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Universal Aging Mechanism for Static and Sliding Friction of Metallic Nanoparticles

2016· article· en· W2467484576 on OpenAlexafffund
Michael Feldmann, Dirk Dietzel, Antoni Tekiel, Jessica M. Topple, Peter Grütter, André Schirmeisen

Bibliographic record

VenuePhysical Review Letters · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaEuropean Cooperation in Science and TechnologyDeutsche ForschungsgemeinschaftCanada Foundation for InnovationJustus Liebig Universität Gießen
KeywordsSliderMaterials scienceStatic frictionDynamical frictionFormalism (music)Slip (aerodynamics)MechanicsFriction coefficientNanotribologyNanotechnologyComposite materialAtomic force microscopyMechanical engineeringPhysicsThermodynamics

Abstract

fetched live from OpenAlex

The term "contact aging" refers to the temporal evolution of the interface between a slider and a substrate usually resulting in increasing friction with time. Current phenomenological models for multiasperity contacts anticipate that such aging is not only the driving force behind the transition from static to sliding friction, but at the same time influences the general dynamics of the sliding friction process. To correlate static and sliding friction on the nanoscale, we show experimental evidence of stick-slip friction for nanoparticles sliding on graphite over a wide dynamic range. We can assign defined periods of aging to the stick phases of the particles, which agree with simulations explicitly including contact aging. Additional slide-hold-slide experiments for the same system allow linking the sliding friction results to static friction measurements, where both friction mechanisms can be universally described by a common aging formalism.

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 score0.235
Threshold uncertainty score0.200

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 head0.014
GPT teacher head0.294
Teacher spread0.280 · 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

Citations29
Published2016
Admission routes2
Has abstractyes

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