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Record W3197174316 · doi:10.1002/anie.202109357

Mechanism of the Photodegradation of A‐D‐A Acceptors for Organic Photovoltaics**

2021· article· en· W3197174316 on OpenAlexafffund
Yuxuan Che, Muhammad Rizwan Niazi, Ricardo Izquierdo, Dmitrii F. Perepichka

Bibliographic record

VenueAngewandte Chemie International Edition · 2021
Typearticle
Languageen
FieldEngineering
TopicOrganic Electronics and Photovoltaics
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à MontréalMcGill University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsPhotodegradationPhotochemistryChemistryPhotocurrentOrganic solar cellMoietyAcceptorThiopheneMaterials scienceStereochemistryOrganic chemistryPolymerPhotocatalysis

Abstract

fetched live from OpenAlex

Herein, we elucidate the photodegradation pathway of A-D-A-type non-fullerene acceptors for organic photovoltaics. Using IT-4F as a benchmark example, we isolated the photoproducts and proved them isomers of IT-4F formed by a 6-e electrocyclic reaction between the dicyanomethylene unit and the thiophene ring, followed by a 1,5-sigmatropic hydride shift. This photoisomerization was accelerated under inert conditions, as explained by DFT calculations predicting a triplet-mediated reaction path (quenchable by oxygen). Adding controlled amounts of the photoproduct P1 to PM6:IT-4F bulk heterojunction cells led to a progressive decrease in photocurrent and fill factor attributed to its poor absorption and charge transport properties. The reaction is a general photodegradation pathway for a series of A-D-A molecules with 1,1-dicyanomethylene-3-indanone termini, and its rate varies with the structure of the donor and acceptor moiety.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.009
GPT teacher head0.217
Teacher spread0.208 · 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 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

Citations90
Published2021
Admission routes2
Has abstractyes

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