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Record W2752582607 · doi:10.1039/c7cp04421f

Dynamics of ethyl cellulose nanoparticle self-assembly at the interface of a nematic liquid crystal droplet

2017· article· en· W2752582607 on OpenAlexafffund
Yining Han, Navid Bizmark, Nasser Mohieddin Abukhdeir, Marios A. Ioannidis

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2017
Typearticle
Languageen
FieldMaterials Science
TopicPickering emulsions and particle stabilization
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLiquid crystalNanoparticleMaterials scienceEthyl celluloseCelluloseChemical engineeringMolecular dynamicsDynamics (music)Liquid liquidLiquid crystallineInterface (matter)Self-assemblyChemical physicsNanotechnologyOrganic chemistryChemistryChromatographyComposite materialComputational chemistryPolymerOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

Design and fabrication of many next-generation liquid crystal (LC)-based devices rely on nematic LC domains in the form of drops or emulsions. In addition to surfactants, solid nanoparticles may be used to stabilize LC-in-water Pickering emulsions, possibly adding new dimensions to device functionality. In this work we quantitatively study the adsorption of ethyl cellulose (EC) nanoparticles, as a colloid model system, on the 4-cyano-4'-pentylbiphenyl (5CB)-water interface via a series of dynamic interfacial tension measurements. It is found that the planar alignment of 5CB molecules at the interface with water is unaffected by particle adsorption, but a significant reduction of the interfacial tension over time occurs. It is also found that adsorption of EC nanoparticles to the LC-water interface is irreversible and results in close hexagonal packing. This study demonstrates a systematic approach to quantitatively investigate the effect of nanoparticles on the stabilization of LC emulsions.

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 score0.039
Threshold uncertainty score0.543

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 science0.0010.001
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 head0.012
GPT teacher head0.281
Teacher spread0.269 · 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

Citations11
Published2017
Admission routes2
Has abstractyes

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