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Record W3118880580 · doi:10.1039/d0sm02106g

Evaporation-induced alterations in oscillation and flow characteristics of a sessile droplet on a rose-mimetic surface

2021· article· en· W3118880580 on OpenAlexaff
Prasenjit Kabi, Vishank Razdan, Durbar Roy, Lalit Bansal, Sumita Sahoo, Rabibrata Mukherjee, Swetaprovo Chaudhuri, Saptarshi Basu

Bibliographic record

VenueSoft Matter · 2021
Typearticle
Languageen
FieldEngineering
TopicNanomaterials and Printing Technologies
Canadian institutionsUniversity of TorontoToronto Public Health
Fundersnot available
KeywordsOscillation (cell signaling)EvaporationMechanicsFlow (mathematics)Rose (mathematics)Solid surfaceMaterials scienceChemistryChemical physicsThermodynamicsPhysicsBiology

Abstract

fetched live from OpenAlex

Strategic control of evaporation dynamics can help control oscillation modes and internal flow field in an oscillating sessile droplet. This article presents the study of an oscillating droplet on a bio-inspired "sticky" surface to better understand the nexus between the modes of evaporation and oscillation. Oscillation in droplets can be characterized by the number of nodes forming on the surface and is referred to as the mode of oscillation. An evaporating sessile droplet under constant periodic perturbation naturally self-tunes between different oscillation modes depending on its geometry. The droplet geometry evolves according to the mode of evaporation controlled by substrate topography. We use a bio-inspired, rose patterned, "sticky" hydrophobic substrate to perpetually pin the contact line of the droplet in order to hence achieve a single mode of evaporation for most of the droplet's lifetime. This allows the prediction of experimentally observed oscillation mode transitions at different excitation frequencies. We present simple scaling arguments to predict the velocity of the internal flow induced by the oscillation. The findings are beneficial to applications which seek to tailor energy and mass transfer rates across liquid droplets by using bio-inspired surfaces.

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 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 score0.291
Threshold uncertainty score0.287

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 head0.011
GPT teacher head0.219
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, 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

Citations2
Published2021
Admission routes1
Has abstractyes

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