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Record W4385426951 · doi:10.1139/cjc-2022-0237

The influence of monomolecular water and bimolecular water on BrO + CH<sub>2</sub>O reaction

2023· article· en· W4385426951 on OpenAlexvenueno aff
Yunju Zhang, Yongguo Liu, Meilian Zhao, Yu‐Xi Sun, Shuxin Liu

Bibliographic record

VenueCanadian Journal of Chemistry · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Chemical Physics Studies
Canadian institutionsnot available
FundersSichuan Province Science and Technology Support Program
KeywordsChemistryReaction rate constantTransition state theoryCatalysisReaction mechanismReaction rateActivation energyKineticsChemical kineticsPhotochemistryPhysical chemistryMoleculeComputational chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The catalytic influence of water molecules on BrO + CH2O reaction was discussed through the investigation of mechanism and kinetics. The potential energy surfaces and dynamic properties of the BrO + CH2O reaction were investigated at the CCSD(T)/aug-cc-pVTZ//B3LYP/cc-pVTZ level. The reaction generates four different products, respectively, through four pathways without water and nine pathways with water. The results have shown that the generation of HCO + HOBr occupied the entire reaction without water. The rate constants were obtained by employing the KisThelP program based on the Transition State Theory with Wigner tunneling correction. This rate constants of the naked reaction are lower than that in the presence of monomolecular water and are higher than that in the presence of bimolecular water. The current calculations indicated that monomolecular water could accelerate the BrO + CH2O reaction, while bimolecular water could not accelerate the BrO + CH2O reaction. The effective rate constants (in the presence of monomolecular water and bimolecular water) are much lower than that of the naked reaction.

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 score0.002
Threshold uncertainty score0.006

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 communication0.0000.001
Open science0.0000.000
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 head0.004
GPT teacher head0.189
Teacher spread0.186 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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