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Record W2990642224 · doi:10.1039/c9ta11285e

Tuning terminal aromatics of electron acceptors to achieve high-efficiency organic solar cells

2019· article· en· W2990642224 on OpenAlexaff
Ran Qin, Di Wang, Guanqing Zhou, Zhipeng Yu, Shuixing Li, Yuhao Li, Zhi-Xi Liu, Haiming Zhu, Minmin Shi, Xinhui Lu, Chang‐Zhi Li, Hongzheng Chen

Bibliographic record

VenueJournal of Materials Chemistry A · 2019
Typearticle
Languageen
FieldEngineering
TopicOrganic Electronics and Photovoltaics
Canadian institutionsGovernment of Northwest Territories
FundersInternational Science and Technology Cooperation ProgrammeFundamental Research Funds for the Central UniversitiesNatural Science Foundation of Zhejiang ProvinceNational Natural Science Foundation of China
KeywordsOrganic solar cellAcceptorTerminal (telecommunication)Electron acceptorFullereneMaterials scienceElectronElectron transferEnergy conversion efficiencyPhotochemistryOptoelectronicsChemistryOrganic chemistryComputer sciencePhysicsTelecommunications

Abstract

fetched live from OpenAlex

The structural tuning of non-fullerene acceptors with extended terminal aromatics enables faster hole transfer from the acceptor to the donor at smaller energy offsets, thereby achieving high efficiency in organic solar cells.

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 score0.001
Threshold uncertainty score0.926

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)0.0010.000

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 head0.188
Teacher spread0.184 · 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

Citations103
Published2019
Admission routes1
Has abstractyes

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