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Record W3153898695 · doi:10.1002/anie.202103357

Synergistic Effect of Dielectric Property and Energy Transfer on Charge Separation in Non‐Fullerene‐Based Solar Cells

2021· article· en· W3153898695 on OpenAlexaff
Pandeng Li, Fang Jin, Yusheng Wang, Sergei Manzhos, Lei Cai, Zheheng Song, Yajuan Li, Tao Song, Xuechun Wang, Xia Guo, Maojie Zhang, Dongling Ma, Baoquan Sun

Bibliographic record

VenueAngewandte Chemie International Edition · 2021
Typearticle
Languageen
FieldEngineering
TopicOrganic Electronics and Photovoltaics
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNational Key Research and Development Program of ChinaCollaborative Innovation Center of Suzhou Nano Science and TechnologyChina Postdoctoral Science FoundationNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsFullereneDielectricOrganic solar cellExcitonMaterials scienceAbsorption (acoustics)Chemical physicsCharge carrierAbsorption spectroscopyPhotovoltaic systemOptoelectronicsChemistryCondensed matter physicsOrganic chemistryPolymerOpticsComposite materialPhysics

Abstract

fetched live from OpenAlex

In non-fullerene-based photovoltaic devices, it is unclear how excitons efficiently dissociate into charge carriers under small driving force. Here, we developed a modified method to estimate dielectric constants of PM6 donor and non-fullerene acceptors. Surprisingly, most non-fullerene acceptors and blend films showed higher dielectric constants. Moreover, they exhibited larger dielectric constants differences at the optical frequency. These results are likely bound to reduced exciton binding energy and bimolecular recombination. Besides, the overlap between the emission spectrum of donor and absorption spectra of non-fullerene acceptors allowed the energy transfer from donor to acceptors. Hence, based on the synergistic effect of dielectric property and energy transfer resulting in efficient charge separation, our finding paves an alternative path to elucidate the physical working mechanism in non-fullerene-based photovoltaic devices.

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 score0.027
Threshold uncertainty score0.415

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 head0.005
GPT teacher head0.208
Teacher spread0.203 · 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

Citations60
Published2021
Admission routes1
Has abstractyes

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