Loss of NO(g) to painted surfaces and its re‐emission with indoor illumination
Bibliographic record
Abstract
Heterogeneous surface reactions play a key role in the chemistry of the indoor environment because of the large indoor surface-to-volume ratio. The presence of photocatalytic material in indoor paints may allow photochemical reactions to occur at wavelengths of light that are present indoors. One such potential reaction is the heterogeneous photooxidation of NO to HONO. NO(g) is commonly found indoors, originating from combustion sources, ventilation and infiltration of outdoor air. We studied the interaction of NO(g) with painted surfaces illuminated with indoor fluorescent and incandescent lighting. There is a loss of NO(g) to painted surfaces in the dark at both 0 and 50% RH. At 50% RH, there is a re-release of some of that NO(g) under illumination. The same behavior is observed for illumination of different colored paints. This is in contrast to what is seen with TiO2 as the substrate, where photoenhanced uptake of NO(g) and formation of NO2(g) are observed. We hypothesize that the loss of NO(g) is due to adsorption and diffusion into the paint. The re-release of NO under illumination is thought to be due to photooxidation of NO to HONO on the painted surface at higher relative humidities and subsequent HONO photolysis.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".