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Record W2585940696 · doi:10.1002/app.44862

Electrospinning of porous polylactic acid fibers during nonsolvent induced phase separation

2017· article· en· W2585940696 on OpenAlexafffund
Ehsan Rezabeigi, Marwa Sta, Mitasha Swain, Julia McDonald, Nicole R. Demarquette, R. A. L. Drew, Paula M. Wood‐Adams

Bibliographic record

VenueJournal of Applied Polymer Science · 2017
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsÉcole de Technologie SupérieureConcordia University
FundersNatural Sciences and Engineering Research Council of CanadaU.S. Food and Drug Administration
KeywordsElectrospinningPolylactic acidMaterials scienceCrystallinityPorosityChemical engineeringScanning electron microscopePhase (matter)PolymerNanofiberComposite materialChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

ABSTRACT In this study, porous micron‐sized fibers of polylactic acid (PLA) are fabricated via electrospinning of PLA‐dichloromethane (DCM)‐hexane systems with no post treatment involved. Several compositions from the liquid‐liquid phase separated region of the phase diagram of this ternary system are selected and their electrospinnability are investigated throughout their phase separation process before gelation. We show that under constant processing and ambient parameters, there is a phase separation shelf time for each composition wherein the viscoelasticity of the systems is optimum to produce long, uniform porous fibers. For the first time, we investigate the effect of aging time during phase separation on the morphology of the electrospun fibers using scanning electron microscopy (SEM). Based on our results, certain phase separated systems provide a range of viscosity allowing for the production of porous spherical micro beads or fibers via electrospraying and electrospinning, respectively. It is also shown that obtaining long, uniform fibers from electrospinning of highly phase separated systems, e.g., a gel, is not feasible due to the high degree of crystallinity of their polymer‐rich domains and the solid‐like yielding behavior. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017, 134, 44862.

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 score0.000
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)0.0000.000

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 head0.014
GPT teacher head0.329
Teacher spread0.315 · 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

Citations54
Published2017
Admission routes2
Has abstractyes

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