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Record W2898948504 · doi:10.1039/c8sm01542b

Patchy colloidal particles at the fluid–fluid interface

2018· article· en· W2898948504 on OpenAlexaff
Chung Chi Chio, Ying‐Lung Steve Tse

Bibliographic record

VenueSoft Matter · 2018
Typearticle
Languageen
FieldMaterials Science
TopicPickering emulsions and particle stabilization
Canadian institutionsGovernment of Northwest Territories
FundersResearch Grants Council, University Grants CommitteeChinese University of Hong KongCroucher Foundation
KeywordsColloidColloidal particleInterface (matter)Materials scienceComplex fluidChemical engineeringMechanicsNanotechnologyPhysicsPulmonary surfactantEngineering

Abstract

fetched live from OpenAlex

Colloidal particles have significantly different characteristics when they are at interfaces from when they are in the bulk. In this study, we applied Monte Carlo simulations to investigate the stability and dynamics of smooth patchy particles and rough patchy particles near or at the fluid-fluid interface. By adjusting the surface area ratio of the two faces of a smooth Janus particle, we show how its stability, in terms of free energy, in either side of the interface can be tuned relative to the smooth homogeneous particle. We demonstrate how roughness can affect the stability and the orientation of a colloidal particle. Moreover, position-dependent diffusion constants in directions parallel and perpendicular to the interface are calculated for the colloidal particles as a function of distance from the interface. We report drastic slowdowns in the perpendicular diffusivity (and less severe slowdowns for the parallel diffusivity) for all the colloidal particles when they approach the fluid-fluid interface. While such a slowdown is well-known for the fluid-solid interface in the literature in terms of frictional force in hydrodynamics, why this happens for the fluid-fluid interface has not been adequately discussed. We provide evidence for the decrease in terms of discrepancy in the fluid density that leads to depletion forces.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.010
Threshold uncertainty score0.993

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)0.0070.014

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 head0.015
GPT teacher head0.263
Teacher spread0.248 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2018
Admission routes1
Has abstractyes

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