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Record W2141027215 · doi:10.1002/anie.201407141

Functional Materials from Cellulose‐Derived Liquid‐Crystal Templates

2014· article· en· W2141027215 on OpenAlexaff
Michael Giese, Lina K. Blusch, Mostofa K. Khan, Mark J. MacLachlan

Bibliographic record

VenueAngewandte Chemie International Edition · 2014
Typearticle
Languageen
FieldMaterials Science
TopicLiquid Crystal Research Advancements
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsTemplateMaterials scienceNanotechnologySupramolecular chemistryCelluloseLiquid crystalAqueous solutionLiquid crystallineCrystal structureChemistryOrganic chemistryOptoelectronics

Abstract

fetched live from OpenAlex

Cellulose nanocrystals (CNCs), known for more than 50 years, have attracted attention because of their unique properties such as high specific strength and modulus, high surface area, and fascinating optical properties. Just recently, however, their potential in supramolecular templating was identified by making use of their self-assembly behavior in aqueous dispersions in the presence of compatible precursors. The combination of the mesoporosity, photonic properties, and chiral nematic order of the materials, which are available as freestanding films, has led to a significant number of interesting and promising discoveries towards new functional materials. This Review summarizes the use of cellulose derivatives, especially CNCs, as novel templates and gives an overview of the recent developments toward new functional materials.

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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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)0.0030.002

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 head0.022
GPT teacher head0.268
Teacher spread0.246 · 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

Citations390
Published2014
Admission routes1
Has abstractyes

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