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Record W4367721970 · doi:10.1039/d3dt00498h

Halloysite nanotubes enhance the mechanical properties and thermal stability of iridescent cellulose nanocrystal films

2023· article· en· W4367721970 on OpenAlexafffund
Huan Gao, Miguel A. Soto, Joanna Szymkowiak, Lucas J. Andrew, Wadood Y. Hamad, Mark J. MacLachlan

Bibliographic record

VenueDalton Transactions · 2023
Typearticle
Languageen
FieldMaterials Science
TopicClay minerals and soil interactions
Canadian institutionsFPInnovationsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaNarodowa Agencja Wymiany AkademickiejChina Scholarship Council
KeywordsHalloysiteNanocrystalMaterials scienceCelluloseThermal stabilityIridescenceComposite numberThermalChemical engineeringNanotechnologyComposite materialOptics

Abstract

fetched live from OpenAlex

Films of cellulose nanocrystals (CNCs) with chiral nematic organization can show vivid iridescence that arises from their hierarchical structure. Unfortunately, the brittleness of the films limits their potential applications. In this paper, we investigate the incorporation of halloysite nanotubes (HNTs) into CNC films to prepare organic-inorganic composite films with enhanced mechanical properties, while preserving the chiral nematic structure and brilliant iridescence. The hybrid composite films containing 10 wt% HNTs are more elastic than pristine CNC films, with a 1.3-fold increase in tensile strength and a 1.6-fold increase in maximum strain. As well, the incorporation of HNTs slightly improves the thermal stability of the composite films. These materials mimic the hybrid composite structures of crab shells, leading to enhanced mechanical properties and thermal stability of CNC films while maintaining iridescence.

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 categoriesInsufficient payload (model declined to judge)
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 score0.004
Threshold uncertainty score1.000

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)0.0010.000

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 head0.036
GPT teacher head0.254
Teacher spread0.218 · 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

Citations5
Published2023
Admission routes2
Has abstractyes

Explore more

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