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Record W2038655719 · doi:10.1098/rsta.2012.0256

Hydrophobic gold nanoparticles via silane conjugation: chemically and thermally robust nanoparticles as dopants for nematic liquid crystals

2013· article· en· W2038655719 on OpenAlexaff
Javad Mirzaei, Martin Urbanski, Heinz‐S. Kitzerow, Torsten Hegmann

Bibliographic record

VenuePhilosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · 2013
Typearticle
Languageen
FieldMaterials Science
TopicLiquid Crystal Research Advancements
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsLiquid crystalMaterials scienceDopantColloidal goldNanoparticleDielectricPhase (matter)Chemical engineeringPhase transitionSilaneAnisotropyDopingOrganic chemistryNanotechnologyOpticsComposite materialOptoelectronicsChemistryCondensed matter physics

Abstract

fetched live from OpenAlex

We examine for the first time how chemically and thermally stable gold nanoparticles (NPs), prepared by a silane conjugation approach, affect both the thermal and the electro-optical properties of a nematic liquid crystal (LC), when doped at concentrations ranging from 0.25 to 7.5 wt%. We find that the octadecylsilane-conjugated gold NPs stabilize both the enantiotropic nematic and the monotropic smectic-A phases of the LC host with a maximum stabilization of 2(°)C for the nematic and 3.5(°)C for the smectic-A phases for the mixture containing 1 wt% of the silanized particles. The same mixture shows the lowest values for the Fréedericksz transition threshold voltage and the highest value for the dielectric anisotropy. Generally, all NP-containing mixtures, except mixtures with NP concentrations exceeding 5 wt%, reduce the threshold voltage, increase the dielectric anisotropy and reduce both rise and decay time; the latter particularly at temperatures at least 10(°)C below the isotropic-nematic phase transition on cooling.

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 score0.536
Threshold uncertainty score0.522

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 studies0.0000.001
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 head0.020
GPT teacher head0.250
Teacher spread0.229 · 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

Citations37
Published2013
Admission routes1
Has abstractyes

Explore more

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