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Record W3083712454 · doi:10.1063/5.0022164

Reversible triplet energy hopping in photo-excited molecules: A two-site model for the spin polarization

2020· article· en· W3083712454 on OpenAlexafffund
Yuri E. Kandrashkin, Marilena Di Valentin, Art van der Est

Bibliographic record

VenueThe Journal of Chemical Physics · 2020
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsBrock University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsExcited stateTriplet stateSpin polarizationPolarization (electrochemistry)ChemistrySpectroscopyKinetic energyElectron paramagnetic resonanceAnisotropyAtomic physicsSinglet stateMolecular physicsCondensed matter physicsElectronPhysicsQuantum mechanicsPhysical chemistry

Abstract

fetched live from OpenAlex

The effect of reversible energy hopping between different local environments on the properties of spin-polarized excited states is investigated theoretically using a two-site model. The kinetic equations for the populations of the spin sublevels of the excited state are derived and then used to obtain analytical expressions for the evolution of the spin polarization of excited triplet states under specific conditions. The time dependence of the triplet state polarization patterns is also obtained by numerical solution of the kinetic equations. It is shown that the reversible energy hopping can lead to significant changes in the properties of the triplet state, including changes in the shape of the observed spectrum and, in some cases, the inversion of the sign of the polarization, the generation of the net polarization, and anisotropic spin-lattice relaxation. The relations between the parameters that can be observed experimentally by time-resolved electron paramagnetic resonance spectroscopy and the kinetic and dynamic parameters of the system are discussed.

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)0.001
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.012
GPT teacher head0.212
Teacher spread0.200 · 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

Citations11
Published2020
Admission routes2
Has abstractyes

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