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Record W3210240917 · doi:10.1039/d1sm01462e

Multiple droplets on a conical fiber: formation, motion, and droplet mergers

2022· article· en· W3210240917 on OpenAlex
Carmen Lee, Tak Shing Chan, Andreas Carlson, Kari Dalnoki‐Veress

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueSoft Matter · 2022
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaNorges Forskningsråd
KeywordsConical surfaceFiberMaterials scienceSurface tensionWettingMechanicsCapillary actionRayleigh scatteringViscosityRayleigh–Taylor instabilityOpticsInstabilityComposite materialPhysicsThermodynamics

Abstract

fetched live from OpenAlex

Small droplets on slender conical fibers spontaneously move along the fiber due to capillary action. The droplet motion depends on the geometry of the cone, the surface wettability, the surface tension, the viscosity, and the droplet size. Here we study with experiments and numerical simulations, the formation, spontaneous motion, and the eventual merger, of multiple droplets on slender conical fibers as they interact with each other. The droplet size and their spacing on the fibre is controlled by the Plateau-Rayleigh instability after dip-coating the conical fiber. Once these droplets are formed on the fiber, they spontaneously start to move. Since droplets of different size move with different speeds, they effectively coarsen the droplet patterning by merging on the fiber. The droplet merging process affects locally the droplet speed and alters the spatiotemporal film deposition on the fiber.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.490
Threshold uncertainty score0.998

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)0.0210.003

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 head0.015
GPT teacher head0.222
Teacher spread0.207 · 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