Evaluation of Natural Clay Minerals for Heavy Metal Adsorption from Lead-Acid Battery Recycling Waste
Bibliographic record
Abstract
The escalating demand for sustainable energy storage underscores the critical role of lead-acid batteries (LABs), yet their recycling generates hazardous wastes laden with toxic heavy metals.This study evaluates natural kaolinite and bentonite clays as low-cost adsorbents for immobilizing lead (Pb) and arsenic (As) in LAB recycling wastes from South Africa.Mineralogical characterization revealed complex slag compositions dominated by sodium sulfates, lead oxides (litharge), and sulfides (galena).Leaching tests (SPLP: pH 4.2; water leaching: pH neutral) demonstrated significant mobilization of Pb (981 ppm) and As (153 ppm) from slags, with Pb release amplified under neutral conditions.Adsorption experiments showed bentonite reduced Pb by 99.4% (981 → 5.91 ppm) in recycled slag leachate, outperforming kaolinite (95.5% reduction).For As, bentonite achieved partial reduction (153 → 107 ppm), while kaolinite increased As concentrations due to competitive ion effects.Elemental releases (Al, Si, Na) post-treatment indicated clay dissolution, highlighting practical limitations.Results affirm bentonite's superior efficacy for Pb immobilization but stress the need for enhanced As adsorption strategies.These clays offer a viable, locally sourced solution for mitigating LAB waste impacts in developing regions, advancing circular economy goals in battery recycling.
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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".