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Record W2318559630 · doi:10.1021/ma300394u

Heterocycle-Induced Phase Separation in Conjugated Polymers

2012· article· en· W2318559630 on OpenAlexaff
Jon Hollinger, Paul M. DiCarmine, Dominik Karl, Dwight S. Seferos

Bibliographic record

VenueMacromolecules · 2012
Typearticle
Languageen
FieldEngineering
TopicOrganic Electronics and Photovoltaics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPolymerDifferential scanning calorimetryThiopheneMaterials scienceCopolymerPolymerizationPolymer chemistryConjugated systemAbsorption spectroscopyChemical engineeringPhase (matter)Absorption (acoustics)ChemistryOrganic chemistryOpticsComposite material

Abstract

fetched live from OpenAlex

Grignard metathesis polymerization was used to synthesize a series of poly(3-hexylselenophene)- block -poly(3-hexylthiophene) copolymers with two different molecular weights and varying selenophene content. These polymers were characterized by optical absorption spectroscopy (film and solution), differential scanning calorimetry, powder X-ray diffraction, variable temperature absorption spectroscopy, and atomic force microscopy (on self-assembled polymer nanofibers). The selenophene to thiophene ratio has a large influence on optical properties, and absorption is tunable across the range of both homochromophores. We observe phase separation in the solid state in both pristine and annealed samples. When allowed to slowly assemble in solution, high molecular weight copolymers have a very sharp transition from the molecularly dissolved to the aggregated state. Most interestingly, increasing polyselenophene content induces the polymer to assemble more readily (at a higher temperature) but also appears to hinder the degree of ordered assembly when the thiophene block is not sufficiently long. This study furthers the understanding of the differences between these structurally similar conjugated polymer building blocks and provides insight into the factors that control heterocycle-induced phase separation.

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 score0.005
Threshold uncertainty score0.515

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 head0.011
GPT teacher head0.265
Teacher spread0.253 · 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

Citations66
Published2012
Admission routes1
Has abstractyes

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