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Record W2976131728 · doi:10.3390/app9194044

In-Situ Continuous Monitoring of the Viscosity of Surfactant-Stabilized and Nanoparticles-Stabilized Pickering Emulsions

2019· article· en· W2976131728 on OpenAlexaff
Upinder Bains, Rajinder Pal

Bibliographic record

VenueApplied Sciences · 2019
Typearticle
Languageen
FieldMaterials Science
TopicPickering emulsions and particle stabilization
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPickering emulsionPulmonary surfactantRheologyMaterials scienceVolume fractionChemical engineeringViscosityEmulsionPhase (matter)Shear rateNanoparticleVolume (thermodynamics)ChromatographyChemistryNanotechnologyComposite materialOrganic chemistryThermodynamics

Abstract

fetched live from OpenAlex

An in-situ method of measuring the viscosity of unstable and stable emulsions on a continuous basis under agitation conditions was developed and utilized to investigate the viscous behaviour of surfactant-stabilized and nanoparticles-stabilized oil-in-water (O/W) emulsions at different volume fractions of the dispersed phase (oil). The stability characteristics (droplet size and phase-separation) of emulsions under quiescent conditions were also determined with the aging of emulsions. Emulsions are Newtonian at low volume fractions of the dispersed phase. At high concentrations of the dispersed phase, emulsions behave as non-Newtonian shear-thinning fluids. The nanoparticles-stabilized (Pickering) emulsions are unstable in comparison with the surfactant-stabilized emulsions. The droplet sizes of Pickering emulsions increase rapidly with aging, whereas the droplet sizes of surfactant-stabilized remain nearly the same over a period of 24 h. However, Pickering emulsions are much more viscous than the surfactant-stabilized emulsions when comparison is made at the same volume fraction of the dispersed phase.

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 score0.050
Threshold uncertainty score0.282

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.017
GPT teacher head0.258
Teacher spread0.241 · 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

Citations23
Published2019
Admission routes1
Has abstractyes

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