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Record W2416937437 · doi:10.1021/acs.jpca.5b12358

Low Temperature Rate Constants for the Reactions of O(<sup>1</sup>D) with N<sub>2</sub>, O<sub>2</sub>, and Ar

2016· article· en· W2416937437 on OpenAlexaboutno aff
Romain Grondin, Jean‐Christophe Loison, Kevin M. Hickson

Bibliographic record

VenueThe Journal of Physical Chemistry A · 2016
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Laser Applications
Canadian institutionsnot available
FundersInstitut national des sciences de l'Univers
KeywordsReaction rate constantQuenching (fluorescence)PhotodissociationAtmospheric temperature rangeAnalytical Chemistry (journal)ChemistryKineticsChemical kineticsAtomic physicsUltravioletFluorescencePhysical chemistryMaterials sciencePhotochemistryThermodynamicsPhysicsOptics

Abstract

fetched live from OpenAlex

The kinetics of the gas-phase quenching reactions O((1)D) + N2, O((1)D) + O2, and O((1)D) + Ar have been studied over the 50-296 K temperature range using the Laval nozzle method. O((1)D) atoms were created in situ by the pulsed photolysis of O3 precursor molecules at 266 nm. Rate constants for these processes were measured directly, following the decay of O((1)D) atoms through vacuum ultraviolet laser-induced fluorescence at 115.215 nm. For the O((1)D) + N2 and O((1)D) + O2 reactions, the quenching efficiencies are seen to increase as the temperature falls. For the O((1)D) + N2 system, this indicates the likely influence of the intermediate complex lifetime on the quenching rate through nonadiabatic processes. For the O((1)D) + O2 system, which is considerably more complex, this behavior could result from the interactions between several potential energy surfaces.

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)0.002
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.006
GPT teacher head0.227
Teacher spread0.221 · 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

Citations41
Published2016
Admission routes1
Has abstractyes

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