Effect of UV Irradiation and TiO2-Photocatalysis on Airborne Bacteria and Viruses: An Overview
Bibliographic record
Abstract
Current COVID-19 pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has put a spotlight on the spread of infectious diseases brought on by pathogenic airborne bacteria and viruses. In parallel with a relentless search for therapeutics and vaccines, considerable effort is being expended to develop ever more powerful technologies to restricting the spread of airborne microorganisms in indoor spaces through the minimization of health- and environment-related risks. In this context, UV-based and photocatalytic oxidation (PCO)-based technologies (i.e., the combined action of ultraviolet (UV) light and photocatalytic materials such as titanium dioxide (TiO2)) represent the most widely utilized approaches at present because they are cost-effective and ecofriendly. The virucidal and bactericidal effect relies on the synergy between the inherent ability of UV light to directly inactivate viral particles and bacteria through nucleic acid and protein damages, and the production of oxidative radicals generated through the irradiation of the TiO2 surface. In this literature survey, we draw attention to the most effective UV radiations and TiO2-based PCO technologies available and their underlying mechanisms of action on both bacteria and viral particles. Since the fine tuning of different parameters, namely the UV wavelength, the photocatalyst composition, and the UV dose (viz, the product of UV light intensity and the irradiation time), is required for the inactivation of microorganisms, we wrap up this review coming up with the most effective combination of them. Now more than ever, UV- and TiO2-based disinfection technologies may represent a valuable tool to mitigate the spread of airborne pathogens.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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".