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Record W2605788905 · doi:10.11159/icnnfc17.1

Casimir Forces in MEMS/NEMS: Influence of Nanoscale Surface Roughness and Material Optical Properties

2017· article· en· W2605788905 on OpenAlexvenueno aff
G. Palasantzas, V. B. Svetovoy

Bibliographic record

VenueProceedings of the World Congress on Recent Advances in Nanotechnology · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum Electrodynamics and Casimir Effect
Canadian institutionsnot available
Fundersnot available
KeywordsCasimir effectNanoelectromechanical systemsMicroelectromechanical systemsNanoscopic scaleMaterials scienceSurface roughnessSurface finishSurface (topology)NanotechnologyComposite materialGeometryClassical mechanicsPhysicsMathematicsNanoparticle

Abstract

fetched live from OpenAlex

Understanding the vacuum state of a quantum system is one of the major challenges of fundamental physics and some associated increasingly important technologies. It is also key to model the possible origin of our universe and its observed accelerated expansion. If confined within boundaries, vacuum fluctuations manifest themselves by the generation of Casimir forces. However, boundaries between interacting bodies posses in many cases nanoscale surface roughness, which is both difficult to avoid and control. When two bodies are separated by a small distance (e.g., less than 200 nm) the roughness starts to play an important role on the Casimir interaction between the bodies and their adhesion. Control of this short-distance interaction is crucial for micro and nanoelectromechanical devices, microfluidics, and for micro/nanotechnology. These forces between flat bodies can be described by the Lifshitz theory that takes into account the actual measured optical properties of interacting materials. For rough surfaces the problem is complicated by the nonadditivity of the dispersion forces. In this talk I will review the current state of the problem with attention to be given for metallic systems, phase change materials, and pure conductors (promising for devices operating under severe environments) with respect to actuation dynamics of MEMS/NEMS.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.540
Threshold uncertainty score0.571

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.006
GPT teacher head0.250
Teacher spread0.244 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations0
Published2017
Admission routes1
Has abstractyes

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