Photocatalysis for Air Treatment Processes: Current Technologies and Future Applications for the Removal of Organic Pollutants and Viruses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Photocatalysis for air treatment or photocatalytic oxidation (PCO) is a relatively new technology which requires titanium dioxide (TiO2) and a source of light (Visible or near-UV) to degrade pollutants contained in air streams. Present approaches for the photodegradation of indoor pollutants in air streams aim to eliminate volatile organic compounds (VOCs) and viruses, which are both toxic and harmful to human health. Photocatalysis for air treatment is an inexpensive and innovative green process. Additionally, it is a technology with a reduced environmental footprint when compared to other conventional air treatments which demand significant energy, require the disposal of used materials, and release CO2 and other greenhouse gases to the environment. This review discusses the most current and relevant information on photocatalysis for air treatment. This article also provides a critical review of (1) the most commonly used TiO2-based semiconductors, (2) the experimental syntheses and the various photocatalytic organic species degradation conversions, (3) the developed kinetics and computational fluid dynamics (CFD) and (4) the proposed Quantum Yields (QYs) and Photocatalytic Thermodynamic Efficiency Factors (PTEFs). Furthermore, this article contains important information on significant factors affecting the photocatalytic degradation of organic pollutants, such as reactor designs and type of photoreactor irradiation. Overall, this review describes state-of-the-art photocatalysis for air treatment to eliminate harmful indoor organic molecules, reviewing as well the potential applications for the inactivation of SARS-CoV2 (COVID-19) viruses.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 it