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Record W2069510902 · doi:10.1039/c3tc32271h

Panchromatic small molecules for UV-Vis-NIR photodetectors with high detectivity

2014· article· en· W2069510902 on OpenAlexaff
Ji Qi, Liang Ni, Dezhi Yang, Xiaokang Zhou, Wenqiang Qiao, Mao Li, Dongge Ma, Zhi Yuan Wang

Bibliographic record

VenueJournal of Materials Chemistry C · 2014
Typearticle
Languageen
FieldEngineering
TopicOrganic Electronics and Photovoltaics
Canadian institutionsCarleton University
Fundersnot available
KeywordsMaterials scienceAbsorption (acoustics)OptoelectronicsPanchromatic filmPhotodetectorTransmittanceAcceptorAttenuation coefficientSpecific detectivityLayer (electronics)OpticsDark currentNanotechnologyPhysics

Abstract

fetched live from OpenAlex

Two donor–acceptor–donor (D–A–D) type low-bandgap small molecules, M1 and M2, with bis(2-thienyl)-N-alkylpyrrole (TPT) as the donor and thieno[3,4-b]thiadiazole (TT) as the acceptor were designed and synthesized. The absorption, transmission, electrochemical, thermal and film properties were studied. The compounds showed panchromatic absorption in the spectral range of 300–1000 nm. Moreover, they also exhibited semi-transparent property in the visible region (400–700 nm). Small molecule photodetectors (SMPDs) based on M1 and M2 were fabricated and studied. For the SMPD with BCP as the hole blocking layer (HBL), a detectivity of 5.0 × 1011 Jones at 800 nm at −0.1 V was obtained, which is among the highest detectivities reported for NIR SMPDs. With a sufficiently thin silver electrode, visibly transparent PDs with an average transmittance of 45% in the visible region were obtained for the first time. The transparent PDs exhibited fairly constant and high detectivity between 1011 and 1012 Jones over a broad spectral range of 300–900 nm. In addition, side chains of the compounds exhibited a great influence on the device performance, which could be assigned to their film absorption coefficient, molecular packing and active layer morphology.

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 score0.004
Threshold uncertainty score0.575

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 head0.004
GPT teacher head0.171
Teacher spread0.167 · 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

Citations59
Published2014
Admission routes1
Has abstractyes

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