Advancements in vat photopolymerization for piezoresistive sensors: materials, mechanisms, and applications
Year: 2025
Type: article
Abstract: Vat photopolymerization (VPP) has emerged as a versatile technique for 3D printing piezoresistive strain sensors, providing high accuracy, resolution, and the ability to fabricate complex geometries tailored to specific applications. This review presents an overview of VPP methods, including stereolithography (SLA), digital light processing (DLP), and two-photon polymerisation (TPP), and examines their unique advantages and limitations in the context of strain sensor fabrication. With the integration of a variety of materials, VPP enables the creation of sensors with customisable sensitivity, durability, and environmental resilience. Such sensors offer real-time, precise strain detection, which is valuable across applications in structural health monitoring, wearable electronics, soft robotics, and human-machine interfaces. This paper also highlights recent advancements in material development for VPP-fabricated sensors, addressing challenges such as material compatibility, light absorption, and mechanical robustness. Key insights into the design and performance of VPP-based sensors underscore the potential of this technology to meet evolving demands for multifunctional, adaptive sensors. Finally, the review discusses existing challenges and future directions, including enhancing multifunctionality, improving printing resolution, exploring new applications, and developing hybrid materials and additive manufacturing methods. This work positions VPP as a pivotal technology for advancing next-generation strain sensors across diverse fields.
Source: Virtual and Physical Prototyping
Authors: Omar Waqas Saadi, Kamran A. Khan
Institution: Khalifa University of Science and Technology
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,070
Awards: G1987322141, G2825996737, G5592229574
OpenAlex ID: W4410734010
