Titania/gum tragacanth nanohydrogel for methylene blue dye removal from textile wastewater using response surface methodology
Bibliographic record
Abstract
Abstract The aim of this study was to statistically evaluate the capability of a prepared TiO2/gum tragacanth hydrogel as a photocatalyst for the removal of methylene blue dye molecules from contaminated solutions. In this regard, TiO2 nanoparticles were sonicated in gum tragacanth and the final hydrogel was prepared by the addition of glutaraldehyde as a crosslinking agent. Response surface methodology was employed as a mathematical and statistical tool to describe the system by a polynomial equation that relates the removal efficiency to selected variables (time, pH, initial dye concentration and photocatalyst dosage). The significance and adequacy of the model were confirmed by high coefficient of determination (R2) and adjusted R2 values (>93%). The system was optimized at an initial dye concentration of 9.37 mg L−1, pH of 9.02, time of 124.34 min and photocatalyst dosage of 0.13 g L−1 using the response optimizer with an efficiency of 88.86%. A kinetic study of photocatalytic decoloration indicated that the pseudo‐second‐order model was well fitted to the experimental data. © 2018 Society of Chemical Industry
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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 | 0.001 |
| 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".