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Record W2902405518 · doi:10.1177/1461348418813256

The effect of sonic vibration on electrospun fiber mats

2018· article· en· W2902405518 on OpenAlexaff
Xiaoxia Li, Jing Qiang, Yuqin Wan, Hongbo Wang, Weidong Gao

Bibliographic record

VenueJournal of low frequency noise, vibration and active control · 2018
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsUniversity of British Columbia
FundersNational Natural Science Foundation of China
KeywordsPolyacrylonitrileMaterials scienceFiberElectrospinningComposite materialNanofiberVibrationFiltration (mathematics)Morphology (biology)PolymerAcoustics

Abstract

fetched live from OpenAlex

Electrospun nanofibers have shown great potential in wide applications such as energy, filtration, medical devices, and sensors. For all these applications, fiber morphology, fiber diameter and pore size of electrospun fiber mat play critical roles in determining the performance of the final products. Research has proved that sonic vibration can dramatically improve the electrospinnability of polymer solutions and subsequently the resultant fiber properties. In this work, the effects of sonic vibration on fiber morphology as well as fiber diameter and the pore structures of electrospun polyacrylonitrile fiber mat were investigated. The relationships between sonic vibration and fiber diameter, and pore size of the electrospun fiber mats were also studied.

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)0.001
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 score0.068
Threshold uncertainty score0.387

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.003
GPT teacher head0.239
Teacher spread0.236 · 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

Citations26
Published2018
Admission routes1
Has abstractyes

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