Date of Award
Doctor of Philosophy (PhD)
Electrical & Computer Engineering
Electrical and Computer Engineering
In this work, we present a novel abiotic glucose fuel cell with battery-less remote access. In the presence of a glucose analyte, we characterized the power generation and biosensing capabilities. This system is developed on a flexible substrate in bacterial nanocellulose with gold nanoparticles used as a conductive ink for piezoelectric deposition based printing. The abiotic glucose fuel cell is constructed using colloidal platinum on gold (Au-co-Pt) and a composite of silver oxide nanoparticles and carbon nanotubes as the anodic and cathodic materials. At a concentration of 20 mM glucose, the glucose fuel cell produced a maximum open circuit voltage of 0.57 V and supplied a maximum short circuit current density of 0.581 mA/cm2 with a peak power density of 0.087 mW/cm2 . The system was characterized by testing its performance using electrochemical techniques like linear sweep voltammetry, cyclic voltammetry, chronoamperometry in the presence of various glucose level at the physiological temperatures. An open circuit voltage (Voc) of 0.43 V, short circuit current density (Isc) of 0.405 mA/cm2 , and maximum power density (Pmax) of 0.055 mW/cm2 at 0.23 V were achieved in the presence of 5 mM physiologic glucose. The results indicate that glucose fuel cells can be employed for the development of a self-powered glucose sensor. The glucose monitoring device demonstrated sensitivity of 1.87 uA/mMcm2 and a linear dynamic range of 1 mM to 45 mM with a correlation coefficient of 0.989 when utilized as a self-powered glucose sensor. For wireless communication, the incoming voltage from the abiotic fuel cell was fed to a low power microcontroller that enables battery less communication using NFC technology. The voltage translates to the NFC module as the digital signals, which are displayed on a custom-built android application. The digital signals are converted to respective glucose concentration using a correlation algorithm that allows data to be processed and recorded for further analysis. The android application is designed to record the time, date stamp, and other independent features (e.g. age, height, weight) with the glucose measurement to allow the end-user to keep track of their glucose levels regularly. Analytics based on in-vitro testing were conducted to build a machine learning model that enables future glucose prediction for 15, 30 or 60 minutes.
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"A Preventive Medicine Framework for Wearable Abiotic Glucose Detection System"
(2022). Doctor of Philosophy (PhD), Dissertation, Electrical & Computer Engineering, Old Dominion University, DOI: 10.25777/b5av-r629