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Dynamics of Atomic Stick-Slip Friction Examined with Atomic Force Microscopy and Atomistic Simulations at Overlapping Speeds

2015· article· en· W2092413515 on OpenAlexafffund
Xin-Z. Liu, Zhijiang Ye, Yalin Dong, Philip Egberts, R. W. Carpick, Ashlie Martini

Bibliographic record

VenuePhysical Review Letters · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsSlip (aerodynamics)Materials scienceAtomic force microscopySiliconMolecular dynamicsThermalVibrationNanotribologyThermal fluctuationsMechanicsAtomic physicsMolecular physicsNanotechnologyPhysicsCondensed matter physicsThermodynamicsMetallurgy

Abstract

fetched live from OpenAlex

Atomic force microscopy (AFM) and atomistic simulations of atomic friction with silicon oxide tips sliding on Au(111) are conducted at overlapping speeds. Experimental data unambiguously reveal a stick-slip friction plateau above a critical scanning speed, in agreement with the thermally activated Prandtl-Tomlinson (PTT) model. However, friction in experiments is larger than in simulations. PTT energetic parameters for the two are comparable, with minor differences attributable to the contact area's influence on the barrier to slip. Recognizing that the attempt frequency may be determined by thermal vibrations of the larger AFM tip mass or instrument noise fully resolves the discrepancy. Thus, atomic stick-slip is well described by the PTT model if sources of slip-assisting energy are accounted for.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.295
Teacher spread0.281 · 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

Citations98
Published2015
Admission routes2
Has abstractyes

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