Removal of Ni and Zn in contaminated neutral drainage by raw and modified wood ash
Bibliographic record
Abstract
In the present study, wood ash was modified by alkaline fusion, prior to hydrothermal synthesis, for potential application in the treatment of mine drainage impacted water. With this objective, two types of wood ash (both raw and modified) were evaluated for the treatment of Ni and Zn in contaminated neutral drainage (CND). Batch adsorption experiments were initially conducted on synthetic CND, and then on two real CND, sampled on two active mine sites, contaminated by either Ni (3.7 mg/L) or Zn (9.1 mg/L). Leaching of Zn was observed during the kinetic tests for the raw wood ash, whereas its modification suppressed the leaching. The cation exchange capacity acquired by modification of the two samples of wood ash exceeded 300 meq/100 g (which is two to fourfold higher than those of the raw ash), while sorption capacity for Ni and Zn tripled relative to the raw material. The Langmuir model best described the sorption process for all materials, while potential mechanisms of metal removal include adsorption, precipitation and ion exchange, following pseudo second-order kinetics. Results also showed that within 2 h of contact of mine effluents with one modified wood ash, Ni and Zn concentrations decreased below the maximum authorized monthly mean concentration allowed by the Canadian law (0.5 mg/L), whereas the other modified wood ash allowed reaching the regulatory conformity after 2 h for Ni but 7 days for Zn (although 93% removed after 2 h). Nonetheless, the pH was raised (10.9-11.8) above the legally allowed limits (6-9.5). Based on these findings, modified wood ash could be considered as a promising option for the treatment of Ni and Zn in CND, but the pH correction of final effluent might be necessary.
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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) | 0.000 | 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 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".