Optimization of porous volcanic ash-based geopolymer for crystal violet adsorption using the Box-Behnken design
Bibliographic record
Abstract
Volcanic ash was used as a precursor for the synthesis of a geopolymer activated by sodium hydroxide and using hydrogen peroxide as a pore-forming agent. Factors controlling geopolymer synthesis such as sodium hydroxide concentration (6–12 mol/L), liquid/solid mass ratio (0.3–0.5), and H2O2 mass concentration (0%–2%) were optimized using the Box-Behnken design method. The chosen process variables were optimized to enhance both the geopolymer's porosity and its effectiveness in removing crystal violet. Sodium hydroxide concentration and H2O2 mass concentration had a significant effect on both responses. Under optimal conditions of 6 mol/L NaOH concentration, a 0.3 liquid/solid ratio, and 2% H2O2 mass concentration, the model-predicted and experimental values for both responses were highly comparable. Additionally, response surface methodology was used to assess the removal of crystal violet from an aqueous solution, employing the geopolymer produced under these optimal conditions as the adsorbent. Experiments were carried out according to the Box-Behnken statistical surface design with four input parameters, namely, contact time (A: 10–120 min), initial crystal violet concentration (B: 20–100 mg/L), adsorbent dose (C: 0.1–0.6 g), and pH (D: 3–9). Regression analysis indicated a strong fit of the experimental data to the second-order polynomial model, with a coefficient of determination ( R2) of 0.9864 and a Fisher's F value of 61.97. Optimization of the parameters A (35.415 mg/L), B (98.184 min), C (0.359 g), and pH (6.950) achieved a maximum crystal violet removal of 98.413% by the geopolymer.
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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".