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Record W1973101794 · doi:10.1039/c4sm02371d

Nano-scale surface wrinkling in chiral liquid crystals and plant-based plywoods

2014· article· en· W1973101794 on OpenAlexafffund
Pardis Rofouie, Damiano Pasini, Alejandro D. Rey

Bibliographic record

VenueSoft Matter · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials and Mechanics
Canadian institutionsMcGill University
FundersMcGill University
KeywordsCurvatureSurface tensionHomeotropic alignmentScalingMaterials scienceLiquid crystalCapillary actionSurface (topology)AnisotropyDilation (metric space)OpticsGeometryPhysicsComposite materialThermodynamicsMathematics

Abstract

fetched live from OpenAlex

We present theoretical scaling and computational analysis of nanostructured free surfaces formed in chiral liquid crystals (LC) and plant-based twisted plywoods. A nemato-capillary model is used to derive a generalized equation that governs the shape of cholesteric free surfaces. It is shown that the shape equation includes three distinct contributions to the capillary pressure: area dilation, area rotation, and director curvature. To analyse the origin of periodic reliefs in plywood surfaces, these three pressure contributions and corresponding surface energies are systematically investigated. It is found that for weak homeotropic surface anchoring, the nano-wrinkling is driven by the director curvature pressure mechanism. Consequently, the model predicts that for a planar surface with a uniform tangential helix vector, no surface nano-scale wrinkling can be observed because the director curvature pressure is zero. Scaling is used to derive the explicit relation between the wrinkling's amplitude to the wavelength ratio as a function of the anisotropic surface tension, which is then validated with experimental values. These new findings can be used to characterize plant-based twisted plywoods, as well as to inspire the design of biomimetic chiro-optical devices.

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 score0.050
Threshold uncertainty score0.469

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)0.0000.000

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 head0.005
GPT teacher head0.188
Teacher spread0.183 · 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

Citations23
Published2014
Admission routes2
Has abstractyes

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