April 22 - 26, 2024
Seattle, Washington
May 7 - 9, 2024 (Virtual)
Symposium Supporters
2024 MRS Spring Meeting
MF01.16.02

Customizable Flexible Pressure Sensors with Enhanced Performance via Ink Optimization and Microdome Integration

When and Where

Apr 26, 2024
8:15am - 8:30am
Room 325, Level 3, Summit

Presenter(s)

Co-Author(s)

Sina Hassanpoor1,Kaliyah Shearod1,Taeil Kim1

Baylor University1

Abstract

Sina Hassanpoor1,Kaliyah Shearod1,Taeil Kim1

Baylor University1
This research presents an extensive exploration of the development of customizable flexible pressure sensors through the application of Direct-Ink-Writing 3D printing technology. It places a primary focus on the optimization of ink formulations, varying filler ratios of carbon nanotubes (CNT) and silicon dioxide (SiO<sub>2</sub>) in polydimethylsiloxane (PDMS) matrix. The research systematically examines how these optimized ink formulations offer precise control over the electrical and hyperelastic properties of the printed sensors and explains their influence on key sensor characteristics such as conductivity and sensitivity. The optimization process of ink formulation is at the core of this research. By adjusting the proportions of CNT, SiO<sub>2</sub>, and PDMS, the printability of inks and the electrical properties of sensors could be effectively manipulated. This fine-tuning is essential for ensuring that the sensor operates efficiently in a wide range of environments and applications. Concurrently, the hyperelastic properties of the sensors, which encompass flexibility and elasticity, can be tailored to meet specific requirements. A crucial aspect of this research is the effect of these controlled modifications on the sensors' performance characteristics. The paper delves into how these adjustments can significantly impact the linearity and sensitivity of pressure sensors. Examining these attributes provides a comprehensive understanding of how customizability can enhance the sensors' precision and suitability for various applications. The integration of microdomes with various sizes and differing ink ratios as printed structures atop a flat printed layer introduces a dynamic dimension to the pressure sensor design. These microdomes are meticulously tailored to function as responsive elements, each uniquely tuned for specific applications. When subjected to varying levels of pressure, these microdomes exhibit an array of behaviors, allowing the sensor to capture detailed data. This multifaceted approach enhances the adaptability of the sensor, making it well-suited for a wide range of applications where precise pressure sensing is imperative, such as touch-sensitive screens, medical devices, or robotic grippers. This adaptability and sensitivity are particularly relevant in healthcare and biomedical applications. Pressure sensors with these attributes can have a meaningful impact on patient care, providing accurate data for diagnosis and treatment. Moreover, integrating microdomes onto the sensor surface, each designed for specific functions, enhances the sensing capability to meet real-world healthcare needs. This integration enables tailored performance for practical applications like continuous blood pressure monitoring, prosthetic limb control, and minimally invasive surgical instruments that rely on precise pressure feedback. The comprehensive discussion in this research spans the methodology used in ink optimization, detailed results from experimental work, and the broad implications of this innovative technology for the domain of flexible pressure sensing. With a focus on precise control over sensor properties, this research aims to contribute to the ongoing advancement of sensor technology and its applications across various fields.

Keywords

additive manufacturing

Symposium Organizers

Emily Davidson, Princeton University
Michinao Hashimoto, Singapore University of Technology and Design
Emily Pentzer, Texas A&M University
Daryl Yee, École Polytechnique Fédérale de Lausanne

Symposium Support

Silver
UpNano US Inc.

Session Chairs

Michinao Hashimoto
Seola Lee

In this Session