The Deep Removal of Mercury in Contaminated Acid by Colloidal Agglomeration Materials M201
Bibliographic record
Abstract
The high-temperature roasting/smelting process of copper and zinc concentrates will cause the mercury in the concentrate to evaporate into the flue gas, and most of the mercury in the flue gas will eventually enter the waste acid in its ionic form. A highly efficient mercury removal agent M201 with long carbon chains and loaded active functional groups can adsorb and disperse fine particles for mercury removal in the system. Through bridging, the linear structure is woven into a network to achieve large-scale capture and dispersion of fine particles and colloidal substances. The recommended operating conditions for developing mercury deep purification technology are as follows: M201 reagent concentration of 50 g/L, 6 mL/L added acid solution, room temperature, mixing time of 5 min, air flotation time of 10 min, ventilation rate of 0.1 L/min, H2SO4 concentration of 33.67 g/L, and the residual mercury content of 2 mg/L (the mercury content reaches 0.01 mg/L after two-stage mercury removal treatment). Meanwhile, the residual arsenic content is 21.9 mg/L. This study shows a better separation of arsenic and mercury and achieves one-step mercury removal.
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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".