Hydrosulfide Oxidation Pathways in Oxic Solutions Containing Iron(III) Chelates
Bibliographic record
Abstract
The role of dissolved oxygen (DO2) on the oxidation of hydrosulfide ions (HS-; C(HS-)0 = 50-150 micromol/L) into polysulfides (S(n)2-; n = 2-9), colloidal sulfur, and oxysulfur species with iron(III) trans-1,2-diaminocyclohexanetetraacetate (iron(III)-cdta; C(Fe(III)0 = 50-300 micromol/L) complexes in alkaline solutions (pH 9-10.2) was investigated at 25 +/- 1 degree C. At higher pH, oxygen was seen to slow down the hydrosulfide conversion rate. For instance, the HS- half-life was 24.8 min in a DO2-saturated iron(III)-cdta solution compared to 11.3 min in the corresponding anoxic solution (pH 10.2, C(HS-)0 = 80 micromol/L, C(Fe(III))0 = 200 micromol/L). In anoxia, HS- oligomerizes into chain-like polysulfides which behave as autocatalysts on the HS- conversion rates. The presence of DO2 disrupts the HS- oligomerization process by generating thiosulfate precursors from polysulfides, a pathway that impedes the HS- uptake. At lower alkaline pH where the hydroxide-free Fe(3+)cdta(4-) is the prevailing iron(III)-cdta species, the "iron(II)-cdta + DO2" oxidative reaction becomes crucial. Oxidative regeneration of iron(III) as Fe(3+)cdta(4-) (being more reactive than Fe(3+)OH(-)cdta(4-)) offsets to some extent the restrictive role of oxygen on the accumulation of polysulfides. Thiosulfate and sulfate were the main end-products for the current experimental conditions to the detriment of colloidal sulfur, which did not form in DO2-saturated solutions.
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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".