 2 gas were detected despite lack of solution chemistry changes. • CH 4 from background organic carbon photolyzed and produced H 2 as a byproduct.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".