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Record W4224239973 · doi:10.1021/acs.langmuir.1c03472

Formation of Core-Sheath Polymer Fibers by Free Surface Spinning of Aqueous Two-Phase Systems.

2022· article· en· W4224239973 on OpenAlexafffund

Bibliographic record

VenuePubMed · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicHermeneutics and Narrative Identity
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsSpinningPolymerPolyethylene oxideDextranFiberAqueous solutionPolyethyleneAttenuated total reflectionComposite number

Abstract

fetched live from OpenAlex

Core-sheath fibers have numerous applications ranging from composite materials for advanced manufacturing to materials for drug delivery and regenerative medicine. Here, a simple and tunable approach for the generation of core-sheath fibers from immiscible solutions of dextran and polyethylene oxide is described. This approach exploits the entanglement of polymer molecules within the dextran and polyethylene oxide phases for free surface spinning into dry fibers. The mechanism by which these core-sheath fibers are produced after contact with a solid substrate (such as a microneedle) involves complex flows of the phase-separating polymer solutions, giving rise to a liquid-liquid core-sheath flow that is drawn into a liquid bridge. This liquid bridge then elongates into a core-sheath fiber through extensional flow as the contacting substrate is withdrawn. The core-sheath structure of the fibers produced by this approach is confirmed by attenuated total reflection Fourier-transform infrared spectroscopy and confocal microscopy. Tuning of the core diameter is also demonstrated by varying the weight percentage of dextran added to the reservoir from which the fibers are formed.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.299
Threshold uncertainty score0.366

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 head0.043
GPT teacher head0.240
Teacher spread0.198 · 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 designNot applicable
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

Citations3
Published2022
Admission routes2
Has abstractyes

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