Impact of support characteristics and preparation method on photocatalytic activity of TiO <sub>2</sub> /ZSM-5/silica gel composite photocatalyst
Bibliographic record
Abstract
Titanium dioxide (Degussa P25) was supported onto two different aluminosilicate zeolites (ZSM-5) and anchored on three silica gels using two separate preparation methods to study the effect of the catalyst components and the preparation method on the photoactivity of composite catalysts. The photoactivity was investigated by tracking phenol disappearance in a batch UVA light-emitting diode reactor. An easily separable photocatalyst with higher photoactivity than commercial Degussa P25 was developed using Degussa P25, ZSM-5 (SiO 2 /Al 2 O 3 = 280) and silica gel (particle size 0.2–0.5 mm and pore size 40 Å). The optimum composition was found to be P25:ZSM-5:silica gel = 0.3 : 0.5 : 0.5 g l −1 . SEM photographs show that the distribution of the composite catalyst components prepared without a binder was better than that prepared with a binder. The efficiency of photocatalytic ozonation of sulfamethoxazole (SMX) using the new photocatalyst was assessed and compared to that of commercially available Degussa P25. It was found that photocatalytic ozonation promoted the SMX disappearance and mineralization. PZS was superior to Degussa P25 with respect to photocatalysis and photocatalytic ozonation. The enhancement was attributed to the synergetic effect between adsorption, ozonation and/or photocatalytic oxidation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".