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Record W2990777867 · doi:10.1088/2058-8585/ab59c0

Integrated capacitive sensor devices aerosol jet printed on 3D objects

2019· article· en· W2990777867 on OpenAlexaff
Sarah Vella, Chad S. Smithson, Kurt Halfyard, Ethan Shen, Michelle N. Chrétien

Bibliographic record

VenueFlexible and Printed Electronics · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsXerox (Canada)
Fundersnot available
KeywordsPrinted circuit boardElectronicsCapacitive sensingPrinted electronicsMaterials scienceArduinoElectrical engineeringComputer scienceEngineeringEmbedded system

Abstract

fetched live from OpenAlex

Abstract A functional capacitive sensing device with five touch points was fabricated on the curved surfaces of polyvinyl chloride, polycarbonate and acrylonitrile butadiene styrene piping. The capacitive touch sensor points and conductive traces were printed with an Optomec™ Aerosol Jet printer using silver nanoparticle ink. We present a solution to a common problem with hybrid printed electronics of transitioning from the printed electronic components on a three-dimensional object to traditional rigid circuit board electronics. We highlight the need for CAD/CAM technology as an essential tool for printing on three-dimensional surfaces. The capacitive touch sensor library of an Arduino Uno Bard Rev 3 microcontroller enabled the detection of contact at the printed touch points. Corresponding LEDs attached to the surface of the pipe light up to indicate contact at the touch points. This hybrid printed electronic device presents a fully integrated and functioning electronic device printed on a three-dimensional surface and highlights the requirement of multidisciplinary knowledge for the field of hybrid printed electronics.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

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 communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.012
GPT teacher head0.225
Teacher spread0.214 · 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 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

Citations31
Published2019
Admission routes1
Has abstractyes

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