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Microbial fuel cells (MFCs) are a class of ideal technologies that function via anaerobic respiration of electricigens, which bring current generation and environmental restoration together. An in-depth understanding of microbial metabolism is of great importance in engineering microbes to further improve their respiration. We employed flux balance analysis and selected Fe(iii) as a substitute for the electrode to simulate current-generating metabolism of Geobacter sulfurreducens PCA with a fixed acetate uptake rate. Simulation results indicated the fluxes of reactions directing acetate towards dissimilation to generate electrons increased under the suboptimal growth condition, resulting in an increase in the respiration rate and a decrease in the growth rate. The results revealed the competitive relationship between oxidative respiration and cell growth during the metabolism of microbe current generation. The results helped us quantitatively understand why microbes growing slowly have the potential to make good use of fuel in MFCs. At the same time, slow growth does not necessarily result in speedy respiration. Alternative respirations may exist under the same growth state due to redundant pathways in the metabolic network. The big difference between the maximum and minimum respiration mainly results from the total formate secretion. With iterative flux variability analysis, a relatively ideal model of variant of G. sulfurreducens PCA was reconstructed by deleting several enzymes in the wild model, which could reach simultaneous suboptimal growth and maximum respiration. Under this ideal condition, flux towards extracellular electron transfer rather than for biosynthesis is beneficial for the conversion of organic matter to electricity without large accumulations of biomass and electricigens may maximize utilization of limited fuel. Our simulations will provide an insight into the enhanced current-generating mechanism and identify theoretical range of respiration rates for guiding strain improvement in MFCs.

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The compound fault diagnosis of rolling bearings has become a hot topic. In this study, a novel method based on adaptive sparse denoising (ASD) combined with periodicity weighted spectrum separation (PWSS) is proposed to diagnose compound faults in rolling bearings. Specifically, ASD reveals fault types and PWSS separates compound faults. First, ASD determines regularization parameters adaptively using the proposed compound frequency multi D-norm, thereby denoising the raw vibration signal and revealing fault types. Then, PWSS constructs the time-frequency spectrum (TFS) and uses the fault periodicity from ASD to determine the time occurrence positions of the repetitive impulses. With this time occurrence position information, a weight matrix is constructed to reweight the TFS. Finally, through the reweighted TFS, PWSS extracts and separates repetitive impulses from compound faults. The performance of the proposed method is validated in both simulation and experimental studies. The results demonstrate that the proposed method can successfully diagnose and separate the compound faults.