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Record W2336427463 · doi:10.1039/c6sm00295a

Dynamics of ordered colloidal particle monolayers at nematic liquid crystal interfaces

2016· article· en· W2336427463 on OpenAlexaff
Wei-Shao Wei, Mohamed Amine Gharbi, Matthew Lohr, Tim Still, Matthew Gratale, T. C. Lubensky, Kathleen J. Stebe, Arjun G. Yodh

Bibliographic record

VenueSoft Matter · 2016
Typearticle
Languageen
FieldMaterials Science
TopicLiquid Crystal Research Advancements
Canadian institutionsMcGill University
FundersMaterials Research Science and Engineering Center, Harvard UniversityUniversity of PennsylvaniaNational Science Foundation
KeywordsLiquid crystalMaterials scienceParticle (ecology)Chemical physicsDiffusionViscosityMonolayerIsotropyPhononColloidal crystalCondensed matter physicsDisplacement (psychology)Phase (matter)ColloidOpticsChemistryNanotechnologyThermodynamicsComposite materialPhysicsPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

We prepare two-dimensional crystalline packings of colloidal particles on surfaces of the nematic liquid crystal (NLC) 5CB, and we investigate the diffusion and vibrational phonon modes of these particles using video microscopy. Short-time particle diffusion at the air-NLC interface is well described by a Stokes-Einstein model with viscosity similar to that of 5CB. Crystal phonon modes, measured by particle displacement covariance techniques, are demonstrated to depend on the elastic constants of 5CB through interparticle forces produced by LC defects that extend from the interface into the underlying bulk material. The displacement correlations permit characterization of transverse and longitudinal sound velocities of the crystal packings, as well as the particle interactions produced by the LC defects. All behaviors are studied in the nematic phase as a function of increasing temperature up to the nematic-isotropic transition.

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 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 score0.006
Threshold uncertainty score0.999

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)0.0080.002

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 head0.273
Teacher spread0.259 · 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

Citations11
Published2016
Admission routes1
Has abstractyes

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