JCVI: Innovative Statistical Interpretation of Shewanella oneidensis Microbial Fuel Cells Data.
 
 
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Babanova S, Bretschger O, Roy J, Cheung A, Artyushkova K, Atanassov P

Innovative Statistical Interpretation of Shewanella oneidensis Microbial Fuel Cells Data.

Physical Chemistry Chemical Physics : PCCP. 2014 Apr 16; 16: 8956-69.

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Abstract

The last decade of research has made significant strides toward practical applications of Microbial Fuel Cells (MFCs); however, design improvements and operational optimization cannot be realized without equally considering engineering designs and biological interfacial reactions. In this study, the main factors contributing to MFCs' overall performance and their influence on MFC reproducibility are discussed. Two statistical approaches were used to create a map of MFC components and their expanded uncertainties, principal component analysis (PCA) and uncertainty of measurement results (UMR). PCA was used to identify the major factors influencing MFCs and to determine their ascendency over MFC operational characteristics statistically. UMR was applied to evaluate the factors' uncertainties and estimate their level of contribution to the final irreproducibility. In order to simplify the presentation and concentrate on the MFC components, only results from Shewanella spp. were included; however, a similar analysis could be applied for any DMRB or microbial community. The performed PCA/UMR analyses suggest that better reproducibility of MFC performance can be achieved through improved design parameters. This approach is exactly opposite to the MFC optimization and scale up approach, which should start with improving the bacteria-electrode interactions and applying these findings to well-designed systems.