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Record W2317933622 · doi:10.1021/mz400464d

Iridescent Chiral Nematic Cellulose Nanocrystal/Polymer Composites Assembled in Organic Solvents

2013· article· en· W2317933622 on OpenAlexaff
Clement C. Y. Cheung, Michael Giese, Joel A. Kelly, Wadood Y. Hamad, Mark J. MacLachlan

Bibliographic record

VenueACS Macro Letters · 2013
Typearticle
Languageen
FieldMaterials Science
TopicLiquid Crystal Research Advancements
Canadian institutionsFPInnovationsUniversity of British Columbia
Fundersnot available
KeywordsMaterials scienceLiquid crystalPolymerNanocrystalLyotropicComposite numberCounterionDispersion (optics)Chemical engineeringCelluloseCastingSelf-assemblyIonic liquidSolventComposite materialPolymer chemistryOrganic chemistryNanotechnologyLiquid crystallineOpticsChemistryIonOptoelectronics

Abstract

fetched live from OpenAlex

We describe an approach to prepare polymer composites with chiral nematic photonic structures through the self-assembly of cellulose nanocrystal (CNC) dispersions in organic solvents. Contrary to previous reports, we demonstrate that dispersions of neutralized sulfated CNCs in polar organic media readily form lyotropic chiral nematic liquid crystalline phases. We have investigated the effect of the neutralizing base on the CNC self-assembly, observing chiral nematic ordering for all counterions studied. The self-assembly of the organic CNC dispersions can be exploited to prepare iridescent polymeric composites simply by casting the CNC dispersion with a suitable polymer soluble in the organic solvent. Photonic properties of the composite films can be easily controlled by either varying the ratio of CNCs to polymer or adding salts.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 head0.012
GPT teacher head0.253
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), 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

Citations120
Published2013
Admission routes1
Has abstractyes

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