3D Printing of Flexible Ionic Liquid Gel Sensors
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Smith-Jones, Julian
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Abstract
3D printing has gained popularity due to the ability to fabricate complex structures from materials ranging from hard materials such as metal and concrete to softer materials including hydrogels and ion gels. Stretchable conductive materials have also attracted great attention due to their potential applications as strain sensors, wearable electronics, soft robotics, and medical devices. The fabrication of these materials with customized object geometries is desirable, but the methods to achieve them are still highly limited. 3D printing via vat photopolymerization can easily generate sophisticated object geometries, but there is still a significant need to print with materials that afford improved conductivity, mechanical properties, elastic recovery, and durability. Additionally, while 3D printing enables control over sensor design in multiple dimensions, customizability of a sensor toward different individual use cases is still limited because each sensorrequires a new design and manufacturing step. This thesis focuses on the development of ionic liquid gels as materials for conductive, elastomeric sensors. Chapter 1 serves as an introduction to 3D printing in general and discusses why ionic liquids are ideal materials for flexible devices. Chapter 2 discusses the application of ion gels as sensors and examines the effects of altering resin components on mechanical properties. Chapter 3 builds on these sensors by incorporating multiple ionic liquids into one structure. The difference in mechanical and elastic recovery properties produces shape transformation when strain is applied to the multi-material constructs. Lastly, chapter 4 seeks to improve the customizability of these ion gel sensors by incorporating dynamic bonds into the polymer networks to form covalent adaptable networks (CANs). The reversable covalent bonds introduced to the networks allow post-printing modification of the gels and facilitate fabrication of modular strain sensors.
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Thesis (Ph.D.)--University of Washington, 2023
