Removal of heavy metals from aqueous solutions using carbonised banana and orange peels
Bibliographic record
Abstract
This research demonstrates the feasibility and efficacy of removing heavy metals from aqueous solutions using waste banana and orange peels. The fruit peels are carbonised, without the addition of chemical substances, to enhance their adsorption capacities. The adsorption capacities are studied in aquatic solutions containing individual and combined metal ions of hexavalent chromium (Cr 6+ ), copper (Cu) and zinc (Zn). The selectivity of the adsorbents towards these metal ions is also exhibited. The results show that both fruit peels exhibit a better selectivity towards zinc ions, followed by copper and then hexavalent chromium. The effects of operational conditions, including pH, adsorbent dosage, contact time and concentration of metal ions, on the removal efficiency and uptake capacity of the carbonised fruit peels are investigated. The optimal adsorption for both adsorbents occurs within 30 min of exposure and at an adsorbent dose of 0.5 g. Additionally, the adsorption kinetics and isotherms, including pseudo-second-order, Langmuir and Freundlich, are modelled for the obtained data.
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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".