Use of a natural mineral for the removal of copper and nickel from aqueous solutions to reduce heavy metal content of precipitation sludges
Bibliographic record
Abstract
A major disadvantage of precipitation removal of heavy metals is the disposal of sludge having a high concentration of heavy metals, sometimes requiring disposal in hazardous waste landfills. A sorption based polishing step using natural sorbents; following precipitation may be feasible to avoid costly disposal of hazardous sludge. This study investigates the applicability of the use of a natural zeolite, namely clinoptilolite, obtained from a Western Anatolian deposit, in removing copper and nickel from aqueous solutions. Maximum achievable capacities were found as 0.31 meq Cu2+/g and 0.32 meq Ni2+/g for as-received, and 0.55 meq Cu2+/g and 0.43 meq Ni2+/g for conditioned clinoptilolite samples, respectively. Use of clinoptilolite for the removal of Cu2+ from precipitation effluents holds more potential for the purpose of meeting discharge standards. Metal removal mechanisms are also investigated via examination of exchangeable cations (Na+, K+, Mg2+, Ca2+) in the aqueous phase.
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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".