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Record W3092345591 · doi:10.1016/j.powtec.2020.10.016

Effects of surface roughness on liquid bridge capillarity and droplet wetting

2020· article· en· W3092345591 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

VenuePowder Technology · 2020
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsMcGill University
FundersEuropean Commission
KeywordsWettingCapillary actionSurface roughnessMaterials scienceSurface finishContact angleSandpaperLaplace pressureComposite materialOpticsSurface tensionThermodynamicsPhysics

Abstract

fetched live from OpenAlex

This study examines the influence of surface roughness on the capillarity of liquid bridges between two solid surfaces and the wettability of droplets on solid surfaces. For this purpose, different surface roughnesses were prepared by gluing waterproof sandpaper onto flat glass surfaces. The effects of surface roughness on liquid bridge capillarity were investigated by using a method that is based on an exact analytical solution of the Young-Laplace equation for a liquid bridge between two parallel planes coupled with capillary force measurement. The capillary forces between two parallel planes were measured by a micro-balance, while the geometries of the capillary bridge were recorded using a high-resolution camera. Using the images of capillary bridges, the meridional profiles of capillary bridges were determined by a high-resolution image processing technique and correlated to the measured capillary forces. The effects of surface roughness on droplet wetting were measured by employing the same high-resolution image processing technique. The measured results show that as the roughness increases, the wetting angle increases, whereas the capillary forces of the liquid bridge decrease.

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)0.000
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 score0.007
Threshold uncertainty score0.502

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 head0.017
GPT teacher head0.240
Teacher spread0.223 · 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