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Record W2157636478 · doi:10.1504/ijecb.2009.022858

Modelling of mechanical properties of electrospun nanofibre network

2009· article· en· W2157636478 on OpenAlexfundno aff
Xiaofan Wei, Zhenhai Xia, Shing Chung Josh Wong, Avinash Baji

Bibliographic record

VenueInternational Journal of Experimental and Computational Biomechanics · 2009
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsnot available
FundersDivision of Civil, Mechanical and Manufacturing InnovationOffice of Naval ResearchMcGill UniversityNational Science Foundation
KeywordsMaterials sciencevan der Waals forceComposite materialFusionTorsion (gastropod)Ultimate tensile strengthStrain energyDeformation (meteorology)ElectrospinningFracture mechanicsPolymerStructural engineeringFinite element method

Abstract

fetched live from OpenAlex

Electrospun nanofibres are widely investigated as extra-cellular matrix for tissue engineering and biomedical applications. Little is understood on the deformation mechanics of spun fibre mats. A model is developed to predict the deformation behaviour of randomly-oriented electrospun nanofibre network/mats with the fibre-fibre fusion and van der Waals interaction. The nanofibres in the mat are represented by chains of beads; the interactions between the beads are described by bonded (stretch, bending and torsion) and non-bonded (van der Waals) potentials. Stress-strain curves and dynamics fracture are predicted by this model. The results show that the fibre-fibre fusion has a significant effect on the tensile strength of the mats. Increasing the number of fusion points in the mat results in an increase in strength, but over-fusion may lead to lower fracture energy. The predicted stress-strain relationships are consistent with the experimental results.

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 score0.356
Threshold uncertainty score0.390

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 head0.018
GPT teacher head0.261
Teacher spread0.242 · 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

Citations74
Published2009
Admission routes1
Has abstractyes

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Same venueInternational Journal of Experimental and Computational BiomechanicsSame topicElectrospun Nanofibers in Biomedical ApplicationsFrench-language works237,207