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Record W4205721793 · doi:10.21203/rs.3.rs-1096204/v1

Forces that control self-organization of chemically-propelled Janus tori

2022· preprint· en· W4205721793 on OpenAlexafffund
Igor Aronson, Jiyuan Wang, Mu‐Jie Huang, Remmi Baker, Raymond Kapral

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsUniversity of Toronto
FundersBasic Energy SciencesNatural Sciences and Engineering Research Council of CanadaOffice of ScienceGovernment of OntarioCompute CanadaUniversity of TorontoU.S. Department of Energy
KeywordsJanusTorusControl (management)Janus particlesNanotechnologyChemistryMaterials scienceComputer scienceMathematicsArtificial intelligenceGeometry

Abstract

fetched live from OpenAlex

Abstract Control of the individual and collective behavior of self-propelled synthetic micro-objects has immediate application for nanotechnology, robotics, and precision medicine. Despite significant progress in the synthesis and characterization of self-propelled Janus (two-faced) particles, predictive understanding of their behavior remains challenging, especially if the particles have anisotropic forms. Here, by using molecular simulation, we describe the interactions of chemically-propelled microtori near a wall. The results show that a torus hovers at a certain distance from the wall due to a combination of gravity and hydrodynamic flows generated by the chemical activity. Moreover, electrostatic dipolar interactions between the torus and the wall result in a spontaneous tilt and horizontal translation, in a qualitative agreement with the experiment. Simulations of the dynamics of two tori near a wall provide evidence for the formation of stable self-propelled bound states. Our results illustrate that self-organization at the microscale occurs due to a combination of multiple factors, including hydrodynamic, chemical, and electrostatic interactions.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.028
GPT teacher head0.327
Teacher spread0.298 · 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 designSimulation or modeling
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

Citations0
Published2022
Admission routes2
Has abstractyes

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