The kinetics of aqueous thiolated arsenic oxidation
Bibliographic record
Abstract
Thiolated arsenic (As) compounds have been identified in various natural and engineered environments worldwide and are important for the biogeochemical cycling of As, yet quantitative data regarding their stability and transformation rates remains scarce. This study investigates the oxidation kinetics of mono-, di-, and tri-thioarsenate at varying pH, Fe, and (thio-)As concentrations in the aqueous phase. Experiments conducted over four weeks revealed that all thioarsenates were oxidized faster at lower pH, with rates of up to several μmoles/L/d at a pH of 3. Trithioarsenate demonstrated approximately two orders-of-magnitude faster oxidation rates than di- and monothioarsenate and these rates exhibited a higher sensitivity to pH and dissolved As and Fe concentrations. The presence of Fe enhanced the oxidation rates of trithioarsenate but had less impact on di- and monothioarsenate. Kinetic data were subsequently used to parameterize oxidation rate equations and determine reaction orders, and to calibrate a kinetic model that was leveraged to determine rate constants. The fundamental insights and kinetic parameters derived for thio-As oxidation in this study are important for predicting the mobility of thio-As compounds and for assessing the potential environmental impacts of As across ambient aquatic systems.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".