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Record W2609469507 · doi:10.1021/acssuschemeng.7b00863

Electrospinning Biopolymers from Ionic Liquids Requires Control of Different Solution Properties than Volatile Organic Solvents

2017· article· en· W2609469507 on OpenAlexafffund
Oleksandra Zavgorodnya, Julia L. Shamshina, Jonathan R. Bonner, Robin D. Rogers

Bibliographic record

VenueACS Sustainable Chemistry & Engineering · 2017
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsMcGill University
FundersOffice of Nuclear EnergyOffice of ScienceCanada Excellence Research Chairs, Government of Canada
KeywordsElectrospinningIonic liquidChemical engineeringViscosityPolymerBiopolymerSpinningFiberMaterials scienceChitinNanofiberRheologyConductivityChemistryPolymer chemistryOrganic chemistryComposite materialChitosanCatalysisPhysical chemistry

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide We report the correlation between key solution properties and spinability of chitin from the ionic liquid (IL) 1-ethyl-3-methylimidazolium acetate ([C 2 mim][OAc]) and the similarities and differences to electrospinning solutions of nonionic polymers in volatile organic compounds (VOCs). We found that when electrospinning is conducted from ILs, conductivity and surface tension are not the key parameters regulating spinability, while solution viscosity and polymer concentration are. Contrarily, for electrospinning of polymers from VOCs, solution conductivity and viscosity have been reported to be among some of the most important factors controlling fiber formation. For chitin electrospun from [C 2 mim][OAc], we found both a critical chitin concentration required for continuous fiber formation (>0.20 wt %) and a required viscosity for the spinning solution (between ca . 450–1500 cP). The high viscosities of the biopolymer–IL solutions made it possible to electrospin solutions with low, less than 1 wt %, polymer concentration and produce thin fibers without the need to adjust the electrospinning parameters. These results suggest new prospects for the control of fiber architecture in nonwoven mats, which is crucial for materials performance.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.977

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 science0.0010.000
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 head0.204
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 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

Citations50
Published2017
Admission routes2
Has abstractyes

Explore more

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