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Record W4306848269 · doi:10.1002/jccs.202200404

Collaborative control of branching ratio in the O + <scp>HO<sub>2</sub></scp> → <scp>OH</scp> + <scp>O<sub>2</sub></scp> reaction via vibrational and rotational excitation

2022· article· en· W4306848269 on OpenAlexaff
Qixin Chen, Xixi Hu, Daiqian Xie

Bibliographic record

VenueJournal of the Chinese Chemical Society · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsMinistry of Education and Child Care
FundersNational Natural Science Foundation of China
KeywordsChemistryExcitationBranching fractionBranching (polymer chemistry)Reaction dynamicsAtomic physicsPotential energy surfaceWork (physics)Analytical Chemistry (journal)MoleculePhysicsOrganic chemistryThermodynamics

Abstract

fetched live from OpenAlex

Abstract To understand the effect of different vibrational and rotational modes of reactant to enhance the reactivity of the O + HO 2 → OH + O 2 reaction, we revisited this important atmospheric reaction. We report here a quasi‐classical trajectory (QCT) study of the reaction dynamics on a recently developed full‐dimensional potential energy surface (PES). Our previous work has indicated that this reaction has two pathways, the H abstraction (HA) channel and the O abstraction (OA) channel, which lead to totally different product energy distribution. In this work, we identified that the vibrational excitation of the OH stretching ( v 1 ) mode of HO 2 is the switch of the HA channel at low collision energy; meanwhile, the rotational excitation can also greatly change the branching ratio of the two pathways. With the excitation of v 1 mode, the original negligible HA channel controlled by the tight transition state becomes quite important. This work presents an approach to control the branching ratio via collaboration between vibrational and rotational excitation and will enrich the knowledge of the O + HO 2 reaction in atmospheric chemistry and physics.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.757
Threshold uncertainty score0.902

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.005
GPT teacher head0.202
Teacher spread0.198 · 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 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
Published2022
Admission routes1
Has abstractyes

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