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Record W2153742028 · doi:10.1002/pola.24538

Band‐gap engineering of polythiophenes via dithienophosphole doping

2011· article· en· W2153742028 on OpenAlexaff
Robin Krüger, Terry J. Gordon, Todd C. Sutherland, Thomas Baumgartner

Bibliographic record

VenueJournal of Polymer Science Part A Polymer Chemistry · 2011
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPolymerThiopheneDopingHOMO/LUMOPhospholePolymer chemistryAcceptorMaterials scienceBand gapChloroformConductive polymerChemistryPhotochemistryMoleculeOrganic chemistryOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

Abstract A series of polythiophenes doped with dithieno[3,2‐b:2′,3′‐d]phosphole units at varying levels (0–17%) were synthesized and characterized. Polymer work up provided two series of polymers from chloroform (C) and hexanes (H) for each doping level, respectively. Systematic structure–property studies revealed that the C‐series polymers generally had higher molecular weights than the H‐series, but also slightly higher relative dithienophosphole concentrations, both having a significant impact on the photophysical and electrochemical properties of the polymers. Furthermore, the presence of the dithienophosphole units also stabilizes the LUMO levels, whereas the HOMO levels remain dominated by the thiophene units, resulting in desirable electronics for an interaction with acceptor materials, such as 1‐(3‐methoxycarbonyl)propyl‐1‐phenyl[6,6]C61. Importantly, increasing amount of dithienophosphole doping results in increased conductivities for the polymers in their oxidized state, while concurrently significantly stabilizing the neutral polythiophenes toward oxidation under environmental conditions. © 2011 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem, 2011

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 score0.002

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 head0.026
GPT teacher head0.235
Teacher spread0.210 · 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

Citations19
Published2011
Admission routes1
Has abstractyes

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