MétaCan
Menu
Back to cohort
Record W2415969157 · doi:10.1021/acs.langmuir.5b01351

Capillary Force between Flexible Filaments

2015· article· en· W2415969157 on OpenAlexafffund
Majid Soleimani, Reghan J. Hill, Theo G. M. van de Ven

Bibliographic record

VenueLangmuir · 2015
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsCapillary actionProtein filamentCapillary numberContact angleMaterials scienceFlexibility (engineering)MechanicsSurface tensionChemistryNanotechnologyComposite materialThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Liquid droplets bridging filaments are ubiquitous in nature and technology. Although the liquid-surface shape and the capillary force and torque have been studied extensively, the effect of filament flexibility is poorly understood. Here, we show that elastic deformation (at large values of the elasto-capillary number) can significantly affect the liquid-surface shape and capillary force. The equilibrium state of parallel filaments is calculated using analytical approximations and numerical solutions for the fluid interface. The results compare well, and the numerical solution is then applied to crossing filaments. In the investigated range of parameters, the capillary force increases rapidly when the filaments touch. The force decreases continuously when decreasing the liquid volume for parallel hydrophilic filaments but produces a maximum for crossed filaments. The liquid volume at the maximum force is reported when changing the filament flexibility, crossing angle, and contact angle. These results may be beneficial in applications where the strength and structure of wet fibrous materials are important, such as in paper formation and when welding flexible components.

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)0.001
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.091
GPT teacher head0.294
Teacher spread0.202 · 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

Citations21
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueLangmuirSame topicSurface Modification and SuperhydrophobicityFrench-language works237,207