MétaCan
Menu
Back to cohort
Record W3202932305 · doi:10.1021/acs.biomac.1c00595

Chiral Cellulose Nanocrystal Humidity-Responsive Iridescent Films with Glucan for Tuned Iridescence and Reinforced Mechanics

2021· article· en· W3202932305 on OpenAlexaff
Yahui Meng, Zhu Long, Zhibin He, Xiaotong Fu, Cuihua Dong

Bibliographic record

VenueBiomacromolecules · 2021
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of New Brunswick
FundersState Key Laboratory of Biobased Material and Green Papermaking, Qilu University of TechnologyGovernment of Jiangsu Province
KeywordsIridescenceStructural colorationComposite numberMaterials scienceCelluloseCoatingComposite materialNanocrystalFabricationNanotechnologyPolymerPhotonic crystalChemical engineeringOptoelectronicsOptics

Abstract

fetched live from OpenAlex

The fabrication of biomimetic photonic materials with environmental stimuli-responsive functions from entirely biobased materials is becoming increasingly challenging with the growing demand for biodegradable materials. Herein, the effect of glucan with different molecular weights on the mechanical performance and tunable structural color of iridescent CNC composite films was investigated. The existence of glucan did not influence the self-assembly performance of CNCs, but rather led to an improvement in the mechanical performance, enabling cholesteric CNC composite films with an adjustable structural color. Simultaneously, the iridescent films showed a conspicuous redshift and enlarged initial pitch without obstruction of the chiral structure. In response to environmental humidity, the structural colors of the iridescent composite films can be changed by regulating their chiral nematic structure. In particular, the films demonstrate a reversible structural color change between blue and red at RH between 50 and 98%. The resulting biobased iridescent composite films have potential applications in decorative coating, optical and humidity sensing, and anticounterfeiting.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.007
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0000.000

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 head0.016
GPT teacher head0.263
Teacher spread0.247 · 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.

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

Citations39
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueBiomacromoleculesSame topicAdvanced Cellulose Research StudiesFrench-language works237,207