Reviewing MnO<sub>x</sub>-based catalysts for decomposition of indoor ozone
Bibliographic record
Abstract
Abstract Ozone is a highly reactive gas and one of important air pollutants for both indoor and outdoor environments. The Occupational Safety and Health Administration (OSHA) guideline for the ozone level limit at workplaces is 100 ppb for 8-hour exposure and the Health Canada guideline for the residential buildings is 20 ppb for 8 hour exposure. Therefore, applying an ozone removal technology in indoor environments is crucial when outdoor ozone concentration is high and/or where strong ozone emission sources exist. Activated carbon-based filters, thermal oxidation, catalytic oxidation, and photocatalytic oxidation are air treatment technologies that have been applied for ozone removal. Among these technologies, the catalytic oxidation approach showed better results, particularly manganese oxide (MnOx) based catalysts, which can decompose ozone to oxygen at room temperature. The low cost as well as high catalytic activity are among the advantages of MnOx-based catalysts. High specific surface area, high density of oxygen vacancy, high reducibility, low average oxidation state, and low relative humidity are beneficial for ozone decomposition over the catalyst. This review presents the importance of ozone removal from the indoor environments, its exposure issues, and the recent studies on MnOx-based catalyst for ozone decomposition.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".