Process optimisation of porous carbon preparation from date palm pits and adsorption kinetics of methylene blue
Bibliographic record
Abstract
Abstract The present study focuses on the process optimisation of porous carbon preparation from date seed pits. The response surface methodology (RSM) technique with Box–Behnken method (BBM) was utilised with the response variables being the yield and BET surface area. The influencing parameters selected were activation temperature, impregnation ratio (IR) and activation time. The optimum conditions were identified to be an activation temperature of 500°C, IR of 2 and activation time of 75 min, with the resulting yield and BET surface area being 46% and 838 m2/g. The average pore volume and average pore diameter were 1.07 cm3/g and 1.69 nm, respectively. In order to assess the suitability of the carbon for adsorption of macromolecules, the kinetics of methylene blue (MB) adsorption were assessed by varying the initial concentration and the adsorption temperature. The kinetic parameters were evaluated applying the pseudo‐second‐order kinetic model by minimising the error between the experimental data and the model prediction; it was found to represent the experimental data more aptly.
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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 | 0.001 |
| 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".