Polypyrrole RVC biofuel cells for powering medical implants

© 2017 IEEE. Batteries for implanted medical devices such as pacemakers typically require surgical replacement every 5 to 10 years causing stress to the patient and their families. A Biofuel cell uses two electrodes with enzymes embedded to convert sugar into electricity. To evaluate the power producing capabilities of biofuel cells to replace battery technology, polypyrrole electrodes were fabricated by compression with Glucose oxidase and Laccase. Vitreous carbon was added to increase the conductivity, whilst glutaraldehyde acted as a crosslinking molecule. A maximum open circuit potential of 558.7 mV, short circuit current of 1.09 mA and maximum power of 0.127 mW was obtained from the fuel cells. This was able to turn on a medical thermometer through a TI BQ25504 energy harvesting circuit, hence showing the powering potential for biomedical devices

A simple microbial fuel cell model for improvement of biomedical device powering times

© 2014 IEEE. This study describes a Matlab based Microbial Fuel Cell (MFC) model for a suspended microbial population, in the anode chamber for the use of the MFC in powering biomedical devices. The model contains three main sections including microbial growth, microbial chemical uptake and secretion and electrochemical modeling. The microbial growth portion is based on a Continuously Stirred Tank Reactor (CSTR) model for the microbial growth with substrate and electron acceptors. Microbial stoichiometry is used to determine chemical concentrations and their rates of change and transfer within the MFC. These parameters are then used in the electrochemical modeling for calculating current, voltage and power. The model was tested for typically exhibited MFC characteristics including increased electrode distances and surface areas, overpotentials and operating temperatures. Implantable biomedical devices require long term powering which is the main objective for MFCs. Towards this end, our model was tested with different initial substrate and electron acceptor concentrations, revealing a four-fold increase in concentrations decreased the power output time by 50%. Additionally, the model also predicts that for a 35.7% decrease in specific growth rate, a 50% increase in power longevity is possible