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Record W2089750940 · doi:10.1021/la804095y

Contact Line Pinning by Microfabricated Patterns: Effects of Microscale Topography

2009· article· en· W2089750940 on OpenAlexaff
Yevgeniy V. Kalinin, Viatcheslav Berejnov, Robert Thorne

Bibliographic record

VenueLangmuir · 2009
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversity of Victoria
FundersNational Institute of General Medical Sciences
KeywordsMicroscale chemistryContact angleMaterials scienceHysteresisWaferDrop (telecommunication)WettingLine (geometry)Surface (topology)MechanicsPolymerRing (chemistry)OpticsGeometryCondensed matter physicsComposite materialNanotechnologyChemistryPhysics

Abstract

fetched live from OpenAlex

We study how the microscale topography of a solid surface affects the apparent advancing and receding angles at the contact line of a liquid drop pinned to this surface. Photolithographic methods are used to produce continuous circular polymer rings of varying cross-sectional size and shape on hydrophilic silicon wafer surfaces. Drops of water and glycerol are dispensed into the areas bounded by these rings, and critical apparent advancing and receding angles are measured and correlated with the parameters that characterize the ring cross section. For much of the examined parameter space, the apparent critical angles are independent of ring height and width and are determined primarily by the slope of the ring's sidewalls, consistent with a model by Gibbs. For ring heights below a few micrometers, the critical angles decrease below the values predicted by the sidewall slopes alone. These results provide data for calculation of hysteresis on naturally rough surfaces and demonstrate a simple method for controlling and enhancing contact line pinning on solid 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.006
GPT teacher head0.228
Teacher spread0.222 · 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 source (direct Gemma or distilled Codex), 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

Citations76
Published2009
Admission routes1
Has abstractyes

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