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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Bioelectrochemistryarrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Bioelectrochemistry
Article . 2018 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
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A novel model for predicting bioelectrochemical performance of microsized-MFCs by incorporating bacterial chemotaxis parameters and simulation of biofilm formation

Authors: Mohammad Kalantar; Mohammad Mahdi Mardanpour; Soheila Yaghmaei;

A novel model for predicting bioelectrochemical performance of microsized-MFCs by incorporating bacterial chemotaxis parameters and simulation of biofilm formation

Abstract

Bacterial transport parameters play a fundamental role in microbial population dynamics, biofilm formation and bacteria dispersion. In this study, the novel model was extended based on the capability of microsized microbial fuel cells (MFCs) as amperometric biosensors to predict the cells' chemotactic and bioelectrochemical properties. The model prediction results coincide with the experimental data of Shewanella oneidensis and chemotaxis mutant of P. aeruginosa bdlA and pilT strains, indicating the complementary role of numerical predictions for bioscreening applications of microsized MFCs. Considering the general mechanisms for electron transfer, substrate biodegradation, microbial growth and bacterial dispersion are the main features of the presented model. In addition, the genetic algorithm method was implemented by minimizing the objective function to estimate chemotaxis properties of the different strains. Microsized MFC performance was assessed by analyzing the microbial activity in the biofilm and the anolyte.

Related Organizations
Keywords

Shewanella, Bioelectric Energy Sources, Chemotaxis, Biosensing Techniques, Models, Biological, Biofilms, Pseudomonas aeruginosa, Computer Simulation, Algorithms

  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    6
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
6
Top 10%
Average
Average
Related to Research communities
Energy Research