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Record W2050819194 · doi:10.1109/jsen.2009.2029453

A Highly Accurate Pull-in Voltage Model for an Atomic Force Microscope Probe

2009· article· en· W2050819194 on OpenAlexaff
Liton Ghosh, Sazzadur Chowdhury

Bibliographic record

VenueIEEE Sensors Journal · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsCantileverDeflection (physics)VoltageFinite element methodNon-contact atomic force microscopyElectrostatic force microscopeNonlinear systemMaterials scienceMechanicsPhysicsOpticsEngineeringMicroscopyStructural engineeringElectrical engineeringKelvin probe force microscope

Abstract

fetched live from OpenAlex

A readily usable closed-form model has been developed to determine the pull-in voltage of an atomic force microscopy (AFM) cantilever probe under electrostatic actuation. The analytical model is derived based on the Euler-Bernoulli beam theory, Taylor series expansion of the nonlinear electrostatic force, and deflection function of the first natural mode of a cantilever beam. The model takes account of the electrostatic force associated with the fringing field capacitances between the cantilever probe and the substrate to predict a more accurate pull-in voltage. The developed closed-form model has been verified by comparing the model predicted values with published experimental results with a maximum deviation of 3.36%. The model has also been compared with a published closed-form model and 3-D electromechanical finite element analysis (FEA) carried out by the authors. The results are found to be in excellent agreement.

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)0.001
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: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0030.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.021
GPT teacher head0.314
Teacher spread0.293 · 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
GenreMethods

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

Citations4
Published2009
Admission routes1
Has abstractyes

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Same venueIEEE Sensors JournalSame topicForce Microscopy Techniques and ApplicationsFrench-language works237,207