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Record W4211239214 · doi:10.3390/fib10020015

Application of Electrospun Nonwoven Fibers in Air Filters

2022· article· en· W4211239214 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFibers · 2022
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsElectrospinningNonwoven fabricMaterials scienceAir filterAir filtrationFiltration (mathematics)Composite materialNanofiberAir permeability specific surfaceCeramicFiberFilter (signal processing)Pressure dropParticulatesPolymerMechanical engineeringComputer scienceEngineering

Abstract

fetched live from OpenAlex

Air filtration has seen a sizable increase in the global market this past year due to the COVID-19 pandemic. Nanofiber nonwoven mats are able to reach certain efficiencies with a low-pressure drop, have a very high surface area to volume ratio, filter out submicron particulates, and can customize the fiber material to better suit its purpose. Although electrospinning nonwoven mats have been very well studied and documented there are not many papers that combine them. This review touches on the various ways to manufacture nonwoven mats for use as an air filter, with an emphasis on electrospinning, the mechanisms by which the fibrous nonwoven air filter stops particles passing through, and ways that the nonwoven mats can be altered by morphology, structure, and material parameters. Metallic, ceramic, and organic nanoparticle coatings, as well as electrospinning solutions with these same materials and their properties and effects of air filtration, are explored.

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.

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)0.001
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 score0.125
Threshold uncertainty score0.819

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.224
Teacher spread0.220 · 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