Kinetics and mechanisms of arsenic release from amorphous and crystalline arsenic trisulfide
Bibliographic record
Abstract
Preventing arsenic release from mine waste materials, i.e., source control, is a preferable option for controlling arsenic discharge to the environment. Designing effective source control strategies requires comprehensive knowledge on the leaching behavior of arsenic from its bearing minerals. To determine the kinetics and mechanisms of arsenic release, we carried out reactor leaching experiments using arsenic trisulfide (As₂S₃) as a model arsenic sulfide mineral. The experimental results show that the arsenic release increased with pH, the dissolved oxygen concentration, and temperature. The speciation analysis indicates that arsenic was present in solution in the form of arsenite (III) and arsenate (V) and that thiosulfate and sulfate were the main soluble sulfur species. A two-step process that involves a series of primary and secondary reactions was proposed to explain the release of different arsenic and sulfur species. The release rates of arsenic and sulfur from crystalline orpiment were always slower than those from amorphous As₂S₃. Kinetic equations were derived from the leaching data to describe the release rate as a function of the leaching parameters for both amorphous As₂S₃and crystalline orpiment. The magnitudes of the reaction orders and the activation energy indicate that the surface chemical reaction is limiting the rate of arsenic release from amorphous As₂S₃. In contrast, both kinetic modelling and the solid surface characterization support that a mixed-control mechanism determines the arsenic release from crystalline orpiment. Namely, the process is controlled by the surface chemical reaction and the diffusion of dissolved oxygen through a product layer on the solid surfaces. The solid surface characterization shows that this product layer is most likely to be an arsenic-deficient phase enriched in elemental sulfur.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".