UV- or Visible-Light-Induced Degradation of X3B on TiO<sub>2</sub> Nanoparticles: The Influence of Adsorption
Bibliographic record
Abstract
Photodegradation of a textile dye X3B using either UV (λ ≥ 320 nm) or visible light (λ ≥ 450 nm) over three catalysts of highly adsorptive TiO 2 nanoparticles in water has been examined. All the adsorption isotherms demonstrated the Langmuir type behavior. The common observation was confirmed that for all the reactions induced by UV or visible light, the apparent initial rate of X3B loss in the aqueous phase increased with the initial equilibrated concentration of X3B. However, this correlation was changed when the rate was determined by the decreased concentration both in the aqueous phase and on the catalyst surface. This increase of real initial rate with the initial equilibrated concentration was observed only in the visible-light-induced reaction over TiO 2 of Degussa P25. For all the other reactions, especially under UV irradiation, the real initial rate was found to increase only initially and then decrease with the initial equilibrated concentration. The result suggests that there is a screening effect by the adsorbed dye in the TiO 2 photocatalytic reaction and a solution filter effect in the photosensitized reaction. Moreover, the photosensitized photodegradation of X3B was found to be also dependent on the physical properties of TiO 2, but interestingly the relative activity among the catalysts was similar to that demonstrated in the photocatalytic reaction.
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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) | 0.001 | 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".