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Record W2033414308 · doi:10.1063/1.1825373

Nematic fluid structure in wall-field geometry

2004· article· en· W2033414308 on OpenAlexaff
T. G. Sokolovska, R. O. Sokolovskii, G. N. Patey

Bibliographic record

VenueThe Journal of Chemical Physics · 2004
Typearticle
Languageen
FieldMaterials Science
TopicLiquid Crystal Research Advancements
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLiquid crystalPerpendicularCondensed matter physicsField (mathematics)WettingParticle (ecology)Orientation (vector space)Surface (topology)Materials scienceBiaxial nematicTopological defectGeometryMechanicsPhysicsGeologyComposite materialMathematics

Abstract

fetched live from OpenAlex

We describe an integral equation method for obtaining the distribution of a nematic fluid near a wall and interacting with a uniform orienting field. Complete density-orientational profiles are calculated for a model nematic with different wall-particle interactions and different orientations of the wall with respect to the field. For orienting walls we identify particular long-range correlations that are responsible for reorientation of the bulk nematic at zero external field. These correlations become stronger as the wall-particle interaction is increased in range; they become longer ranged as the orienting field is weakened. Special attention is focused on systems where the wall-particle interaction favors orientations perpendicular to the surface. The local director orientation can vary discontinuously with the distance from the surface when the orienting influences of the field and the wall are antagonistic. At high densities smectic-like structures appear. Adsorption phenomena are also discussed. For inert hard walls, the ordered fluid avoids the surface, and a surface layer where the particles tend to orient perpendicular to the bulk director appears. Experimentally, this might be seen as wetting of the wall by a less-ordered fluid.

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 score0.104
Threshold uncertainty score0.226

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 science0.0010.000
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 head0.014
GPT teacher head0.291
Teacher spread0.277 · 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

Citations9
Published2004
Admission routes1
Has abstractyes

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