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Record W2785110079 · doi:10.1139/tcsme-2017-1011

SURFACE AZIMUTHAL ANCHORING ENERGY BETWEEN THE GRATING SURFACE AND NEMATIC LIQUID CRYSTAL LAYER STUDIED BY USING A GEOMETRIC MODEL

2017· article· en· W2785110079 on OpenAlexvenueno aff
C.Y. Lin, Tei-Chen Chen

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2017
Typearticle
Languageen
FieldMaterials Science
TopicLiquid Crystal Research Advancements
Canadian institutionsnot available
Fundersnot available
KeywordsLiquid crystalAnchoringAzimuthGratingMaterials scienceOpticsLiquid-crystal displayPhysics

Abstract

fetched live from OpenAlex

A simple geometric model is proposed for estimating the azimuthal anchoring energy between the sinusoidal relief grating surface and nematic liquid crystal layer of a liquid crystal display (LCD) device as a function of the grating height and grating pitch. The model parameters are determined experimentally, and the model is then used to predict the surface azimuthal anchoring energy for gratings with various pitches and heights. It is shown that a good agreement exists between the predicted results for the surface azimuthal anchoring energy and the experimental data. Moreover, a good agreement is also observed between the estimated results and those obtained from Berreman’s expression and finite element method (FEM) simulations, respectively. Overall, the experimental and numerical results show that for the nematic liquid crystal considered in the present study (4-n-pentyl-4’-cyanobiphenyl (5CB)), the surface azimuthal anchoring energy increases with an increasing grating height or a reducing grating pitch.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.047
GPT teacher head0.289
Teacher spread0.242 · 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 designSimulation or modeling
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

Citations1
Published2017
Admission routes1
Has abstractyes

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