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Record W2154743846 · doi:10.1002/smll.200901976

Catalytic Nanomotors: Self‐Propelled Sphere Dimers

2010· article· en· W2154743846 on OpenAlexaff
Leonardo Fonseca Valadares, Yu-Guo Tao, Nicole S. Zacharia, Vladimir Kitaev, Fernando Galembeck, Raymond Kapral, Geoffrey A. Ozin

Bibliographic record

VenueSmall · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsWilfrid Laurier UniversityUniversity of Toronto
Fundersnot available
KeywordsMesoscopic physicsDimerMolecular dynamicsAqueous solutionCoordination sphereChemical physicsPlatinumCatalysisSubstrate (aquarium)Materials scienceChemistryCrystallographyPhysical chemistryComputational chemistryPhysicsCrystal structureCondensed matter physicsOrganic chemistry

Abstract

fetched live from OpenAlex

Experimental and theoretical studies of the self-propelled motional dynamics of a new genre of catalytic sphere dimer, which comprises a non-catalytic silica sphere connected to a catalytic platinum sphere, are reported for the first time. Using aqueous hydrogen peroxide as the fuel to effect catalytic propulsion of the sphere dimers, both quasi-linear and quasi-circular trajectories are observed in the solution phase and analyzed for different dimensions of the platinum component. In addition, well-defined rotational motion of these sphere dimers is observed at the solution-substrate interface. The nature of the interaction between the sphere dimer and the substrate in the aqueous hydrogen peroxide phase is discussed. In computer simulations of the sphere dimer in solution and the solution-substrate interface, sphere-dimer dynamics are simulated using molecular-dynamics methods and solvent dynamics are modeled by mesoscopic multiparticle collision methods taking hydrodynamic interactions into account. The rotational and translational dynamics of the sphere dimer are found to be in good accord with the predictions of computer simulations.

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: Bench or experimental · Consensus signal: Bench or experimental
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.005
GPT teacher head0.193
Teacher spread0.188 · 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

Citations257
Published2010
Admission routes1
Has abstractyes

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