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Record W2120443641 · doi:10.1139/v09-157

Design, synthesis, and properties of benzobisthiadiazole-based donor–π–acceptor–π–donor type of low-band-gap chromophores and polymers

2010· article· en· W2120443641 on OpenAlexafffundvenue
Gang Qian, Zhi Yuan Wang

Bibliographic record

VenueCanadian Journal of Chemistry · 2010
Typearticle
Languageen
FieldEngineering
TopicOrganic Electronics and Photovoltaics
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChromophoreAcceptorChemistryPhotochemistryThiopheneIntramolecular forcePyrrolePolymerFluoreneBand gapPolymer solar cellMaterials scienceStereochemistryOptoelectronicsOrganic chemistry

Abstract

fetched live from OpenAlex

A novel low-band-gap chromophore (5, 0.86 eV) having fluorene as a donor, benzobisthiadiazole (BBTD) as an acceptor, and pyrrole as a π-spacer was successfully designed and synthesized, to probe the effect of π-spacer on the band-gap level of the donor–π–acceptor–π–donor type of chromophores. Compared with the thiophene spacer analogue (in compound 3), the intramolecular hydrogen bonding between the pyrrole and the neighboring BBTD unit pushes the absorption maximum and fluorescence emission of chromophore 5 into the near-infrared spectral region with a red shift of 172 and 158 nm, respectively. The same red-shift phenomenon can also be realized by addition of Lewis acid (e.g., BF 3 ) to the BBTD-containing chromophores with other spacers. Attempt of using low-band-gap chromophore 5 in bulk heterojunction (BHJ) solar cells was made, showing a non-optimized photovoltaic device with the power conversion efficiency of 0.01%. A precursor approach to introduction of the alkaline-labile BBTD acceptor into the polymer backbone has been demonstrated by successful synthesis of low-band-gap polymer P2. The same strategy can be in principle applied to the synthesis of a series of low-band-gap chromophores or polymers with strong acceptors.

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 score0.007
Threshold uncertainty score0.495

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 head0.009
GPT teacher head0.174
Teacher spread0.165 · 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

Citations50
Published2010
Admission routes3
Has abstractyes

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