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Record W2734666534 · doi:10.1021/acs.langmuir.7b01740

Effect of Ionic Strength and Surface Charge Density on the Kinetics of Cellulose Nanocrystal Thin Film Swelling

2017· article· en· W2734666534 on OpenAlexafffund
Michael S. Reid, Stephanie A. Kedzior, Marco Villalobos, Emily D. Cranston

Bibliographic record

VenueLangmuir · 2017
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCabot Corporation
KeywordsIonic strengthSwellingSurface chargeChemical engineeringParticle (ecology)Charge densityIonic bondingChemistryCelluloseThin filmvan der Waals forceKineticsAqueous solutionMaterials scienceComposite materialNanotechnologyPhysical chemistryOrganic chemistryMoleculeIon

Abstract

fetched live from OpenAlex

This work explores cellulose nanocrystal (CNC) thin films (<50 nm) and particle-particle interactions by investigating film swelling in aqueous solutions with varying ionic strength (1-100 mM). CNC film hydration was monitored in situ via surface plasmon resonance, and the kinetics of liquid uptake were quantified. The contribution of electrostatic double-layer forces to film swelling was elucidated by using CNCs with different surface charges (anionic sulfate half ester groups, high and low surface charge density, and cationic trimethylammonium groups). Total water uptake in the thin films was found to be independent of ionic strength and surface chemistry, suggesting that in the aggregated state van der Waals forces dominate over double-layer forces to hold the films together. However, the rate of swelling varied significantly. The water uptake followed Fickian behavior, and the measured diffusion constants decreased with the ionic strength gradient between the film and the solution. This work highlights that nanoparticle interactions and dispersion are highly dependent on the state of particle aggregation and that the rate of water uptake in aggregates and thin films can be tailored based on surface chemistry and solution ionic strength.

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)0.001
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 score0.001
Threshold uncertainty score0.284

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.018
GPT teacher head0.285
Teacher spread0.267 · 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

Citations49
Published2017
Admission routes2
Has abstractyes

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