Detoxification of Arsenic-Containing Copper Smelting Dust by Electrochemical Advanced Oxidation Technology
Bibliographic record
Abstract
A large amount of arsenic-containing solid waste is produced in the metallurgical process of heavy nonferrous metals (copper, lead, and zinc). The landfill disposal of these arsenic-containing solid waste will cause serious environmental problems and endanger people’s health. An electrochemical advanced oxidation experiment was carried out with the cathode modified by adding carbon black and polytetrafluoroethylene (PTFE) emulsion. The removal rate of arsenic using advanced electrochemical oxidation with the modified cathode in 75 g/L NaOH at 25 °C for 90 min reached 98.4%, which was significantly higher than 80.69% of the alkaline leaching arsenic removal process. The use of electrochemical advanced oxidation technology can efficiently deal with the problem of arsenic-containing toxic solid waste, considered as a cleaner and efficient method.
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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".