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Record W2807015393 · doi:10.1088/1361-665x/aac134

Frequency domain analysis of droplet-based electrostatic transducers

2018· article· en· W2807015393 on OpenAlex
Graham Allegretto, Yuta Dobashi, Katelyn Dixon, Justin Wyss, Dickson R. Yao, John D. W. Madden

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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueSmart Materials and Structures · 2018
Typearticle
Languageen
FieldEngineering
TopicElectrowetting and Microfluidic Technologies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFrequency domainAcousticsTransducerMaterials scienceDomain (mathematical analysis)Computer sciencePhysicsMathematicsMathematical analysis

Abstract

fetched live from OpenAlex

Abstract Squeezing a water droplet between two electrodes can generate a potential difference by converting some of the mechanical energy in vibrations into electrical energy. By utilizing the high capacitance inherent to electric double layers, and the surface charging at a polymer/water interface, we demonstrate a sensor that generates up to 892 mV peak-to-peak between 1 and 100 Hz, in response to a 250 μ m deformation. This frequency response is described and explained using a linearized model in which the interfacial charge acts as the priming voltage, removing the need for external charging normally required in capacitive generators. The model suggests how to design the cell for maximum power output and provides an intuitive understanding of the high pass nature of the sensor. It successfully predicts the point of maximum power transfer.

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)0.000
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 score0.013
Threshold uncertainty score0.422

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 studies0.0000.000
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 head0.004
GPT teacher head0.200
Teacher spread0.196 · 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