Green Route to Arsenic Detoxification: Mechanochemical Transformation of As<sub>2</sub>O<sub>3</sub> to As(0)
Bibliographic record
Abstract
With the continuous exploitation of arsenic-containing resources and growing concern over arsenic contamination, the disposal of arsenic has become a global challenge. Elemental arsenic, because of its nontoxic nature, is considered a preferred form for arsenic detoxification and mitigation. We introduce an innovative and eco-friendly method to convert the highly toxic arsenic trioxide into nonpoisonous elemental arsenic via a mechanochemical processing at room temperature. Utilizing zinc powder as the reductant and acetic acid as the reaction medium, we achieved a remarkable reduction efficiency of 92.5%, producing elemental arsenic of approximately 99% purity. Thermodynamic analysis revealed the pivotal role of acetic acid in stabilizing the reaction system and eliminating the formation of arsine. Density functional theory calculations further confirmed that the reduction of H 3 AsO 3 on the zinc surface was the dominant reaction in the Zn–CH 3 COOH–As 2 O 3 system. The introduction of mechanical force lowered the relative energy of the reaction, resulting in superior reduction performance under relatively mild conditions. These findings could pave the way for the safe disposal of arsenic-containing waste and offer a sustainable route to reducing the toxicity of arsenic trioxide.
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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) | 0.002 | 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".