Insights into the environmental benefits of using apple pomace for biosorption of lead from contaminated water
Bibliographic record
Abstract
<h2>Abstract</h2> The apple processing industry generates large quantities of organic waste, presenting a major source of organic contamination. Consequently, finding an effective solution for valorizing this waste has become a pressing issue. This study aims to address two key concerns: (i) solving an agricultural problem by efficiently using agri-food residue, and (ii) removing lead, an extremely toxic element, from contaminated waters to mitigate environmental pollution. Two biosorbents were tested: raw apple waste (RA), obtained from a mixture of apple varieties, and the same material after extracting valuable bioactive and reusable components, extracted apple (EA). The study evaluated the influence of pH, initial biosorbent mass, adsorption kinetics, and equilibrium isotherms. The results are very promising, showing a lead removal efficiency of 82 % for RA and 100 % for EA at a low initial concentration of the solution of 20 mg Pb<sup>2</sup>⁺/L and an optimal pH of 5 ± 0.5. The Langmuir model predicted a maximum adsorption capacity of 44.6 mg/g for RA and 48.6 mg/g for EA. These findings demonstrate that apple waste, even after selective extraction of valuable bioactive components, can be effectively used for environmental remediation on a practical scale.
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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) | 0.001 | 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 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".