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Record W4309710740 · doi:10.1021/acsanm.2c03788

Self-Assembled Gels of Cellulose Nanocrystals for Diffusion-Controlled Color Switching

2022· article· en· W4309710740 on OpenAlexafffund
Yihan Shi, Miguel A. Soto, Mark J. MacLachlan

Bibliographic record

VenueACS Applied Nano Materials · 2022
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British Columbia
KeywordsNanocrystalSupramolecular chemistryCelluloseDiffusionPolymerMaterials scienceNanotechnologyNanoparticleMoleculeSelf-assemblySupramolecular polymersChemical engineeringChemical physicsChemistryOrganic chemistryComposite materialThermodynamics

Abstract

fetched live from OpenAlex

We demonstrate a new method to control the diffusion of guest molecules within cellulose nanocrystal (CNC) matrices containing embedded receptors. Specifically, we modified the structural composition of the guests by connecting them to polymer chains or nanoparticles, to slow down their diffusion rate, leading to controlled and colorful host–guest self-assembly processes within CNC-based materials. Moreover, considering the differences in guest binding affinity, we successfully built a supramolecular system that can exhibit macroscopic color changes by a sequenced exchange process. This system has been implemented in a spontaneous color-evolving film. We anticipate that these materials may be valuable to design a set of self-responsive materials and size exclusion gels functioning as, for example, time-evolving coatings, sustained-releasing materials, and separation columns.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient 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 score0.005
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.013
GPT teacher head0.262
Teacher spread0.249 · 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
Published2022
Admission routes2
Has abstractyes

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