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Record W1979334042 · doi:10.1103/physrevb.71.085406

Calculations of the threshold force and threshold power to move adsorbed nanoparticles

2005· article· en· W1979334042 on OpenAlexaff
D. A. Aruliah, Martin H. Müser, Udo D. Schwarz

Bibliographic record

VenuePhysical Review B · 2005
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsWestern UniversityOntario Tech University
Fundersnot available
KeywordsDragQuasistatic processPhysicsMomentum transferOscillation (cell signaling)Cluster (spacecraft)Coupling (piping)Condensed matter physicsLattice (music)Particle (ecology)Materials scienceMechanicsClassical mechanicsOpticsScatteringQuantum mechanics

Abstract

fetched live from OpenAlex

We propose and analyze a simple model for the calculation of the power ${P}^{*}$ necessary to depin an essentially rigid cluster or nanoparticle on a surface with a scanning force microscope tip in tapping mode. The model contains the coupling between the particle's lateral and normal motion. We show that there are two important limiting regimes. (i) If momentum transfer occurs gradually between tip and particle, ${P}^{*}$ depends mainly on the viscous-type drag between particle and surface. (ii) If momentum transfer occurs instantaneously once per oscillation, ${P}^{*}$ is dominated by the minimum energy barrier necessary to move the cluster by one lattice constant. In the quasistatic driving mode (i), a critical impact angle ${\ensuremath{\alpha}}_{\mathrm{t}}^{*}$ is identified below which depinning cannot be achieved due to lateral-normal coupling.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.010
GPT teacher head0.311
Teacher spread0.301 · 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 designSimulation or modeling
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

Citations21
Published2005
Admission routes1
Has abstractyes

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