Synthesis, Characterization, and Comparison of Sol–Gel TiO <sub>2</sub> Immobilized Photocatalysts
Bibliographic record
Abstract
Abstract This study was focused on the synthesis of titania-based photocatalytic coatings with high photocatalytic activity, attrition resistance, and stability. Five different photocatalytic coatings were synthesized using the sol–gel technique. Three coatings were prepared using aqueous sols of TiO 2 nanoparticles with different amounts of titanium tetraisopropoxide and different quantities and types of acids. The other two photocatalysts were composite sol–gel coatings which were prepared by incorporating commercial Degussa P25 into the TiO 2 synthesized through sol–gel technique. The physical and optical properties of the immobilized photocatalysts were characterized with UV–vis spectroscopy, X-ray diffraction, scanning electron microscopy, and light scattering. The photocatalytic activity of each coating was determined using a lab-scale differential photoreactor by measuring the degradation rate of a model micropollutant, the herbicide 2,4-dichlorophenoxyacetic acid (2,4-D). The conversions of 2,4-D obtained with the TiO 2 coatings without Degussa P25 were in the order of 7–23%, whereas the two composite coatings provided conversions in the range of 66–69%. In addition, one of the composite coatings showed a more homogeneous morphology and less cracking, and for this reason, it was more durable and showed lower attrition.
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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) | 0.000 | 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".