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Record W2121658232 · doi:10.1139/v10-063

A quasi-classical trajectory (QCT) study of the H + OF reaction stereodynamics

2010· article· en· W2121658232 on OpenAlexvenueno aff
Dan Zhao, Tianyu Zhang, Tian Shu Chu

Bibliographic record

VenueCanadian Journal of Chemistry · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Chemical Physics Studies
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsChemistryExcited stateAdiabatic processPolarization (electrochemistry)Atomic physicsPotential energy surfacePerpendicularReagentProduct (mathematics)ScatteringMolecular physicsAnalytical Chemistry (journal)MoleculePhysical chemistryPhysicsQuantum mechanicsGeometry

Abstract

fetched live from OpenAlex

Based on the global three-dimensional adiabatic potential surface of the 13A′ excited electronic state (J. Chem. Phys. 2005, 123, 114310) of the OHF system, we investigated the H + OF → OH + F/HF + O reaction stereodynamics by using the quasi-classical trajectory (QCT) method. The four polarization-dependent differential cross sections (PDDCSs) and the three angular distributions P(θr), P([Formula: see text]), P(θr, [Formula: see text]) were calculated at a low collision energy of 0.48 eV for both product channels. The results indicated that the products are backward-scattering on the triplet state, and the product rotational angular momenta are aligned or oriented. Moreover, there is a remarkable difference between the polarization behaviors of the two product channels. Product orientation exhibited by the OH + F channel is found to be absent in the HF + O channel at this collision energy albeit the latter shows stronger alignment along the direction perpendicular to the reagent relative velocity k than OH + F.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

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 studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.218
Teacher spread0.211 · 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 designSimulation or modeling
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
Published2010
Admission routes1
Has abstractyes

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