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Record W2006064382 · doi:10.1063/1.2221310

Chromophores in phenylenevinylene-based conjugated polymers: Role of conformational kinks and chemical defects

2006· article· en· W2006064382 on OpenAlexaff
Emmanuelle Hennebicq, Caroline Deleener, Jean‐Luc Brédas, Gregory D. Scholes, David Beljonne

Bibliographic record

VenueThe Journal of Chemical Physics · 2006
Typearticle
Languageen
FieldEngineering
TopicOrganic Electronics and Photovoltaics
Canadian institutionsUniversity of Toronto
FundersDivision of ChemistryDivision of Materials ResearchNational Science FoundationFonds pour la Formation à la Recherche dans l’Industrie et dans l’AgricultureFonds De La Recherche Scientifique - FNRSOffice of Naval ResearchAlfred P. Sloan Foundation
KeywordsExcited stateConjugated systemChromophoreDelocalized electronChemical physicsAcceptorPolymerMaterials scienceRelaxation (psychology)PhotochemistryExcitationMolecular physicsChemistryAtomic physicsCondensed matter physicsPhysicsQuantum mechanicsOrganic chemistry

Abstract

fetched live from OpenAlex

The influence of chemical defects and conformational kinks on the nature of the lowest electronic excitations in phenylenevinylene-based polymers is assessed at the semiempirical quantum-chemical level. The amount of excited-state localization and the amplitude of through-space (Coulomb-like) versus through-bond (charge-transfer-like) interactions have been quantified by comparing the results provided by excitonic and supermolecular models. While excitation delocalization among conjugated segments delineated by the defects occurs in the acceptor configuration, self-confinement on individual chromophores follows from geometric relaxation in the excited-state donor configuration. The extent of excited-state localization is found to be sensitive to both the nature of the defect and the length of the conjugated chains. Implications for resonant energy transfer along conjugated polymer chains 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 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 score0.015
Threshold uncertainty score0.338

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 head0.003
GPT teacher head0.175
Teacher spread0.172 · 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

Citations45
Published2006
Admission routes1
Has abstractyes

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