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Surface Textures for Stretchable Cap...

Liu, Han.

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Surface Textures for Stretchable Capacitive Strain Sensors.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Surface Textures for Stretchable Capacitive Strain Sensors./
Author:	Liu, Han.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	40 p.
Notes:	Source: Masters Abstracts International, Volume: 82-01.
Contained By:	Masters Abstracts International82-01.
Subject:	Civil engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27837622
ISBN:	9798662372092

Surface Textures for Stretchable Capacitive Strain Sensors.
Liu, Han.

Surface Textures for Stretchable Capacitive Strain Sensors. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 40 p.

Source: Masters Abstracts International, Volume: 82-01.

Thesis (M.S.)--Iowa State University, 2020.

This item must not be sold to any third party vendors.

Advances in smart materials and electronics have enabled compliant sensing systems mimicking nature. In structural health monitoring (SHM) applications, the technology still lacks accuracy in tracking measurements over large geometries. Herein we report a facile fabrication for a compliant sensor based on an elastomeric capacitive sensor technology, augmented with a scalable surface texture inspired by various grid geometries. Texture designs are selected to direct, and therefore improve, the strain sensing capabilities and thus the sensor's gauge factor. We showcase selected designs based on an a detailed engineering analysis. The selected surface patterns are investigated under increasing strain targets and strain frequency sweeps. Results confirm that a stretchable thermoplastic composite sensor with a textured pattern embossed into the hyper-elastic matrix yields approximately 30% increase in the gauge factor and 35% in signal accuracy, great linearity up to 30% strain, and overall signal stability to empower SHM applications.

ISBN: 9798662372092Subjects--Topical Terms:

860360
Civil engineering.
Subjects--Index Terms:

Sensing skin

Surface Textures for Stretchable Capacitive Strain Sensors.
LDR:02155nmm a2200361 4500 001 2281065
005 20210913093701.5
008 220723s2020 ||||||||||||||||| ||eng d
020 $a 9798662372092
035 $a (MiAaPQ)AAI27837622
035 $a AAI27837622
040 $a MiAaPQ $c MiAaPQ
100 1 $a Liu, Han. $3 2095320
245 1 0 $a Surface Textures for Stretchable Capacitive Strain Sensors.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 40 p.
500 $a Source: Masters Abstracts International, Volume: 82-01.
500 $a Advisor: Laflamme, Simon.
502 $a Thesis (M.S.)--Iowa State University, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a Advances in smart materials and electronics have enabled compliant sensing systems mimicking nature. In structural health monitoring (SHM) applications, the technology still lacks accuracy in tracking measurements over large geometries. Herein we report a facile fabrication for a compliant sensor based on an elastomeric capacitive sensor technology, augmented with a scalable surface texture inspired by various grid geometries. Texture designs are selected to direct, and therefore improve, the strain sensing capabilities and thus the sensor's gauge factor. We showcase selected designs based on an a detailed engineering analysis. The selected surface patterns are investigated under increasing strain targets and strain frequency sweeps. Results confirm that a stretchable thermoplastic composite sensor with a textured pattern embossed into the hyper-elastic matrix yields approximately 30% increase in the gauge factor and 35% in signal accuracy, great linearity up to 30% strain, and overall signal stability to empower SHM applications.
590 $a School code: 0097.
650 4 $a Civil engineering. $3 860360
653 $a Sensing skin
653 $a Sensor
653 $a Soft elastomeric capacitor
653 $a Strain monitoring
653 $a Structural health monitoring
653 $a Texture
690 $a 0543
710 2 $a Iowa State University. $b Civil, Construction, and Environmental Engineering. $3 1022221
773 0 $t Masters Abstracts International $g 82-01.
790 $a 0097
791 $a M.S.
792 $a 2020
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27837622