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Record W4225289277 · doi:10.1063/5.0094187

Structure, stability, and rheological properties of zirconia suspensions in the presence of nanocrystals: Effects of ionic strength

2022· article· en· W4225289277 on OpenAlexafffund
Marziyeh Danesh, Damien Mauran, Richard M. Berry, Marek Pawlik, Savvas G. Hatzikiriakos

Bibliographic record

VenuePhysics of Fluids · 2022
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsCelluForce (Canada)University of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCubic zirconiaZeta potentialAdsorptionChemical engineeringRheologyIonic strengthScanning electron microscopeElectrolyteNanocrystalSurface chargeMaterials scienceNanoparticleComposite materialAqueous solutionChemistryPhysical chemistryCeramic

Abstract

fetched live from OpenAlex

Cellulose nanocrystals (CNCs) are used as a stabilizing agent in water-based zirconia suspensions at high concentration. The effects of CNC and NaCl concentrations were studied on the stability, adsorption, zeta potential, size, and rheology of slurries. We characterized samples and visualized CNCs at the surface of solid particles by scanning electron microscopy. The results confirm that the adsorption capacity of CNC on the surface of zirconia particles increases as salt concentration increases and causes an increase in the viscoelastic properties and a denser structure on the surface of adsorbent. The presence of counter-ions from the added electrolyte shortens the range of electrostatic repulsion between CNC particles through screening its electrical double layer resulting in more adsorption of adsorbate on the zirconia surface and, thus, provides stabilization.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.030
GPT teacher head0.272
Teacher spread0.242 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2022
Admission routes2
Has abstractyes

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