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Record W3216914824 · doi:10.1021/acsapm.1c01088

On the Importance of Noncrystalline Phases in Semicrystalline Electrospun Nanofibers

2021· article· en· W3216914824 on OpenAlexafffund
Foad Soleimani, Mozhdeh Mazaheri, Christian Pellerin, Reza Bagheri

Bibliographic record

VenueACS Applied Polymer Materials · 2021
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of CanadaSharif University of Technology
KeywordsDifferential scanning calorimetryMaterials scienceMesophaseCrystallinityNanofiberCrystallizationFourier transform infrared spectroscopyAmorphous solidEnthalpyAttenuated total reflectionAnnealing (glass)Glass transitionChemical engineeringComposite materialCrystallographyPolymerLiquid crystalThermodynamicsChemistry

Abstract

fetched live from OpenAlex

Tailoring the properties of electrospun fibers requires a detailed understanding and control of their microstructure. We investigate the structure/property relationships in fabrics of randomly aligned fibers of polylactide, a prevalent biopolymer, either as-spun or after annealing and solvent-induced crystallization. In-depth characterization by field-emission scanning electron microscope (FESEM), wide-angle X-ray diffraction (WAXD), attenuated total reflection Fourier transform infrared (ATR-FTIR), and modulated temperature differential scanning calorimetry (MT-DSC) reveals that the as-spun fibers comprise crystalline and mesomorphic phases, as well as oriented but mobile amorphous chain segments. These chains are mostly responsible for the low-temperature cold crystallization and for the recovery endotherm around the glass transition, while the mesophase transforms into crystals with nearly zero enthalpy. Such behaviors are attributed to high molecular orientation, which is further evidenced by unveiling a fibrillar superstructure in nanofibers. The thermodynamics and structural evolution under different conditions are described from an energy landscape perspective. Finally, we propose a micromechanism, based on a modified supramolecular model, which helps to elucidate the fibers’ molecular dynamics.

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 head0.009
GPT teacher head0.239
Teacher spread0.231 · 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

Citations11
Published2021
Admission routes2
Has abstractyes

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