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Record W3116823765 · doi:10.1039/d0sm01550d

Polymer entanglement drives formation of fibers from stable liquid bridges of highly viscous dextran solutions

2020· article· en· W3116823765 on OpenAlexafffund
Gurkaran Chowdhry, Yi Ming Chang, John P. Frampton, Laurent Kreplak

Bibliographic record

VenueSoft Matter · 2020
Typearticle
Languageen
FieldMaterials Science
TopicPickering emulsions and particle stabilization
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for InnovationCancer Research Coordinating Committee
KeywordsReptationQuantum entanglementPolymerDextranMaterials scienceChemical physicsPolymer scienceChemistryComposite materialPhysicsChromatographyQuantum mechanics

Abstract

fetched live from OpenAlex

Liquid bridges have been studied for over 200 years due to their occurrence in many natural and industrial phenomena. Most studies focus on millimeter scale liquid bridges of Newtonian liquids. Here, reptation theory was used to explain the formation of 10 cm long liquid bridges of entangled polymer solutions, which subsequently stabilize into polymer fibers with tunable diameters between 3 and 20 mm. To control the fiber formation process, a horizontal single-fiber contact drawing system was constructed consisting of a motorized stage, a micro-needle, and a liquid filled reservoir. Analyzing the liquid bridge rupture statistics as a function of elongation speed, solution concentration and dextran molecular weight revealed that the fiber formation process was governed by a single timescale attributed to the relaxation of entanglements within the polymer solution. Further characterization revealed that more viscous solutions produced fibers of larger diameters due to secondary flow dynamics. Verification that protein additives such as type I collagen had minimal effect on fiber formation demonstrates the potential application in biomaterial fabrication.

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 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 score0.025
Threshold uncertainty score1.000

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)0.0010.000

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 head0.022
GPT teacher head0.236
Teacher spread0.214 · 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

Citations12
Published2020
Admission routes2
Has abstractyes

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