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Record W6884500584 · doi:10.1021/la301623h.s001

Understanding the Edge\nEffect in Wetting: A Thermodynamic\nApproach

2016· article· en· W6884500584 on OpenAlexaff

Bibliographic record

VenueFigshare · 2016
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFrustumWettingDrop (telecommunication)Enhanced Data Rates for GSM EvolutionWetting transitionContact anglePosition (finance)

Abstract

fetched live from OpenAlex

Edge effect is known to hinder spreading of a sessile\ndrop. However,\nthe underlying thermodynamic mechanisms responsible for the edge effect\nstill is not well-understood. In this study, a free energy model has\nbeen developed to investigate the energetic state of drops on a single\npillar (from upright frustum to inverted frustum geometries). An analysis\nof drop free energy levels before and after crossing the edge allows\nus to understand the thermodynamic origin of the edge effect. In particular,\nfour wetting cases for a drop on a single pillar with different edge\nangles have been determined by understanding the characteristics of\nFE plots. A wetting map describing the four wetting cases is given\nin terms of edge angle and intrinsic contact angle. The results show\nthat the free energy barrier observed near the edge plays an important\nrole in determining the drop states, i.e., (1) stable or metastable\ndrop states at the pillar’s edge, and (2) drop collapse by\nliquid spilling over the edge completely or staying at an intermediate\nsidewall position of the pillar. This thermodynamic model presents\nan energetic framework to describe the functioning of the so-called\n“re-entrant” structures. Results show good consistency\nwith the literature and expand the current understanding of Gibbs’\ninequality condition.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.281
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0810.003

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 head0.161
GPT teacher head0.269
Teacher spread0.108 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2016
Admission routes1
Has abstractyes

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