MétaCan
Menu
Back to cohort
Record W1996299322 · doi:10.1088/0960-1317/24/6/065021

Fabrication of nanoelectromechanical systems via the integration of high surface area glancing angle deposition thin films

2014· article· en· W1996299322 on OpenAlexaff
Jocelyn N. Westwood, Vincent T. K. Sauer, Jonathan K. Kwan, Wayne K. Hiebert, Jeremy C. Sit

Bibliographic record

VenueJournal of Micromechanics and Microengineering · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsNational Institute for NanotechnologyUniversity of Alberta
Fundersnot available
KeywordsNanoelectromechanical systemsMaterials scienceCantileverFabricationDeposition (geology)Resonance (particle physics)Gravimetric analysisNanotechnologyThin filmStress (linguistics)Surface stressLayer (electronics)Microelectromechanical systemsOptoelectronicsComposite materialChemistrySurface energyPhysics

Abstract

fetched live from OpenAlex

Nanoelectromechanical systems (NEMS) coated with a high surface area thin film are fabricated. Glancing angle deposition (GLAD) is used to uniformly deposit high surface area, nanostructured SiO2 films on top of released NEMS. The resonance frequencies and quality factors are measured to assess the potential of the high surface area NEMS for sensing experiments. Resonance frequencies of coated cantilevers, although reduced by mass loading, can be predicted accurately using our derived model. Compressive stress makes the resonance frequencies of coated doubly-clamped beams difficult to predict. The quality factors of the coated NEMS are reduced by one order of magnitude by a quasi-continuous layer at the base of the GLAD film, which also introduces an estimated compressive stress of 5.3-9.3 MPa. The limit of detection is demonstrated to be ∼2 pg cm-2. With this successful proof-of-concept demonstration, we anticipate the future use of these devices as high surface area gravimetric mass sensors for applications such as gas chromatography.

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)0.001
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 score0.484
Threshold uncertainty score0.358

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.005
GPT teacher head0.190
Teacher spread0.184 · 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

Citations4
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Micromechanics and MicroengineeringSame topicMechanical and Optical ResonatorsFrench-language works237,207