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Record W3038333716 · doi:10.1063/5.0012265

Self-propelled torus colloids

2020· article· en· W3038333716 on OpenAlexafffund
Jiyuan Wang, Mu‐Jie Huang, Raymond Kapral

Bibliographic record

VenueThe Journal of Chemical Physics · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTorusColloidJanusClassical mechanicsActive matterColloidal particleMaterials scienceMechanicsPhysicsChemical physicsNanotechnologyGeometryChemistryMathematicsPhysical chemistry

Abstract

fetched live from OpenAlex

Suspensions of chemically powered self-propelled colloidal particles are examples of active matter systems with interesting properties. While simple spherical Janus particles are often studied, it is known that geometry is important and recent experiments have shown that chemically active torus-shaped colloids behave differently from spherical colloids. In this paper, coarse-grained microscopic simulations of the dynamics of self-diffusiophoretic torus colloids are carried out in bulk solution in order to study how torus geometric factors influence their active motion. The concentration and velocity fields are key ingredients in self-diffusiophoretic propulsion, and the forms that these fields take in the colloid vicinity are shown to be strong functions of torus geometric parameters such as the torus hole size and thickness of the torus tube. This work utilizes a method where self-diffusiophoretic torus colloids with various geometric and dynamical characteristics can be built and studied in fluid media that include chemical reactions and fluid flows. The model can be used to investigate the collective properties of these colloids and their dynamics in confined systems, topics that are of general importance for applications that use colloidal motors with complex geometries.

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 score0.024
Threshold uncertainty score0.260

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 head0.012
GPT teacher head0.217
Teacher spread0.205 · 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

Citations3
Published2020
Admission routes2
Has abstractyes

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