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Record W4221122490 · doi:10.3390/nano12060967

Ultra-High Tg Thermoset Fibers Obtained by Electrospinning of Functional Polynorbornenes

2022· article· en· W4221122490 on OpenAlexaff
Basile Commarieu, Moubarak Compaoré, Raphaël de Boëver, Régis Imbeault, Maxime Leprince, Barbara Y. Martin, Bruno Perard, Weiguang Qiu, Jérôme P. Claverie

Bibliographic record

VenueNanomaterials · 2022
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsElectrospinningThermosetting polymerPolymerMaterials scienceGlass transitionComposite materialLeaching (pedology)Polymer chemistry

Abstract

fetched live from OpenAlex

Insertion polynorbornenes (PBNEs) are rigid-rod polymers that have very high glass transition temperatures (Tg). In this study, two functional PNBEs were electrospun in the presence of a variety of cross-linkers, resulting in fibers with Tgs greater than 300 °C. The fibers are long (several mm), rigid, and with diameters that can be tuned in the range 300 nm–10 μm. The electrospinning process can be used to encapsulate dyes or graphene dots. Due to the high cross-linking density of the fiber, dye leaching is prevented. In contrast with other rigid-rod polymers, electrospinning of PNBE is facile and can be performed at injection rates as high as 1 mL/min.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.009
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.000

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 head0.006
GPT teacher head0.215
Teacher spread0.209 · 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.

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

Citations1
Published2022
Admission routes1
Has abstractyes

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