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Record W2346925948 · doi:10.1139/cjp-2015-0383

Static and dynamic instability of nanowire-fabricated nanoelectromechanical systems: effects of flow damping, van de Waals force, surface energy and microstructure

2016· article· en· W2346925948 on OpenAlexvenueno aff
Maryam Keivani, Ali Koochi, N. Abadian, Morteza Rezaei, Mohamadreza Abadyan

Bibliographic record

VenueCanadian Journal of Physics · 2016
Typearticle
Languageen
FieldMaterials Science
TopicNonlocal and gradient elasticity in micro/nano structures
Canadian institutionsnot available
Fundersnot available
KeywordsNanoelectromechanical systemsPhysicsvan der Waals forceNanowireInstabilityCantileverMechanicsSurface stressMicrostructureSurface energyNonlinear systemClassical mechanicsCondensed matter physicsMaterials scienceComposite materialThermodynamics

Abstract

fetched live from OpenAlex

Surface energy and microstructure-dependent size phenomena can play significant roles in physical performance of nanoelectromechanical systems (NEMS). Herein, the static and dynamic pull-in instability of cantilever and double-clamped NEMS fabricated from conductive cylindrical nanowires with circular cross section is studied. The Gurtin–Murdoch surface elasticity in combination with the couple stress continuum theory is employed to incorporate the coupled effects of surface energy and microstructure-dependent size phenomenon. Using Green–Lagrange strain, the higher order surface stress components are incorporated into the governing equation. The effect of gas damping is considered in the model as well as structural damping. The nonlinear governing equation is solved using analytical reduced order method. The effects of various parameters on the static and dynamic pull-in parameters, phase plans, and stability threshold of the nanowire-based structures are demonstrated.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.000
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.003
GPT teacher head0.190
Teacher spread0.187 · 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
Published2016
Admission routes1
Has abstractyes

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Same venueCanadian Journal of PhysicsSame topicNonlocal and gradient elasticity in micro/nano structuresFrench-language works237,207