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Record W1973078859 · doi:10.1039/c4sm02857k

Catalytic dimer nanomotors: continuum theory and microscopic dynamics

2015· article· en· W1973078859 on OpenAlexaff
Shang Yik Reigh, Raymond Kapral

Bibliographic record

VenueSoft Matter · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDimerDynamics (music)CatalysisChemical physicsNanotechnologyMolecular dynamicsMaterials sciencePhysicsChemistryComputational chemistryNuclear magnetic resonanceOrganic chemistry

Abstract

fetched live from OpenAlex

Synthetic chemically-powered motors with various geometries have potentially new applications involving dynamics on very small scales. Self-generated concentration and fluid flow fields, which depend on geometry, play essential roles in motor dynamics. Sphere-dimer motors, comprising linked catalytic and noncatalytic spheres, display more complex versions of such fields, compared to the often-studied spherical Janus motors. By making use of analytical continuum theory and particle-based simulations we determine the concentration fields, and both the complex structure of the near-field and point-force dipole nature of the far-field behavior of the solvent velocity field that are important for studies of collective motor motion. We derive the dependence of motor velocity on geometric factors such as sphere size and dimer bond length and, thus, show how to construct motors with specific characteristics.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.006
GPT teacher head0.223
Teacher spread0.217 · 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

Citations64
Published2015
Admission routes1
Has abstractyes

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