Product design: Microfluidic paper-based analytical device

Microfluidic paper-based analytical devices (μPADs) have emerged as promising tools for point-of-care diagnostics due to their low cost and ease of use. However, the design of these devices often relies on empirical trial-and-error, leading to suboptimal performance. This study addresses this limitation by introducing a novel, systematic 7-step design procedure for μPADs. The methodology utilizes a State-Task Network to map processing steps and generate process alternatives, followed by zone pattern generation and dimensional optimization through imbibition process simulation. The effectiveness of this approach was demonstrated through two applications: a glucose colorimetric sensor using tear fluid and an ELISA sensor for SARS-CoV-2 detection. By integrating fluid dynamics simulation and systematic process representation, this framework enables the efficient design of μPADs without extensive experimental iterations, advancing the field of paper-based diagnostics.