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Record W4283724167 · doi:10.1016/j.jcis.2022.06.103

Wicking through complex interfaces at interlacing yarns

2022· article· en· W4283724167 on OpenAlexaff
R. Fischer, Christian M. Schlepütz, René M. Rossi, Dominique Derome, Jan Carmeliet

Bibliographic record

VenueJournal of Colloid and Interface Science · 2022
Typearticle
Languageen
FieldMaterials Science
TopicTextile materials and evaluations
Canadian institutionsUniversité de Sherbrooke
FundersPaul Scherrer Institut
KeywordsMaterials scienceYarnCapillary actionComposite materialPorosityTransition zoneMaxima and minimaMechanicsGeologyPhysics

Abstract

fetched live from OpenAlex

HYPOTHESIS: Wicking flow in the wale direction of knit fabrics is slowed by capillary pressure minima during the transition at yarn contacts. The characteristic pore structure of yarns leads to an unfavorable free energy evolution and is the cause of these minima. EXPERIMENTS: Time-resolved synchrotron tomographic microscopy is employed to study the evolution of water configuration during wicking flow in interlacing yarns. Dynamic pore network modeling is used based on the obtained image data and distributions of delay times for pore intrusion. Good agreement is observed by comparison to the experimental data. FINDINGS: Yarn-to-yarn transition is found to coincide with slow water advance in a thin interface zone at the yarn contact. The pore spaces of the two yarns merge within this interface zone and provide a transition path. A deep capillary pressure minimum occurs while water passes through the center of the interface zone, effectively delaying the wicking flow. A pore network model considering pore intrusion delay times is expanded to include inter-yarn wicking and reproduce the observed wicking dynamics.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.039
GPT teacher head0.320
Teacher spread0.281 · 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 source (direct Gemma or distilled Codex), 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

Citations15
Published2022
Admission routes1
Has abstractyes

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