{"id":{"repo_id":"cambridge","oai_identifier":"oai:www.repository.cam.ac.uk:1810/383246"},"canonical_url":"https://search.dev.ndltd.org/etd/cambridge/oai:www.repository.cam.ac.uk:1810/383246","repository":{"repo_id":"cambridge","name":"Cambridge University","base_url":"https://api.repository.cam.ac.uk/server/oai/request"},"display":{"title":"AI-assisted Development of Energy Harvesters for POCT","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Kandukuri, Tharun Reddy"],"institution":"University of Cambridge","degree_name":"Doctor of Philosophy (PhD)","degree_level":"Doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Occhipinti, Luigi G"],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-12-21","date_published":"2024-12-21","updated_at":"2026-07-24T01:33:23Z","subjects":["Biomedical Devices","Energy Harvesters","Point of care","Optimisation Algorithms"],"languages":["eng"],"rights":[],"rights_urls":["https://www.repository.cam.ac.uk/bitstreams/0cb2c475-3425-496b-83f5-8ced0199b6ae/download","http://purl.org/NET/rdflicense/allrightsreserved"],"identifier_entries":[]},"links":{"outbound_url":"https://doi.org/10.17863/CAM.117741","outbound_label":"DOI","outbound_source":"dc:identifier.doi"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Occhipinti, Luigi G"]},{"key":"dc:creator","label":"Author","values":["Kandukuri, Tharun Reddy"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2024-12-21"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Cambridge"]},{"key":"dc:relation.isreferencedby.uri","label":"Dc Relation Isreferencedby URI","values":["https://www.repository.cam.ac.uk/handle/1810/383246"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Biomedical Devices","Energy Harvesters","Point of care","Optimisation Algorithms"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["https://www.repository.cam.ac.uk/bitstreams/0cb2c475-3425-496b-83f5-8ced0199b6ae/download","http://purl.org/NET/rdflicense/allrightsreserved"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.17863/CAM.117741"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://www.repository.cam.ac.uk/bitstreams/4198fbdf-f71b-4e90-abff-d354182c957d/download"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:format.checksum.md5","label":"Dc Format Checksum Md5","values":["87eda9de84448d1f82354d60eee3eb5f","679abf135454aed8aa974da9d87a1585"]},{"key":"dc:title","label":"Title","values":["AI-assisted Development of Energy Harvesters for POCT"]}]}],"canonical_facts":{"dc:contributor.advisor":["Occhipinti, Luigi G"],"dc:creator":["Kandukuri, Tharun Reddy"],"dc:date.issued":["2024-12-21"],"dc:description.abstract":["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."],"dc:format.checksum.md5":["87eda9de84448d1f82354d60eee3eb5f","679abf135454aed8aa974da9d87a1585"],"dc:identifier.doi":["https://doi.org/10.17863/CAM.117741"],"dc:identifier.uri":["https://www.repository.cam.ac.uk/bitstreams/4198fbdf-f71b-4e90-abff-d354182c957d/download"],"dc:language":["eng"],"dc:publisher.institution":["University of Cambridge"],"dc:relation.isreferencedby.uri":["https://www.repository.cam.ac.uk/handle/1810/383246"],"dc:rights":["https://www.repository.cam.ac.uk/bitstreams/0cb2c475-3425-496b-83f5-8ced0199b6ae/download","http://purl.org/NET/rdflicense/allrightsreserved"],"dc:subject":["Biomedical Devices","Energy Harvesters","Point of care","Optimisation Algorithms"],"dc:title":["AI-assisted Development of Energy Harvesters for POCT"],"dc:type":["Thesis"],"dc:type.qualificationlevel":["Doctoral"],"dc:type.qualificationname":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T01:33:23Z"}