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Record W2417726307 · doi:10.1021/acs.macromol.5b00859

Optimization of Broad-Response and High-Detectivity Polymer Photodetectors by Bandgap Engineering of Weak Donor–Strong Acceptor Polymers

2015· article· en· W2417726307 on OpenAlexafffund
Ji Qi, Jinfeng Han, Xiaokang Zhou, Dezhi Yang, Jidong Zhang, Wenqiang Qiao, Dongge Ma, Zhi Yuan Wang

Bibliographic record

VenueMacromolecules · 2015
Typearticle
Languageen
FieldEngineering
TopicOrganic Electronics and Photovoltaics
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsPhotodetectorMaterials sciencePolymerBand gapStackingAbsorption (acoustics)OptoelectronicsAcceptorSpecific detectivityHOMO/LUMOAbsorption spectroscopySpectral lineOpticsDark currentChemistryPhysicsMoleculeOrganic chemistry

Abstract

fetched live from OpenAlex

A series of weak donor–strong acceptor polymers containing two different electron-deficient units (diketopyrrolopyrrole and thienoisoindigo) are synthesized and used in broad-response and high-detectivity polymer photodetectors. By adjusting the composition ratio of the two acceptors, the absorption spectra, energy levels, molecular stacking, and film morphology are affected, which in turn influence the photodetector performance. With increased thienoisoindigo component, the HOMO energy levels shift from −5.41 to −4.76 eV, and the LUMO energy levels are nearly unchanged, corresponding to reduced bandgaps and red-shifted absorption spectra. 1,8-Diiodooctane additive shows greatly impact on the film morphology, which affects the photodetector performance significantly. Going from P1 to P5, the detectivity decreases, but the response range increases. The photodetector based on P4 exhibits detectivity of greater than 10 11 Jones in a broad spectral region of 300–1200 nm, which is really promising for UV–vis–NIR light detection.

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 score0.017
Threshold uncertainty score0.889

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 head0.005
GPT teacher head0.186
Teacher spread0.182 · 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

Citations101
Published2015
Admission routes2
Has abstractyes

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