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Record W3040396709 · doi:10.1088/1361-6439/aba226

The development of a pressure sensor using a technique for patterning silver nanowires on 3-dimensional curved PDMS membranes

2020· article· en· W3040396709 on OpenAlexafffund
Rick Helgason, Angelica Campigotto, Yongjun Lai

Bibliographic record

VenueJournal of Micromechanics and Microengineering · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGauge factorResistive touchscreenMaterials scienceNanowireFabricationPressure sensorMembraneDevelopment (topology)MoldNanotechnologyPressure measurementStrain gaugeOptoelectronicsComposite materialMechanical engineeringElectrical engineeringChemistryEngineering

Abstract

fetched live from OpenAlex

Abstract The development of flexible electronic devices has primarily been focused on the production of flat 2-dimensional sensors and has lacked the ability to manufacture devices with complicated 3-dimensional geometry. A mold-based method for manufacturing devices with 3-dimensional geometry that is cost-effective and repeatable is presented herein. This technique is demonstrated by the fabrication of a novel pressure sensor using a 3-dimensional PDMS membrane patterned with a resistive silver nanowire network. The specific geometry of the sensor was chosen to provide a uniform strain distribution along the silver nanowire network. The sensor has a linear response to pressure, a gauge factor of 4–29, and behaves well under repeated cyclical testing. A flat sensor with a 2-dimensional membrane was also manufactured for comparison to the 3-dimensional sensor. It was observed that the flat membrane has a higher gauge factor but has a non-linear response to pressure.

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)0.000
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 score0.137
Threshold uncertainty score0.530

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 head0.020
GPT teacher head0.220
Teacher spread0.200 · 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

Citations8
Published2020
Admission routes2
Has abstractyes

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