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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".