Next-Gen Diagnostic Sensors for Arboviral Diseases: A Multilayer Sensor Design

In an era of rapidly emerging infectious diseases, tropical and subtropical regions are witnessing a significant rise in severe illnesses such as dengue, malaria, chikungunya, and Zika. These arbovirus-borne infections often share overlapping symptoms, making early diagnosis difficult and often fatal if delayed. Traditional diagnostic techniques rely on invasive methods, lengthy sample processing, and complex lab environments-posing challenges in time-critical and resource-limited scenarios. This book presents a comprehensive study and innovative solution for tropical disease diagnosis through the design, development, and deployment of advanced MEMS-based biosensors. Centered on a multilayer piezoresistive microcantilever array, the proposed system offers high sensitivity, selectivity, and non-invasive detection capabilities. What sets this research apart is the catalytic enhancement strategy employed to significantly improve sensor performance in detecting all serotypes of dengue viruses and other arboviral diseases. The biosensor system integrates seamlessly with low-power embedded platforms like FPGA and ATMega328P, offering rapid detection within 30 minutes and delivering remarkable accuracy (96.23%), specificity (98.8%), and sensitivity (98.3%). This book, designed for researchers, students, and professionals in biomedical engineering, MEMS, nanotechnology, and public health, effectively bridges the gap between novel micro-sensing technologies and practical epidemic control. It is a vital contribution to the development of point-of-care diagnostic systems that are cost-effective, fast, and minimally invasive-tailored for the needs of today's global health landscape.