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Record W2403795476 · doi:10.1016/j.sbsr.2016.05.005

Optimized design of micromachined electric field mills to maximize electrostatic field sensitivity

2016· article· en· W2403795476 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueSensing and Bio-Sensing Research · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsShutterElectric fieldElectrodeSIGNAL (programming language)Sense (electronics)PerforationSensitivity (control systems)Surface micromachiningParametric statisticsMaterials scienceAcousticsShielded cableElectrical engineeringField (mathematics)OpticsOptoelectronicsComputer scienceElectronic engineeringEngineeringPhysicsFabricationMathematicsComposite material

Abstract

fetched live from OpenAlex

This paper describes the design optimization of a micromachined electric field mill, in relation to maximizing its output signal. The cases studied are for a perforated electrically grounded shutter vibrating laterally over sensing electrodes. It is shown that when modeling the output signal of the sensor, the differential charge on the sense electrodes when exposed to vs. visibly shielded from the incident electric field must be considered. Parametric studies of device dimensions show that the shutter thickness and its spacing from the underlying electrodes should be minimized as these parameters very strongly affect the MEFM signal. Exploration of the shutter perforation size and sense electrode width indicate that the best MEFM design is one where shutter perforation widths are a few times larger than the sense electrode widths.

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.

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)0.002
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 score0.223
Threshold uncertainty score0.640

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.028
GPT teacher head0.299
Teacher spread0.270 · 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