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Record W3006184856 · doi:10.1021/acs.biomac.0c00056

Iridescent Cellulose Nanocrystal Films Modified with Hydroxypropyl Cellulose

2020· article· en· W3006184856 on OpenAlexafffund
Christopher M. Walters, Charlotte E. Boott, Thanh‐Dinh Nguyen, Wadood Y. Hamad, Mark J. MacLachlan

Bibliographic record

VenueBiomacromolecules · 2020
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsFPInnovationsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaKillam TrustsCanada Foundation for Innovation
KeywordsHydroxypropyl celluloseMaterials scienceCelluloseNanocrystalPolymerLiquid crystalComposite numberUltimate tensile strengthChemical engineeringMethacrylateMethyl methacrylateEvaporationThin filmComposite materialPolymer chemistryNanotechnologyOptoelectronicsCopolymer

Abstract

fetched live from OpenAlex

The introduction of polymers into a chiral nematic cellulose nanocrystal (CNC) matrix allows for the tuning of optical and mechanical properties, enabling the development of responsive photonic materials. In this study, we explored the incorporation of hydroxypropyl cellulose (HPC) into a CNC film prepared by slow evaporation. In the composite CNC/HPC thin films, the CNCs adopt a chiral nematic structure, which can selectively reflect certain wavelengths of light to yield a colored film. The color could be tuned across the visible spectrum by changing the concentration or molecular weight of the HPC. Importantly, the composite films were more flexible than pure CNC films with up to a ten-fold increase in elasticity and a decrease in stiffness and tensile strength of up to six times and four times, respectively. Surface modification of the films with methacrylate groups increased the hydrophobicity of the films, and therefore, the water stability of these materials was also improved.

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 score0.002
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 head0.024
GPT teacher head0.254
Teacher spread0.229 · 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

Citations103
Published2020
Admission routes2
Has abstractyes

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