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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 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

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.021
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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