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Record W4388892540 · doi:10.1139/cjp-2023-0273

Time-dependent wave-packet approach to the excited-state intramolecular proton transfer based on relaxed potential energy surfaces of benzimidazole molecule

2023· article· en· W4388892540 on OpenAlexvenueno aff
Jinmu Gao, Xucong Zhou, Qingtian Meng, Hua Zhang

Bibliographic record

VenueCanadian Journal of Physics · 2023
Typearticle
Languageen
FieldChemistry
TopicPhotochemistry and Electron Transfer Studies
Canadian institutionsnot available
FundersNatural Science Foundation of Shandong ProvinceNational Natural Science Foundation of China
KeywordsIntramolecular forceExcited stateBenzimidazoleMoleculeProtonEnergy transferWave packetPotential energyPotential energy surfaceTransfer (computing)Energy (signal processing)ChemistryMaterials scienceAtomic physicsMolecular physicsPhysicsComputer scienceStereochemistryQuantum mechanics

Abstract

fetched live from OpenAlex

The wave-packet dynamical approach was used to investigate the excited-state intramolecular proton transfer (PT) of 5′-amino-2-(2′-hydroxyphenyl) benzimidazole (P1) based on the relaxed potential surfaces. For the involved PT process of P1 molecule, the quantum chemical calculation shows that relative to the ground state S0 with a single potential well, the excited state S1 with a double potential well can give much dynamical information. It is also found that because of the existence of a shallow potential barrier on the excited state S1, the PT in it is much easier than that in the ground state S0. When a laser field is used to pump the wave packet and induce the PT process, the external field parameters, such as the frequency and the envelope, also have influences on the PT process because of the impact of both pump time and the energy gap between two states. This kind of external field effect on the PT process gives a specific perspective to examine its dynamical mechanism and provides a reference for exploring the light-induced biophysical processes.

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)0.000
metaresearch head score (Gemma)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.232
Threshold uncertainty score0.781

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.010
GPT teacher head0.190
Teacher spread0.180 · 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
Published2023
Admission routes1
Has abstractyes

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