MétaCan
Menu
Back to cohort
Record W3203210015 · doi:10.1080/01411594.2021.1979974

Nonlinear optical properties of nematic liquid crystal matrix doped with graphene nanosheets

2021· article· en· W3203210015 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePhase Transitions · 2021
Typearticle
Languageen
FieldMaterials Science
TopicLiquid Crystal Research Advancements
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsLiquid crystalMaterials scienceGrapheneBirefringenceDielectricDopingRefractive indexPolarization (electrochemistry)Phase transitionOpticsAnisotropyCondensed matter physicsOptoelectronicsNanotechnologyChemistryPhysical chemistryPhysics

Abstract

fetched live from OpenAlex

In this research, nonlinear optical (NLO) properties of the nematic liquid crystal doped with different concentrations of the graphene nanosheets are studied. The nematic liquid crystal molecules are aligned in parallel and perpendicular to the incident light polarization and accordingly order parameters of the aligned samples are studied using the dichroism method. We show how doped graphene nanosheets increase the nonlinear birefringence and nonlinear refractive index of the graphene guest-nematic liquid crystal host. Furthermore, we use LCR meter to obtain dielectric anisotropy of the mixture at the frequency of 1 kHz with varying temperatures. Obtained experimental results demonstrate doping 1% concentration of the graphene nanosheets increases significantly NLO properties and also the dielectric response of the nematic liquid crystal by the magnitude of two orders and shifts the phase transition point by about 15°.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score1.000

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)0.0010.000

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 head0.031
GPT teacher head0.317
Teacher spread0.287 · 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