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Record W2045454839 · doi:10.1002/adfm.201102623

Effects of the Molecular Weight and the Side‐Chain Length on the Photovoltaic Performance of Dithienosilole/Thienopyrrolodione Copolymers

2012· article· en· W2045454839 on OpenAlexaff
Ta‐Ya Chu, Jianping Lu, Serge Beaupré, Yanguang Zhang, Jean‐Rémi Pouliot, Jiayun Zhou, Ahmed Najari, Mario Leclerc, Ye Tao

Bibliographic record

VenueAdvanced Functional Materials · 2012
Typearticle
Languageen
FieldEngineering
TopicOrganic Electronics and Photovoltaics
Canadian institutionsUniversité LavalInstitute for Microstructural Sciences
Fundersnot available
KeywordsMaterials sciencePolymer solar cellPhotocurrentCopolymerEnergy conversion efficiencyPhotovoltaic systemPolymerSide chainAlkylBand gapEquivalent series resistanceQuantum efficiencySolar cellChemical engineeringPolymer chemistryOptoelectronicsOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

Abstract A series of low‐bandgap alternating copolymers of dithienosilole and thienopyrrolodione (PDTSTPDs) are prepared to investigate the effects of the polymer molecular weight and the alkyl chain length of the thienopyrrole‐4,6‐dione (TPD) unit on the photovoltaic performance. High‐molecular‐weight PDTSTPD leads to a higher hole mobility, lower device series resistance, a larger fill factor, and a higher photocurrent in PDTSTPD:[6,6]‐phenyl C71 butyric acid methyl ester (PC71BM) bulk‐heterojunction solar cells. Different side‐chain lengths show a significant impact on the interchain packing between polymers and affect the blend film morphology due to different solubilities. A high power conversion efficiency of 7.5% is achieved for a solar cell with a 1.0 cm2 active area, along with a maximum external quantum efficiency (EQE) of 63% in the red region.

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 head0.158
Teacher spread0.155 · 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

Citations234
Published2012
Admission routes1
Has abstractyes

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