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Record W2907160486 · doi:10.1109/tia.2018.2867523

Mass Production of Nanocomposites Using Electrospinning

2018· article· en· W2907160486 on OpenAlexafffund
Chitral J. Angammana, Ryan Gerakopulos, Shesha Jayaram

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2018
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectrospinningNanocompositeProduction (economics)Materials scienceComposite materialPolymer

Abstract

fetched live from OpenAlex

Many conventional polymer processing technologies for compounding micro-/nanocomposites are known in the field. These methods include the direct use of high shear mixers, roll mixers, Banbury mixers, and extruders. With recent interest in advanced composites with nanoscale fillers, efforts have been made to enhance conventional processing technologies as the imposed input energy is often ineffective at breaching the energy barrier to breakup agglomerated nanofiller structures. Electrospinning is a simple, inexpensive process that can be used to produce continuous fibers from submicron to nanometer diameter scale through an electrically charged polymer jet. In this paper, authors present a rotary electrospinning method developed to produce nanocomposites at mass scale by using simultaneously mechanical and electrical forces with a proprietary apparatus. A case study using silica nanoparticles and silicone rubber matrix is presented to demonstrate the capability of the above method of dispersing nanoparticles in highly viscous polymer matrix materials.

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: none
Teacher disagreement score0.785
Threshold uncertainty score0.805

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.019
GPT teacher head0.288
Teacher spread0.269 · 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

Citations20
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueIEEE Transactions on Industry ApplicationsSame topicElectrospun Nanofibers in Biomedical ApplicationsFrench-language works237,207