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Record W2100614922 · doi:10.1021/ct4010799

Electronic Excited States in Amorphous MEH-PPV Polymers from Large-Scale First Principles Calculations

2014· article· en· W2100614922 on OpenAlexaff
Haibo Ma, Ting Qin, Alessandro Troisi

Bibliographic record

VenueJournal of Chemical Theory and Computation · 2014
Typearticle
Languageen
FieldEngineering
TopicOrganic Electronics and Photovoltaics
Canadian institutionsMinistry of Education and Child Care
Fundersnot available
KeywordsDelocalized electronExcited stateAmorphous solidExcitonChromophoreChemical physicsMaterials sciencePolymerMolecular physicsMolecular dynamicsQuantumCondensed matter physicsPhysicsAtomic physicsChemistryQuantum mechanicsCrystallography

Abstract

fetched live from OpenAlex

The electronic excited states of amorphous polymeric semiconductor MEH-PPV are investigated by first principles quantum chemical calculations based on trajectories from classical molecular dynamics simulations. We inferred an average conjugation length of ∼5-7 monomers for lowest vertical excitations of amorphous MEH-PPV at room temperature and verified that the normal definition of a chromophore in a polymer based on purely geometric "conjugation breaks" is not always valid in amorphous polymers and a rigorous definition can be only on the basis of the evaluation of the polymer excited state wave function. The charge transfer character is observed to be nearly invariant for all excited states in low energy window while the exciton delocalization extent is found to increase with energy. The interchain excitonic couplings for amorphous MEH-PPV are shown to be usually smaller than 10 meV suggesting that the transport mechanism across chain can be described by incoherent hopping. All these observations about the energetic and spatial distribution of the excitons in polymer as well as their couplings provide important qualitative insights and useful quantitative information for constructing a realistic model for exciton migration dynamics in amorphous polymer materials.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.269
Threshold uncertainty score0.334

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 head0.004
GPT teacher head0.200
Teacher spread0.196 · 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 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

Citations42
Published2014
Admission routes1
Has abstractyes

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