Optimisation of Microbial Fuel Cells (MFCs) through bacterial-robot interaction

Theodosiou, Pavlina

Abstract

For over 100 years, Microbial Fuel Cells (MFCs) have been developed as eco-friendly alternatives for generating electricity via the oxidation of organic matter by bacteria. In the early 2000s, collectives of MFCs were proven fea-sible energy providers for low-power robots such as Gastrobot and EcoBots. Even though individual MFC units are low in power, significant progress has been achieved in terms of MFC materials and configurations, enabling them to generate higher output levels. However, up to this date, MFCs are produced and matured using conventional laboratory methods that can take up to three months to bring the MFCs to their maximum power aptitudes.
In this work, an approach to use a low-cost (£1.5k) RepRap liquid handling robot called EvoBot was employed with the aim to automate the maturing process of MFCs and allow them to reach their maximum power ability in a shorter time span. Initially, the work focused on establishing an interface and interconnection between the living cells (in the MFC) and the robotic platform, and investigating whether the MFC voltage can trigger a feedback loop feeding mechanism. It was shown that the robot successfully matured the MFCs in just 6 days and, they were also 1.4 times more powerful than conventionally matured MFCs (from 19.1 mW/m2 to 26.5 mW/m2).
This work took a rounded approach in improving the overall MFC perfor-mance. 3D-printable materials that can emerge from EvoBot were investi-gated for fabricating MFCs. MFCs employing these materials improved their power output by almost 50% (from 66μW to 130 μW) compared to the ones based on conventional, fluorinated materials. Furthermore, EvoBot was able to improve the fuel supply frequency and composition using evolutionally algorithms. For the first time, this project has demonstrated that the fabrica-tion, maintenance and power generation of MFCs can be optimised via the interaction and support of a dedicated robotic system.

• Thesis Type: Thesis
• Publicly Available Date: Sep 2, 2019
• Public URL: https://uwe-repository.worktribe.com/output/1490990
• Contract Date: Sep 2, 2019
• Award Date: Sep 2, 2019