Insights into the environmental benefits of using apple pomace for biosorption of lead from contaminated water
Bibliographic record
Abstract
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 2 ⁺/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 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 | 0.001 | 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".