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Surface Effects Considerations for the Design of Casimir Actuated Nanoswitches

2011· article· en· W2067577485 on OpenAlexafffund
Jian Ming Bryan, Samuel F. Asokanthan, Jiang Li

Bibliographic record

VenueApplied Mechanics and Materials · 2011
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum Electrodynamics and Casimir Effect
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCasimir effectDeflection (physics)Surface stressSurface (topology)Casimir pressureMechanicsClassical mechanicsPhysicsMathematicsGeometry

Abstract

fetched live from OpenAlex

New techniques that can control Casimir forces in nanosacle structures may soon ensure the physical realization of switchable Casimir-force devices. In order to provide useful insights into the behaviour of this class of switches, the idea of Casimir-force actuation window has been proposed here to assist in the design of such switches. The influence of surface effects including residual surface stress and surface elasticity on the pull-in parameters of Casimir actuated switches has been demonstrated. These effects, together with other currently known difficulties due to uncertainties such as surface roughness and trapped electric charge may hinder the realization of this class of devices. An Euler-Bernoulli beam model has been employed to demonstrate surface effects in a nanocantilever switch, and numerical solutions employing a finite difference approach have been obtained for the static bending of this switch. The results demonstrate that surface effects play a significant role in the selection of basic design parameters of Casimir actuated switches, such as static deflection and detachment length. Threshold value of residual surface stress is also studied for these switches. The predictions reveal that exclusion of surface effects in Casimir-force actuation window may result in non-functional switch designs.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

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)0.0010.000

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 head0.026
GPT teacher head0.227
Teacher spread0.200 · 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 designTheoretical or conceptual
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

Citations1
Published2011
Admission routes2
Has abstractyes

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