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Record W3085908276 · doi:10.1111/ina.12741

Loss of NO(g) to painted surfaces and its re‐emission with indoor illumination

2020· article· en· W3085908276 on OpenAlexaff
Stephanie H. Jones, Florian P. R. Hosse, Xiaoying Yang, D. J. Donaldson

Bibliographic record

VenueIndoor Air · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersAlfred P. Sloan Foundation
KeywordsPhotochemistryIncandescent light bulbPhotocatalysisChemistryIndoor airAdsorptionEnvironmental chemistryEnvironmental scienceEnvironmental engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.166
Threshold uncertainty score0.417

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.280
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2020
Admission routes1
Has abstractyes

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