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Record W1810647922 · doi:10.1039/c5sm00245a

Measured capillary forces on spheres at particle-laden interfaces

2015· article· en· W1810647922 on OpenAlexfundno aff
Wei He, Nesrin Şenbil, A. D. Dinsmore

Bibliographic record

VenueSoft Matter · 2015
Typearticle
Languageen
FieldMaterials Science
TopicPickering emulsions and particle stabilization
Canadian institutionsnot available
FundersDivision of Chemical, Bioengineering, Environmental, and Transport SystemsMcMaster UniversityNational Science Foundation
KeywordsCantileverCapillary actionSPHERESDeflection (physics)PerpendicularMechanicsMaterials scienceParticle (ecology)NanotechnologyClassical mechanicsPhysicsComposite materialGeometry

Abstract

fetched live from OpenAlex

We measure capillary forces on particles at fluid interfaces in order to assess the key parameters that yield effective stabilizing particles. In our experiments, a millimeter-scale particle is attached to a cantilever, which is used to pull the particle perpendicular to the interface. Simultaneously, we image from the side to measure the cantilever's deflection and thus the pulling force, as well as the height of the particle and the shape of the interface. We find that the peak force on a particle at an interface crowded with other particles is consistently smaller than the force at a clean interface. This result is independent of the difference in fluid mass densities, the material of the target sphere, and the capillary charge of the free particles. We attribute the force reduction to the perturbation of interface shape due to the constraints at the boundaries of the free particles. The results should be helpful in designing particles to stabilize droplets in new oil dispersants or in other applications.

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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.046
GPT teacher head0.267
Teacher spread0.222 · 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

Citations17
Published2015
Admission routes1
Has abstractyes

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