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Record W1994175498 · doi:10.1021/cm1018184

Toward Stabilization of Domains in Polymer Bulk Heterojunction Films

2010· article· en· W1994175498 on OpenAlexafffund
Bobak Gholamkhass, Steven Holdcroft

Bibliographic record

VenueChemistry of Materials · 2010
Typearticle
Languageen
FieldEngineering
TopicOrganic Electronics and Photovoltaics
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceFourier transform infrared spectroscopyPolymer solar cellChemical engineeringMorphology (biology)HeterojunctionTransmission electron microscopyPolymerSolar cellEnergy conversion efficiencyCopolymerPolymer chemistryNanotechnologyOptoelectronicsComposite material

Abstract

fetched live from OpenAlex

With the purpose of studying the effect of stabilizing film morphology on polymer photovoltaic cell performance, the morphology and characteristics of bulk heterojunction devices fabricated using binary blends of an azide-functionalized graft copolymer of poly(3-hexylthiophene) (P3HT) and [6,6]-phenyl C 61 -butyric acid methyl ester (PCBM) were examined. A thermal, solid state reaction between the azide groups attached to P3HT and PCBM, confirmed by Fourier transform infrared spectroscopy (FTIR) and UV−vis spectrocopies, rendered the films largely insoluble and stabilized the morphology, as evidenced by limited growth of macroscopic crystals of PCBM over time, although transmission electron microscopy (TEM) analysis revealed no dramatic changes in morphology at the nanoscopic level. Photovoltaic (PV) devices prepared from these stabilized layers exhibited 1.85% power conversion efficiencies (PCE) which fell to 0.93% over 3 h at 150 °C, whereas native P3HT/PCBM devices, initially displaying 2.5% PCE dropped to 0.5% over the same period. The extent to which the morphology of the bulk heterojunction can be stabilized by this route is discussed.

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 categoriesInsufficient payload (model declined to judge)
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 score1.000

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 head0.006
GPT teacher head0.194
Teacher spread0.188 · 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.

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

Citations71
Published2010
Admission routes2
Has abstractyes

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