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Record W2150528957 · doi:10.1039/c5nr00660k

Structure and properties of composite films formed by cellulose nanocrystals and charged latex nanoparticles

2015· article· en· W2150528957 on OpenAlexafffund
Héloïse Thérien‐Aubin, Ariella Lukach, Natalie Pitch, Eugenia Kumacheva

Bibliographic record

VenueNanoscale · 2015
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceComposite numberLiquid crystalNanoparticleNanocrystalChemical engineeringCellulosePhase (matter)BirefringenceCircular dichroismPolymerComposite materialNanotechnologyCrystallographyOrganic chemistryOpticsChemistry

Abstract

fetched live from OpenAlex

We report the structural and optical properties of composite films formed from mixed suspensions of cellulose nanocrystals (CNCs) and fluorescent latex nanoparticles (NPs). We explored the effect of NP concentration, size, surface charge, glass transition temperature and film processing conditions on film structure and properties. The chiral nematic order, typical of CNC films, was preserved in films with up to 50 wt% of negatively-charged latex NPs. Composite films were characterized by macroscopically close-to-uniform fluorescence, birefringence, and circular dichroism properties. In contrast, addition of positively charged latex NPs led to gelation of CNC-latex suspensions and disruption of the chiral nematic order in the composite films. Large latex NPs disrupted the chiral nematic order to a larger extend than small NPs. Furthermore, the glass transition of latex NPs had a dramatic effect on the structure of CNC-latex films. Latex particles in the rubbery state were easily incorporated in the ordered CNC matrix and improved the structural integrity of its chiral nematic phase.

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 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 score0.007
Threshold uncertainty score0.463

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 communication0.0000.000
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 head0.025
GPT teacher head0.256
Teacher spread0.232 · 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.

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

Citations47
Published2015
Admission routes2
Has abstractyes

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