Development of Porous Titania Structure with Improved Photocatalytic Activity: Response Surface Modeling and Multi-Objective Optimization
Bibliographic record
Abstract
Porous titania was successfully synthesized by an ultrasound-assisted sol-gel route. The synthesis process was empirically modeled and optimized using the response surface methodology (RSM). Input variables adopted for optimization dealt with the weight ratio of precursors (r) and the sonication time (t), representing the used factors in the synthesis procedure. With regard to application, the synthesized TiO2 samples were tested for the photodegradation of two water-soluble organic pollutants under UV–Vis irradiation. Optimal conditions for the efficient pollutants’ photodegradation were found to involve a precursors ratio of 3 and a sonication time of 60 min. Thus, the M5 sample prepared under the founded optimal conditions yielded the maximal removal efficiencies of 98.4% and 46.3% for the photodegradation of CR dye and 2,4-D herbicide, respectively. In addition, the photodegradation kinetics revealed the pseudo first-order rate constants, showing the photodegradation of CR (k1 = 8.86 × 10−2 min−1) by M5 sample is about 1.3-fold faster than the photodegradation of 2,4-D pesticide (k2 = 6.84 × 10−2 min−1).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".