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Record W4226405313 · doi:10.1016/j.carpta.2022.100201

Effect of hydrophobic modification of cellulose nanocrystal (CNC) and salt addition on Pickering emulsions undergoing phase-transition

2022· article· en· W4226405313 on OpenAlexaff
Parinaz Ataeian, Qingyue Shi, Marios A. Ioannidis, Kam Chiu Tam

Bibliographic record

VenueCarbohydrate Polymer Technologies and Applications · 2022
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPickering emulsionSurface tensionChemical engineeringMaterials scienceAdsorptionNanocrystalEmulsionPhase (matter)Surface chargeParticle (ecology)DiffusionSurface energyCharge densitySurface modificationNanotechnologyThermodynamicsComposite materialChemistryOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

The charge density of emulsifiers determines the thermodynamics and kinetics adsorption properties at the oil-water interface of an emulsion. This study examines the rupture resistance and stability of the crystallized dispersed phase based on the partitioning capability of cellulose nanocrystals (CNCs) with different surface charges. The charge density of CNC particles was designed through salt addition and hydrophobic modification. The measurement of dynamic interfacial tension confirmed that the reduction of negative charge of CNC particles improves their Pickering stabilizer role. We observed that the interfacial tension displayed a sigmoidal profile with increasing time. The dynamic stability of the emulsion during phase transition of the oil phase was enhanced by increasing the interfacial particle accumulation via the reduction of surface charge of CNC. The results indicated based on the surface charge adjustment, a balance between diffusion rate and kinetic barrier adsorption of particles is required to achieve optimum system stability.

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 score0.012
Threshold uncertainty score0.563

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 head0.011
GPT teacher head0.277
Teacher spread0.266 · 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

Citations22
Published2022
Admission routes1
Has abstractyes

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