Sorption of Arsenite, Arsenate, and Thioarsenates to Iron Oxides and Iron Sulfides: A Kinetic and Spectroscopic Investigation
Bibliographic record
Abstract
Sorption to iron (Fe) minerals determines the fate of the toxic metalloid arsenic (As) in many subsurface environments. Recently, thiolated As species have been shown to dominate aqueous As speciation under a range of environmentally relevant conditions, thus highlighting the need for a quantitative understanding of their sorption behavior. We conducted batch experiments to measure the time-dependent sorption of two S-substituted arsenate species, mono- and tetrathioarsenate, and compared it to the sorption of arsenite and arsenate, in suspensions containing 2-line ferrihydrite, goethite, mackinawite, or pyrite. All four As species strongly sorbed to ferrihydrite. For the other sorbents, binding of the thiolated As species was generally lower compared to arsenate and arsenite, with the exception of the near instantaneous and complete sorption of monothioarsenate to pyrite. Analysis of the X-ray absorption spectroscopy (XAS) spectra of sorbed complexes implied that monothioarsenate binds to Fe oxides as a monodentate, inner-sphere complex. In the presence of Fe sulfides, mono- and tetrathioarsenate were both unstable and partially reduced to arsenite. Adsorption of the thiolated As species to the Fe sulfide minerals also caused the substitution of surface sulfur (S) atoms by As and the formation of As-Fe bonds.
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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".