A review of prospects and challenges of photocatalytic decomposition of volatile organic compounds (<scp>VOCs</scp>) under humid environment
Bibliographic record
Abstract
Abstract Volatile organic compounds (VOCs) are harmful for humans and the surrounding ecosystem. Emissions from these pollutants have caused a significant reduction in air quality, which has an effect on people's health. Alkanes, alkenes, alcohols, aromatics, and other VOC pollutants have all been broken down by TiO 2 photocatalytic processes. Due to several operating inefficiencies and deactivation issues in humid environments, the practical application of photocatalysis has not been realized on a broader scale. The effectiveness of photo‐oxidation of VOCs is impacted by a variety of environmental conditions. In the photocatalytic oxidation of the VOCs, relative humidity (RH) is critical. Therefore, it is important to review the recent findings on how humidity affects the photocatalytic breakdown of VOCs in air. To satisfy this need, this work provides a critical review of the related literature with focus on the fundamentals of photocatalysis, photocatalytic degradation of air pollutants, and the influence of humidity on the photocatalytic process degradation for selected air pollutants. It also highlights the kinetic models and typical photocatalytic reactor and supports for VOC removal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".