AI-assisted Development of Energy Harvesters for POCT

This thesis, titled "AI-assisted Development of Energy Harvesters for POCT" investigates innovative energy harvesting technologies and neural network optimization to enhance design of pathogen detection and energy harvesting in biomedical applications. The work is centered on the design and validation of impedance spectroscopy-based biosensors that offer sensitive, and specific pathogen detection, crucial for timely healthcare interventions. A key component of this research is the multipotentiostat, a PCB board design incorporating four AFE chips that perform potentiostat operations like cyclic voltammetry simultaneously, with capabilities for Arduino Nano integration enabling both wired and wireless communication and data transfer. Additionally, the thesis explores piezoelectric energy harvesters, tailored to power these biosensors autonomously, addressing the limitations imposed by conventional power sources and promoting sustainability in medical devices. A significant contribution of this research is the development of a neural network-optimized framework that streamlines the process of designing and integrating these technologies effectively. The results demonstrate that integrating energy harvesting with optimized biosensor systems could significantly advance the field of portable and sustainable medical diagnostics, thereby offering robust tools for managing public health, particularly in resource-limited settings. This work not only furthers the development of autonomous biomedical devices but also sets the stage for future innovations in the integration of renewable energy solutions in medical technology.

Name dc:type.qualificationname

Doctor of Philosophy (PhD)

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Cambridge

Year dc:date.issued

2024

Author dc:creator

• Kandukuri, Tharun Reddy

Advisor dc:contributor.advisor

• Occhipinti, Luigi G

• Biomedical Devices
• Energy Harvesters
• Point of care
• Optimisation Algorithms

Licence

• https://www.repository.cam.ac.uk/bitstreams/0cb2c475-3425-496b-83f5-8ced0199b6ae/download
• http://purl.org/NET/rdflicense/allrightsreserved

Language dc:language

eng

DOI dc:identifier.doi

https://doi.org/10.17863/CAM.117741

OAI identifier oai:identifier

oai:www.repository.cam.ac.uk:1810/383246