An Overview of Recent Developments in Improving the Photocatalytic Activity of TiO2-Based Materials for the Treatment of Indoor Air and Bacterial Inactivation
Bibliographic record
Abstract
Indoor air quality has become a significant public health concern. The low cost and high efficiency of photocatalytic technology make it a natural choice for achieving deep air purification. Photocatalysis procedures have been widely investigated for environmental remediation, particularly for air treatment. Several semiconductors, such as TiO2, have been used for photocatalytic purposes as catalysts, and they have earned a lot of interest in the last few years owing to their outstanding features. In this context, this review has collected and discussed recent studies on advances in improving the photocatalytic activity of TiO2-based materials for indoor air treatment and bacterial inactivation. In addition, it has elucidated the properties of some widely used TiO2-based catalysts and their advantages in the photocatalytic process as well as improved photocatalytic activity using doping and heterojunction techniques. Current publications about various combined catalysts have been summarized and reviewed to emphasize the significance of combining catalysts to increase air treatment efficiency. Besides, this paper summarized works that used these catalysts to remove volatile organic compounds (VOCs) and microorganisms. Moreover, the reaction mechanism has been described and summarized based on literature to comprehend further pollutant elimination and microorganism inactivation using photocatalysis. This review concludes with a general opinion and an outlook on potential future research topics, including viral disinfection and other hazardous gases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".