Kinetic study and Box–Behnken design approach to optimize the sorption process of toxic azo dye onto organo-modified bentonite
Bibliographic record
Abstract
Kinetic study was applied for sodium bentonite (Na-B) and hexadecylpyridinium bentonite (HDP-B) under different amounts, namely 50% (50HDP-B), 100% (100HDP-B), and 200% (200HDP-B) with respect to cation exchange capacity (CEC). Pseudo first-order and pseudo second-order kinetic models were performed to optimize the sorption of Congo red (CR) dye from aqueous solution. The experimental data fit the pseudo second order kinetic model well. The sorption capacity (qe) of CR dye by the organo-bentonites at equilibrium was 36.0 mg g−1 (72.1%) for 50HDP-B, 48.05 mg g−1 (96.1%) for 100HDP-B, and 49.2 mg g−1 (98.4%) for 200HDP-B. These results were considerably higher than that found by Na-B. Response surface methodology with three-variable, three-level Box–Behnken design was applied for 100HDP-B to describe the removal of CR dye. The effects of three variables, namely temperature, adsorbent dosage, and initial dye concentration, were studied. Predicted values of adsorption efficiency were found to be in good agreement with the obtained experimental values (R2 = 0.97). A second-order polynomial model successfully described the effects of independent variables on the CR dye removal. At the optimized condition, the toxic azo dye could be quantitatively removed from aqueous solution. The results of the present study suggest that the organo-bentonite can be used as an efficient sorbent for dye removal from aqueous solution.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".