Photocatalytic Efficiency of Zeolite‐Based TiO <sub>2</sub> Composites for Reduction of Cu ( <scp>II</scp> ): Kinetic Models
Bibliographic record
Abstract
This study evaluates the photoactivity of novel zeolite‐based T i O 2 composites modified with metal particles for the photoreduction of C u 2+ , depending on the zeolite and metal particles ( A g or F e) content and to establish the photoreduction mechanism. The composites have been prepared by incorporating zeolite particles in the sols during the sol‐gel preparation of the T i O 2 , followed by impregnation of the gels in solutions containing metal ions. The zeolite‐based T i O 2 composites have been characterized using X ‐ray diffraction, Raman and FTIR spectroscopy techniques and TEM microscopy. The role of zeolite particles in the photoreduction process was demonstrated by using different kinetic models. The apparent constant rate for photoreduction increases in direct proportion with the content of zeolite and is higher for the composites modified with F e than for those modified with A g nanoparticles. The results demonstrate that composites have a higher photocatalytic efficiency than T i O 2 . The experiments in this study reveal that the migration of C u 2+ in solution occurs rapidly and that the diffusion through the film on the composite's surface and, subsequently, the photoreduction process are the rate limiting steps. The role of the zeolite in photoreduction is to diminish the recombination rate of the charge carriers.
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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 | 0.000 |
| 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) | 0.000 | 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".