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Record W1998010357 · doi:10.1063/1.3664787

Prediction of atomic force microscope probe dynamics through the receptance coupling method

2011· article· en· W1998010357 on OpenAlexafffund
Majid Mehrpouya, S. S. Park

Bibliographic record

VenueReview of Scientific Instruments · 2011
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsUniversity of Calgary
FundersAUTO21 Network of Centres of ExcellenceNatural Sciences and Engineering Research Council of Canada
KeywordsAtomic force microscopyMaterials scienceDisplacement (psychology)Non-contact atomic force microscopyCoupling (piping)Kelvin probe force microscopeScanning probe microscopyNanoscopic scaleFabricationNanotechnologyOpticsPhysicsComposite material

Abstract

fetched live from OpenAlex

The increased growth in the use of tip-based sensing, manipulations, and fabrication of devices in atomic force microscopy (AFM) necessitates the accurate prediction of the dynamic behavior of the AFM probe. The chip holder, to which the micro-sensing device is attached, and the rest of the AFM system can affect the overall dynamics of the probe. In order to consider these boundary effects, we propose a novel receptance coupling method to mathematically combine the dynamics of the AFM setup and probe, based on the equilibrium and compatibility conditions at the joint. Once the frequency response functions of displacement over force at the tool tip are obtained, the dynamic interaction forces between the tip and the sample in nanoscale can be determined by measuring the probe tip displacement.

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)0.001
metaresearch head score (Gemma)0.004
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: none
Teacher disagreement score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.031
GPT teacher head0.309
Teacher spread0.277 · 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

Citations5
Published2011
Admission routes2
Has abstractyes

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