A review of the influence of humidity on photocatalytic decomposition of gaseous pollutants on TiO<sub>2</sub>‐based catalysts
Bibliographic record
Abstract
Abstract Humidity can have an appreciable influence on the photocatalytic degradation of volatile organic compounds (VOCs) in air. It has been proposed that there are multiple layers of water molecules on the surface of the photocatalyst, and a VOC molecule must penetrate this layer and be able to diffuse to the photocatalytic surface for a reaction to occur. An updated analysis of related literature from the past 12 years indicates that this proposed mechanism remains reasonable for explaining observed effects. According to this mechanism the VOC's hydrophilicity should dominate the influence of humidity on the photocatalytic reaction kinetics, and this effect can be seen in a categorization of literature observations based on the octanol‐water partition coefficient. The frequently cited Langmuir‐Hinshelwood kinetic model does not directly account for humidity effects in this way but can be adapted for this purpose. As an example, the rate of photocatalytic decomposition of chlorobenzene in air under humid conditions can be described by a modified Langmuir‐Hinshelwood kinetic model, where the reaction rate coefficient is dependent on the water concentration. Understanding and considering the effect of humidity is useful for optimizing the efficacy of photocatalytic VOC treatment of air.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".