Cellular non-linear network model of microbial fuel cell

Phillips, Neil; Sirakoulis, Georgios Ch; Tsompanas, Michail Antisthenis; Adamatzky, Andrew; Ieropoulos, Ioannis; Greenman, John

Abstract

© 2017 Elsevier B.V. A cellular non-linear network (CNN) is a uniform regular array of locally connected continuous-state machines, or nodes, which update their states simultaneously in discrete time. A microbial fuel cell (MFC) is an electro-chemical reactor using the metabolism of bacteria to drive an electrical current. In a CNN model of the MFC, each node takes a vector of states which represent geometrical characteristics of the cell, like the electrodes or impermeable borders, and quantify measurable properties like bacterial population, charges produced and hydrogen ion concentrations. The model allows the study of integral reaction of the MFC, including temporal outputs, to spatial disturbances of the bacterial population and supply of nutrients. The model can also be used to evaluate inhomogeneous configurations of bacterial populations attached on the electrode biofilms.

Citation

Sirakoulis, G. C., Phillips, N., Tsompanas, M. A., Adamatzky, A., Ieropoulos, I., & Greenman, J. (2017). Cellular non-linear network model of microbial fuel cell. BioSystems, 156-157, 53-62. https://doi.org/10.1016/j.biosystems.2017.04.003

• Journal Article Type: Article
• Acceptance Date: Apr 12, 2017
• Online Publication Date: Apr 17, 2017
• Publication Date: Jun 1, 2017
• Deposit Date: Apr 26, 2017
• Publicly Available Date: Apr 17, 2018
• Journal: BioSystems
• Print ISSN: 0303-2647
• Electronic ISSN: 1872-8324
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 156-157
• Pages: 53-62
• DOI: https://doi.org/10.1016/j.biosystems.2017.04.003
• Keywords: microbial fuel cells, cellular non-linear network, spatial models
• Public URL: https://uwe-repository.worktribe.com/output/883811
• Publisher URL: http://dx.doi.org/10.1016/j.biosystems.2017.04.003
• Related Public URLs: http://www.sciencedirect.com/science/article/pii/S0303264717300758