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Record W4392281352 · doi:10.1088/2058-8585/ad2ece

Aerosol jet printed temperature sensors using an environmentally friendly bilayer dielectric

2024· article· en· W4392281352 on OpenAlexafffund
Mathieu N. Tousignant, Vanessa Tischler, Kaitlin Wagner, Zheng Lin, Jaclyn L. Brusso, Ricardo Izquierdo, Benoît H. Lessard

Bibliographic record

VenueFlexible and Printed Electronics · 2024
Typearticle
Languageen
FieldEngineering
TopicNanomaterials and Printing Technologies
Canadian institutionsÉcole de Technologie SupérieureUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEnvironmentally friendlyMaterials scienceAerosolDielectricBilayerOptoelectronicsJet (fluid)NanotechnologyEngineering physicsAerospace engineeringMeteorologyEngineeringPhysicsChemistryMembrane

Abstract

fetched live from OpenAlex

Abstract With the rise of the internet of things and applications such as smart packaging, the need for low cost, disposable temperature sensors with minimum environmental impact are critical. In this study, we report fully printed capacitive temperature sensors made from bio-degradable dielectric materials. All layers were aerosol jet printed and the areal capacitance was characterized at several temperatures between room temperature (22 ˚C) and 80 °C. Using a bilayer dielectric structure, a layer of poly (vinyl alcohol) (PVA) was encapsulated with polycaprolactone (PCL) through interfacial crosslinking to protect it against humidity. Various concentrations and layer amounts of PVA were investigated, with the most effective capacitors consisting of a single layer of PVA deposited from a 5.0 mg ml−1 solution followed by a layer of the UV-crosslink-able PCL deposited from 2.0 mg ml−1 solution, achieving a 43 ± 6% increase in areal capacitance at 80 °C when compared to room temperature, measured at a frequency of 501 Hz.

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 score0.001
Threshold uncertainty score0.002

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.231
Teacher spread0.220 · 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

Citations11
Published2024
Admission routes2
Has abstractyes

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