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Record W2008320407 · doi:10.1103/physreve.90.012125

Reinterpretation of velocity-dependent atomic friction: Influence of the inherent instrumental noise in friction force microscopes

2014· article· en· W2008320407 on OpenAlexaff
Yalin Dong, Hongyu Gao, Ashlie Martini, Philip Egberts

Bibliographic record

VenuePhysical Review E · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsUniversity of Calgary
FundersAir Force Office of Scientific Research
KeywordsNoise (video)Atomic force microscopyMechanicsHarmonicPhysicsClassical mechanicsMaterials scienceAcousticsNanotechnology

Abstract

fetched live from OpenAlex

We have applied both the master equation method and harmonic transition state theory to interpret the velocity-dependent friction behavior observed in atomic friction experiments. To understand the discrepancy between attempt frequencies measured in atomic force microscopy experiments and those estimated by theoretical models, both thermal noise and instrumental noise are introduced into the model. It is found that the experimentally observed low attempt frequency and the transition point at low velocity regimes can be interpreted in terms of the instrumental noise inherent in atomic force microscopy. In contrast to previous models, this model also predicts (1) the existence of a two-slope curve of velocity dependence and (2) the decrease of critical velocity with temperature, which provides clues for further experimental verification of the influence of instrumental noise in friction measurements.

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)0.002
metaresearch head score (Gemma)0.006
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0010.002
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 head0.285
Teacher spread0.278 · 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

Citations13
Published2014
Admission routes1
Has abstractyes

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