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Record W2296736035 · doi:10.1021/acs.iecr.5b03710

Spontaneous and Light-Driven Conversion of NO<sub><i>x</i></sub> on Oxide-Modified TiO<sub>2</sub> Surfaces

2015· article· en· W2296736035 on OpenAlexaff
Cao‐Thang Dinh, Sjoerd Hoogland, Edward H. Sargent

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversity of Toronto
FundersDuPont
KeywordsRutileNOxUltravioletMaterials scienceChemical engineeringOxidePhase (matter)Layer (electronics)Ultraviolet lightTitanium oxideCoatingChemistryNanotechnologyOptoelectronicsOrganic chemistry

Abstract

fetched live from OpenAlex

Heterogeneous reactions of trace atmospheric gases on solid surfaces play an important role in atmospheric chemistry. In this study, we investigate the reaction of NO 2 with solid surfaces of TiO 2 covered with a thin layer of an alumina–silica mixture over a range of different compositions. The reactions were conducted in the presence of oxygen under dark or ultraviolet light (UV) illumination conditions. The results show that coating TiO 2 with alumina–silica greatly affects the formation of gas-phase products NO and N 2 O both with or without illumination using UV light. A thick shell of alumina–silica enhances the formation of NO in the dark, whereas adding a small amount of alumina on the surface of rutile TiO 2 significantly enhances the conversion of NO 2 to NO under UV illumination. Overall rates of conversion (on the order of mg/m 2 paint/year) suggest that these materials shoud have negligible impact on NO x levels in the environment.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.052
GPT teacher head0.253
Teacher spread0.202 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations4
Published2015
Admission routes1
Has abstractyes

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