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Record W4387161403 · doi:10.1177/00037028231202791

Raman Analysis of Orientation and Crystallinity in High <i>T</i> <sub>g</sub> , Low Crystallinity Electrospun Fibers

2023· article· en· W4387161403 on OpenAlexafffund
Arnaud W. Laramée, Christian Pellerin

Bibliographic record

VenueApplied Spectroscopy · 2023
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsUniversité de Montréal
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsCrystallinityAmorphous solidMaterials sciencePolymerRaman spectroscopyElectrospinningChemical engineeringEvaporationComposite materialCrystallographyChemistryOptics

Abstract

fetched live from OpenAlex

Electrospun fibers of amorphous or low-crystallinity polymers typically exhibit a low molecular orientation that can hamper their properties and application. A key stage of the electrospinning process that could be harnessed to mitigate the loss of orientation is jet rigidification, which relates closely to the solvent evaporation rate. Here, we establish quantitative Raman methods to assess the molecular orientation and crystallinity of weakly crystalline poly(2,6-dimethyl-1,4-phenylene oxide) fibers with varying diameters. Our findings demonstrate that solvent volatility can be leveraged to modulate the orientation and crystallinity through its impact on the effective glass transition temperature ( T g,eff ) of the polymer jet during the electrospinning process. Specifically, a highly volatile solvent yields a higher and more sustained orientation (median ⟨ P 2 ⟩ of 0.53 for diameters &lt; 1.0 µm) because its fast evaporation rapidly increases T g,eff above room temperature. This vitrification early along the jet path promotes the formation of an oriented amorphous phase and a moderate fraction of strain-induced crystals. Our data reveals that a high T g is a crucial parameter for reaching high orientation in amorphous or low-crystallinity polymer systems.

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 categoriesMeta-epidemiology (narrow)
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 score0.033
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.006
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 head0.005
GPT teacher head0.241
Teacher spread0.236 · 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

Citations9
Published2023
Admission routes2
Has abstractyes

Explore more

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