Optimal removal of diclofenac and amoxicillin by activated carbon prepared from coconut shell through response surface methodology
Bibliographic record
Abstract
Agricultural residues like coconut shell are widely available in Benin Republic and can be used as adsorbent. In this study, we determine the limits of pharmaceutical substances adsorption by activated carbon from coconut shells. The synthesis of this adsorbent was optimized by the Response Surface Methodology (RSM) with two factors: the impregnation ratio IR and activation temperature. The iodine value was considered as the performance (response) parameter of this synthesis. After characterizing the prepared activated carbon, adsorption tests were performed on diclofenac sodium (DCF) and amoxicillin (AMX) by varying the contact time and the adsorbate-adsorbent ratio Rads. The synthesis results showed that the optimal physicochemical properties of the activated carbon were observed at 740 °C with phosphoric acid (IR = 1.66). Under these optimal conditions, the activated carbon from the coconut shells presented a large microporous specific surface (SBET = 437 m2/g and Vmicro = 0.21 cm3/g), optimal iodine adsorption (930.28 mg/g), amorphous and low heterogeneous chemical composition. In addition, the prepared activated carbon was an excellent adsorbent for the removal of the pharmaceutical substances studied. The experimental adsorption data followed the Langmuir isotherm and the pseudo first-order kinetic model. However, the efficiency varied depending on the nature of the adsorbate and the adsorbate-adsorbent ratio was the main limiting factor in the adsorption process. Optimal elimination greater than 98% was noticed with Rads = 0.10 and a contact time of 15 min (90 min) for DCF (AMX). However, we noticed the complete elimination of AMX (DCF) for Rads ≤ 0.075 (Rads ≤ 0.040). It was observed that the removal efficiency of pollutant was not defined by the adsorption rate constant but the reactivity with the adsorbent.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".