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Record W2802630929 · doi:10.1002/adfm.201800207

Hydrogen‐Bonded Liquid Crystals in Confined Spaces—Toward Photonic Hybrid Materials

2018· article· en· W2802630929 on OpenAlexafffund
Matthias Spengler, Ronald Y. Dong, Carl A. Michal, Wadood Y. Hamad, Mark J. MacLachlan, Michael Giese

Bibliographic record

VenueAdvanced Functional Materials · 2018
Typearticle
Languageen
FieldMaterials Science
TopicLiquid Crystal Research Advancements
Canadian institutionsFPInnovationsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceLiquid crystalMesoporous materialPhase (matter)Differential scanning calorimetryPhotonicsChemical engineeringChemical physicsNanotechnologyOptoelectronicsOrganic chemistryThermodynamics

Abstract

fetched live from OpenAlex

Abstract A series of hybrid materials based on chiral nematic mesoporous organosilica (CNMO) films infiltrated with liquid crystalline hydrogen‐bonded assemblies is prepared and characterized with respect to the mutual manipulation of the photonic properties of the host and the liquid‐crystalline behavior of the guest. Detailed differential scanning calorimetry studies reveal the impact of confinement on the mesomorphic behavior of the liquid crystalline assemblies in the pores of the CNMO films. The photonic properties of the chiral nematic mesoporous host can be controlled by changing the temperature or irradiating the films with UV light. These stimuli‐induced phase transitions are accompanied by changes in the orientational order of the mesogens as revealed by 19 F NMR spectroscopy. The combination of confinement and changes in the molecular orientation in a unique hybrid material based on hydrogen‐bonded liquid crystals and a porous host with a chiral nematic mesostructure is an interesting concept for the design of optical sensors, reflectors, or filters.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.038
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0410.004

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 head0.026
GPT teacher head0.286
Teacher spread0.261 · 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; both teacher heads agree on what is shown here.

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

Citations33
Published2018
Admission routes2
Has abstractyes

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