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Record W2042710135 · doi:10.1126/science.1125874

Atomic-Scale Control of Friction by Actuation of Nanometer-Sized Contacts

2006· article· en· W2042710135 on OpenAlexaff
Anisoara Socoliuc, Enrico Gnecco, Sabine Maier, Oliver Pfeiffer, A. Baratoff, Roland Bennewitz, Ernst Meyer

Bibliographic record

VenueScience · 2006
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsStictionDissipationMaterials scienceNanotribologyAtomic unitsExcitationPerpendicularSlip (aerodynamics)Microelectromechanical systemsRange (aeronautics)Nanoelectromechanical systemsActuatorNanometreTribometerMechanicsAtomic force microscopyTribologyNanotechnologyComposite materialElectrical engineeringPhysicsNanoparticle

Abstract

fetched live from OpenAlex

Stiction and wear are demanding problems in nanoelectromechanical devices, because of their large surface-to-volume ratios and the inapplicability of traditional liquid lubricants. An efficient way to switch friction on and off at the atomic scale is achieved by exciting the mechanical resonances of the sliding system perpendicular to the contact plane. The resulting variations of the interaction energy reduce friction below 10 piconewtons in a finite range of excitation and load, without any noticeable wear. Without actuation, atomic stick-slip motion, which leads to dissipation, is observed in the same range. Even if the normal oscillations require energy to actuate, our technique represents a valuable way to minimize energy dissipation in nanocontacts.

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.003
GPT teacher head0.239
Teacher spread0.236 · 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

Citations342
Published2006
Admission routes1
Has abstractyes

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