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Record W1607759602 · doi:10.1109/icmens.2004.1508918

Liquid Nanostructures: Phase Transitions, Forces and Friction

2006· article· en· W1607759602 on OpenAlexaff
Christopher Hemming, S. D. Overduin, G. N. Patey

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicnanoparticles nucleation surface interactions
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNanoscopic scaleNanostructurePerpendicularMaterials scienceMolecular dynamicsPhase (matter)Monte Carlo methodChemical physicsBinary numberNanotechnologyCondensed matter physicsPhysicsChemistryComputational chemistryGeometry

Abstract

fetched live from OpenAlex

Summary form only given, as follows. Grand canonical Monte Carlo calculations and molecular dynamics simulations are used to investigate binary mixtures confined between chemically patterned surfaces. For certain patterns and surface separations, a surfaceinduced phase consisting of liquid "bridges" joining like-patterned regions of the plates is observed. Furthermore, since these bridges involve liquid-liquid interfaces their shape and extent can be dramatically altered by simply adding an appropriate surfactant to the mixture. The nanoscale liquid structures associated with a bridge phase can strongly influence both the perpendicular and frictional forces acting on the plates at separations well beyond those where molecular "ordering" is dominant. The origin, nature and interesting physical implications of these forces will be discussed.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score0.996

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)0.0050.000

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 head0.007
GPT teacher head0.227
Teacher spread0.219 · 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 designObservational
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
Published2006
Admission routes1
Has abstractyes

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