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Record W2752798013 · doi:10.1021/acsnano.7b05546

Conductive Polymer Waving in Liquid Nitrogen

2017· article· en· W2752798013 on OpenAlexafffund
Jifu Mao, Chaojing Li, Hyun Jin Park, Mahmoud Rouabhia, Ze Zhang

Bibliographic record

VenueACS Nano · 2017
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchFondation CHU de QuébecCentre Hospitalier Universitaire de Québec
KeywordsPolypyrroleMaterials sciencePolymerConductive polymerNanotechnologyElectrical conductorGlass transitionCompoundingMembraneLiquid nitrogenChemical engineeringComposite materialPolymerizationOrganic chemistryChemistry

Abstract

fetched live from OpenAlex

The poor mechanical properties and processability of pristine heterocyclic conductive polymers represent the most notable scientific and technological challenges that have greatly limited the application of these polymers. We report a soft and mechanically processable free-standing pristine polypyrrole (PPy) membrane (PPy-N) that is as soft in liquid nitrogen (−196 °C) as it is at room temperature, despite a glass transition temperature ( T g ) above 100 °C. This PPy membrane also displays a highly attractive combination of properties, including mechanical processability, lightweight (9 g m –2 ), large surface area (14.5 m 2 g –1 ), stable electrothermal behavior, amphiphilicity, excellent cytocompatibility, and easy synthesis, at virtually any size. This discovery demonstrates an approach to changing the mechanical property of heterocyclic conductive polymer with no chemical alterations or compounding and may enhance the development of inherently conducting polymers for applications in energy storage and biomedicine and as lightweight conducting and heating materials.

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 score0.342

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 head0.033
GPT teacher head0.299
Teacher spread0.266 · 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

Citations35
Published2017
Admission routes2
Has abstractyes

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