Innovative statistical interpretation of Shewanella oneidensis microbial fuel cells data

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.

Bioelectrocatalysis and More

“…The applied statistical interpretation of the results from a significant amount of MFCs data showed that design parameters were the dominating factors when the necessity bacterial biofilm was developed. The same conclusion was made when the expanded uncertainties of the main operational characteristics were evaluated (Figure 3) [28]. a.…”

“…Through evaluation of the expanded uncertainty of microbial fuel cells, the variations in electrodes resistance and mostly difference in their electrochemical accessible surface area were identified as the main parameters causing irreproducibility of MFCs responses [24,25]. The idea of using statistical approaches for analysis of biofuel cells was further enlarged to include Design of Experiments, Principal Component Analysis (PCA) and Canonical Correspondence Analysis [7,[26][27][28][29]. The applied statistical interpretation of the results from a significant amount of MFCs data showed that design parameters were the dominating factors when the necessity bacterial biofilm was developed.…”

Low-temperature microbial and direct conversion of lignocellulosic biomass to electricity: Advances and challenges

“…A statistical analysis using PCA/UMR approach was conducted for a Shewanella oneidensis MFC to discern the relative importance of various factor on MFC performance [101]. Buffer pH is a main parameter determining MFC performance.…”

Influence of anode surface chemistry on microbial fuel cell operation

“…[22]. The inability to distinguish the influence of only one parameter from the whole set of parameters that are usually altered through the commonly used surface modification techniques is a result of the intercorrelation between the introduced variances in the parameters’ magnitudes [23]. Therefore, the impact of the anode surface chemistry on current and power output in MFC should be studied using flat electrode material, thus de-coupling chemical effects from change in surface morphology.…”