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Record W7018133814

A Comparative Analysis of Capacitive Based Flexible Pressure Sensors

2018· article· en· W7018133814 on OpenAlexaff

Bibliographic record

VenueScholarship at UWindsor (University of Windsor) · 2018
Typearticle
Languageen
FieldEngineering
TopicDielectric materials and actuators
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsCapacitive sensingPressure sensorElastomerElectronic skinCapacitorFlexibility (engineering)WindsorDielectric
DOInot available

Abstract

fetched live from OpenAlex

A Comparative Analysis of Capacitive Based Flexible Pressure Sensors Julia Pignanelli1, Dr. Simon Rondeau-Gagne2 and Dr. Jalal Ahamed3 Department of Biology, University of Windsor Department of Chemistry & Biochemistry, University of Windsor Mechanical, Automotive and Materials Engineering Department, University of Windsor This paper presents the material characterization of a flexible polymer for potential biomedical pressure sensing applications. The emergence of flexible, capacitive based pressure sensors with similar tactile sensing properties as human skin are highly desirable in many applications such as continuous cardiovascular monitoring, electronic skin and rehabilitation technologies [2,3]. Most of these sensors require high sensitivity, fast response time, flexibility and low cost. Due to flexible and foldable nature of the sensor, it can be integrated to artificial skin or imposed on the body surface. Among different sensing technologies, one promising method is through the use of micro-structured, flexible, dielectric polymers, for example PDMS (Polydimethylsiloxane). Micro-structures increase the sensitivity of the device. Current methods for constructing micro-patterns into the PDMS requires expensive and labor intensive methods such as photolithographic techniques and chemical etching, which lack low-cost and large-area compatible alternatives. The objective of this work is to construct and characterize a flexible capacitor pressure sensing device by using a simple, cost effective method of PDMS microstructuring as described by previously by Grzybowki et al. [1]. The PDMS was prepared by mixing 20 parts elastomer and 1 part curing agent. The rough structured PDMS is prepared by simply curing the polymer within an epoxy mold that incorporates a micro – pattern like design found within a commercially available tape [1]. The sample was placed in a vacuum for one hour at room temperature and then cured for another 24 hours at room temperature. Flexibility of the sensors is a key parameter related to sensor sensitivity. Flexibility can be measured by measuring the modulus of elasticity. Our test reveals the rough structured 20:1 PDMS modulus to be 1.29 MPa whereas the non-structured 20:1 PDMS modulus was found to be 2.90 MPa. The difference in moduli was not determined to be significant as expected since the structures are not changing the material property of PDMS. Through characterization and preparation of rough structured capacitive based PDMS pressure sensors, we hope to produce a capacitive sensing device with excellent detection sensitivity at a low cost and with a simple method of production. Refrences: Grzybowski, B., Qin, D., Haag, R., & Whitesides, G. (2000). Elastomeric optical elements with deformable surface topographies: applications to force measurements, tunable light transmission and light focusing. Sensors And Actuators A: Physical, 86(1-2), 81-85. Boutry, C., Nguyen, A., Lawal, Q., Chortos, A., Rondeau-Gagné, S., & Bao, Z. (2015). A Sensitive and Biodegradable Pressure Sensor Array for Cardiovascular Monitoring. Advanced Materials, 27(43), 6954-6961. Mannsfeld, S., Tee, B., Sltengerg, R, Chen, C.,,, Barman, S., Muir, B., Sokiloy, A., Reese, C. and Bao, Z. (2010). Highly sensitive flexible pressure sensors with microstrucutred rubber dielectric layers. Nature Materials, 9(10), 859-864.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.253
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.220
Teacher spread0.202 · 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.

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

Citations0
Published2018
Admission routes1
Has abstractyes

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