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Record W2969909308 · doi:10.1002/adfm.201905552

Hybrid Cholesteric Films with Tailored Polarization Rotation

2019· article· en· W2969909308 on OpenAlexafffund
Pardis Rofouie, Elizabeth Galati, Lu Sun, Amr S. Helmy, Eugenia Kumacheva

Bibliographic record

VenueAdvanced Functional Materials · 2019
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials sciencePolarizerNanoparticleOptical rotationOptoelectronicsPolarization (electrochemistry)PlasmonComposite numberColloidal goldPhotonicsCircular polarizationBroadbandNanotechnologyOpticsComposite materialBirefringence

Abstract

fetched live from OpenAlex

Abstract Incorporation of metal nanoparticles in chiral (Ch) films of cellulose nanocrystals (CNCs) enhances nanoparticle plasmon resonances, due to the coupling of the intrinsic properties of the plasmonic guest and the photonic properties of the Ch‐CNC host. In contrast with previous reports, the properties of the Ch‐CNC host are focused and an effective strategy is developed for tuning the optical polarization rotation of the composite films formed by the CNCs and gold nanoparticles. A twofold enhancement in the polarization rotation power of the composite Ch‐CNC films, with an insignificant change in the incurred optical losses, is achieved by varying the density and dimensions of gold nanoparticles embedded in the Ch‐CNC films. For such films, a new approach is developed to obtain broadband circular dichroism by fabricating films from mixtures of CNC suspensions ultrasonicated for different time intervals. These new findings enable fine‐tuning of the power and spectral range of the polarization rotation and offer a novel strategy for the fabrication of broadband reflectors and polarizers, smart solar windows, and detectors for circularly polarized light.

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 categoriesInsufficient 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 score0.009
Threshold uncertainty score0.999

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.002

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 head0.241
Teacher spread0.230 · 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

Citations19
Published2019
Admission routes2
Has abstractyes

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