Effect of Infill Density on Electrical Sensitivity of 3D-Printed Flexible Pressure Sensors Using Ultrasonication Cavitation-Enabled Treatment and Thermal-Assisted Method
Year: 2024
Type: article
Abstract: The development of physiological detection is advancing rapidly, driven largely by the increase in the awareness of sport, healthcare, and biomedical knowledge. Wearable electronics have been integrated into real-world physiological sensing applications, with many recent studies aimed at enhancing their capabilities from both material selection and fabrication perspectives. To create the best fit for specific wearers, three-dimensional (3D) printing is an excellent candidate because of its potential to create structures ranging from simple to highly complex. This work investigates the effect of infill densities (20%, 40%, and 60%) on the electromechanical properties of 3D-printed thermoplastic polyurethane (TPU) using fused deposition modeling (FDM). The printing conditions were consistently controlled throughout the study, specifically using a honeycomb infill pattern. The flexible TPU substrates were successfully 3D-printed, and 1% w/v of multiwalled carbon nanotubes (MWCNTs) were embedded in the 3D-printed samples using an ultrasonic cavitation-enabled treatment and thermal-assisted method. This process aims to prevent CNT fallout while maintaining the compression load-bearing capacity. A compressive load of 10 kN was applied to the samples during electromechanical testing. The results show that a 20% infill density provides the optimum sensitivity of 11.32 MPa-1 at 2V applied voltage due to its appropriate current path, which is confirmed by scanning electron microscope (SEM). The dimension accuracy of the 3D-printed TPU samples tend to increase with higher infill densities and application of the double treatment.
Source: Key engineering materials
DOI: 10.4028/p-eqgxj8
Authors: Siriporn Wu, Chuanchom Aumnate, Pranut Potiyaraj, Patrapee Kungsadalpipob
Institution: Chulalongkorn University
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4405819009
