Biochar-templated surface precipitation and inner-sphere complexation effectively removes arsenic from acid mine drainage
Bibliographic record
Abstract
Treatment of aqueous leachate from acid mine tailings with pristine biochar (BC) resulted in the removal of more than 90% of the dissolved arsenic with an attendant rapid and sustained pH buffering from 3 to 4. Pine forest waste BC was transformed to a highly effective adsorbent for arsenic remediation of acid mine drainage (AMD) because the dissolved iron induced "activation" of BC through accumulation of highly reactive ferric hydroxide surface sites. Physicochemical properties of the BC surface, and molecular mechanisms of Fe, S, and As phase transfer, were investigated using a multi-method, micro-scale approach (SEM, XRD, FTIR, XANES, EXAFS, and STXM). Co-located carbon and iron analysis with STXM indicated preferential iron neo-precipitates at carboxylic BC surface sites. Iron and arsenic X-ray spectroscopy showed an initial precipitation of ferrihydrite on BC, with concurrent adsorption/coprecipitation of arsenate. The molecular mechanism of arsenic removal involved bidentate, binuclear inner-sphere complexation of arsenate at the surfaces of pioneering ferric precipitates. Nucleation and crystal growth of ferrihydrite and goethite were observed after 1 h of reaction. The high sulfate activity in AMD promoted schwertmannite precipitation beginning at 6 h of reaction. At reaction times beyond 6 h, goethite and schwertmannite accumulated at the expense of ferrihydrite. Results indicate that the highly functionalized surface of BC acts as a scaffolding for the precipitation and activation of positively charged ferric hydroxy(sulf)oxide surface sites from iron-rich AMD, which then complex oxyanion arsenate, effectively removing it from porewaters. Graphical abstract.
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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".