Cocoa Shells as Adsorbent for Metal Recovery from Acid Effluent
Bibliographic record
Abstract
Abstract Cocoa shells are commonly used in the horticulture field. This inexpensive substrate was studied for metal removal from acidic effluents. Batch adsorption tests in shake flasks revealed that cocoa shells were particularly efficient for lead removal. More than 90% of lead could be removed from a mono-metallic solution containing 51.8 mg Pb/L (250 µM Pb) using 20 g/L of cocoa shells. Langmuir isotherm indicated that cocoa shells have a maximum lead uptake of 7.56 mg/g (36.5 µmol/g) at pH = 2.0. Adsorption tests were also successfully completed with three types of heavily contaminated acid effluents: a multi-element synthetic solution and effluents produced during sewage sludge and soil decontamination. These tests have shown that the presence of other metals and organic matter only slightly decreases the lead removal by cocoa shells. After adsorption, metals could be eluted using a diluted acid solution (0.5 N) and the cocoa shells could be reused for many adsorption/desorption cycles.
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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 | 0.004 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.077 | 0.013 |
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; both teacher heads agree on what is shown here.
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".