Adsorptive removal of recalcitrant organic compounds of compost leachate by epichlorohydrin cross‐linked cyclodextrin copolymer
Bibliographic record
Abstract
Abstract Hazardous materials in compost leachate pose a threat to the environment, and its treatment has become a concern in recent years. The adsorption process is a highly effective method that is used for treating these contaminants. This research pioneered the application of cyclodextrin‐epichlorohydrin (ECP) copolymer for chemical oxygen demand (COD) removal in compost leachate treatment. It offers a practical and effective approach to addressing environmental concerns associated with COD, with an extremely high adsorption capacity of 11,246 mg/g. Fourier transform infrared (FTIR), x‐ray diffraction (XRD), scanning electron microscopy (SEM), and energy dispersive x‐ray (EDX) analyses were performed to identify the synthesized ECP. The effects of different parameters on the adsorption process were systematically investigated. Organic compounds removal was also evaluated using response surface methodology (RSM) in relation to process parameters. The maximum removal of COD was achieved in acidic solutions with an agitation speed of 160 rpm and adsorbent dosage of 3 g/L at 60 min. The corresponding maximum removal percentage achieved under the optimum conditions was about 70%. The adsorption process followed pseudo−second order kinetics and Freundlich isotherms models. The adsorption behaviour of ECP was investigated in ionic environment and it was efficiently regenerated for three adsorption–desorption cycles. The ECP showed high adsorption capacity and was found to be cost‐effective for COD removal. It was found to be a sustainable waste management strategy.
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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".