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Record W4310117114 · doi:10.1063/5.0132157

A two-site triplet exciton hopping model: Application to 3P700

2022· article· en· W4310117114 on OpenAlexafffund
Yuri E. Kandrashkin, Art van der Est

Bibliographic record

VenueThe Journal of Chemical Physics · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpectroscopy and Quantum Chemical Studies
Canadian institutionsBrock University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHamiltonian (control theory)PopulationRedistribution (election)Electron paramagnetic resonanceTriplet stateChemistrySpectral lineRate equationAtomic physicsExcited stateExcitonDensity matrixPhysicsMolecular physicsEigenvalues and eigenvectorsQuantum mechanicsCondensed matter physicsQuantum

Abstract

fetched live from OpenAlex

A model is presented describing the effect on spin-polarized transient EPR signals caused by incoherent state hopping between two sites. It is shown that the size of the spin state space can be reduced by half to the subspace described by the site-average Hamiltonian and that the dynamics of the system results in a redistribution of the population between its eigenstates. Analytical expressions for the rates of population redistribution and the line shape are derived for the general case in which the back-and-forth rates are unequal. The EPR signals calculated using these expressions are in very good agreement with those obtained by direct numerical solution of the density matrix rate equations. The model is then used to investigate the influence of exciton hopping on triplet state transient EPR spectra. Using the triplet state of the primary donor of Photosystem I as an example, it is shown that the influence of unequal hopping rates becomes more pronounced in the spectrum at longer delay times after the laser flash.

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 score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.012
GPT teacher head0.268
Teacher spread0.256 · 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

Citations3
Published2022
Admission routes2
Has abstractyes

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