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Record W3202356826 · doi:10.21127/yaoyigc20200022

Electrospinning as an Important Tool for Fabrication of Nanofibers for Advanced Applications—a Brief Review

2021· article· en· W3202356826 on OpenAlexaff
Merin Sara Thomas, Prasanth K.S. Pillai, Scott C. Farrow, Laly A. Pothan and Sabu Thomas

Bibliographic record

VenueGeneral Chemistry · 2021
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsTrent University
Fundersnot available
KeywordsElectrospinningNanofiberFabricationNanotechnologyMaterials scienceComputer scienceComposite materialPolymerMedicine

Abstract

fetched live from OpenAlex

Nanomaterials are attracting renewed interest due to their novel properties, which are not seen in their conventional micro state counterparts. They are used in advanced applications in the fields of catalysis, medicine, electronics, optics and membranes. Electrospinning is one of the simplest and cheapest methods to make nano-porous polymer membranes, and these offer a large surface area-to-volume ratio, high porosity and small pore size. These electrospun nonwoven mats could be employed in myriad applications ranging from filtration, sensors, electrode materials, drug delivery, cosmetics, and tissue scaffolding. This technique can introduce novel functional characteristics, and hence by changing the basic experimental set up, solvent, solution and polymer characteristics can alter the composition, morphology, and porosity of the processed material. The unique structural and functional characteristics inherited at submicron to nanoscale dimensions via electrospinning makes it an attractive technique for advanced industrial applications.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.292
Threshold uncertainty score0.681

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 head0.007
GPT teacher head0.291
Teacher spread0.283 · 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
GenreMethods

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

Citations2
Published2021
Admission routes1
Has abstractyes

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