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Record W2952194161 · doi:10.48550/arxiv.1412.5269

Design of a robust superhydrophobic surface: thermodynamic and kinetic analysis

2014· preprint· en· W2952194161 on OpenAlexaff
Anjishnu Sarkar, Anne‐Marie Kietzig

Bibliographic record

VenuearXiv (Cornell University) · 2014
Typepreprint
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsMcGill University
Fundersnot available
KeywordsRobustness (evolution)WettingKinetic energyMechanicsMaterials scienceComputer scienceMechanical engineeringClassical mechanicsEngineeringPhysicsChemistryComposite material

Abstract

fetched live from OpenAlex

The design of a robust superhydrophobic surface is a widely pursued topic.While many investigations are limited to applications with high impact velocities (for raindrops of the order of a few m/s), the essence of robustness is yet to be analyzed for applications involving quasi-static liquid transfer.To achieve robustness with high impact velocities, the surface parameters (geometrical details, chemistry) have to be selected from a narrow range of permissible values, which often entail additional manufacturing costs.From the dual perspectives of thermodynamics and mechanics, we analyze the significance of robustness for quasi-static drop impact, and present the range of permissible surface characteristics.For surfaces with a Youngs contact angle greater than 90° and square micropillar geometry, we show that robustness can be enforced when an intermediate wetting state (sagged state) impedes transition to a wetted state (Wenzel state).From the standpoint of mechanics, we use available scientific data to prove that a surface with any topology must withstand a pressure of 117 Pa to be robust.Finally, permissible values of surface characteristics are determined, which ensure robustness with thermodynamics (formation of sagged state) and mechanics (withstanding 117 Pa).

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.255
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.069
GPT teacher head0.187
Teacher spread0.118 · 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.

Study designSimulation or modeling
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

Citations0
Published2014
Admission routes1
Has abstractyes

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