Temperature, Inocula and Substrate: Contrasting Electroactive Consortia, Diversity and Performance in Microbial Fuel Cells
Overview
Affiliations
The factors that affect microbial community assembly and its effects on the performance of bioelectrochemical systems are poorly understood. Sixteen microbial fuel cell (MFC) reactors were set up to test the importance of inoculum, temperature and substrate: Arctic soil versus wastewater as inoculum; warm (26.5°C) versus cold (7.5°C) temperature; and acetate versus wastewater as substrate. Substrate was the dominant factor in determining performance and diversity: unexpectedly the simple electrogenic substrate delivered a higher diversity than a complex wastewater. Furthermore, in acetate fed reactors, diversity did not correlate with performance, yet in wastewater fed ones it did, with greater diversity sustaining higher power densities and coulombic efficiencies. Temperature had only a minor effect on power density, (Q: 2 and 1.2 for acetate and wastewater respectively): this is surprising given the well-known temperature sensitivity of anaerobic bioreactors. Reactors were able to operate at low temperature with real wastewater without the need for specialised inocula; it is speculated that MFC biofilms may have a self-heating effect. Importantly, the warm acetate fed reactors in this study did not act as direct model for cold wastewater fed systems. Application of this technology will encompass use of real wastewater at ambient temperatures.
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PMID: 39834375 PMC: 11743565. DOI: 10.3389/fmicb.2024.1511142.
Yoshizu D, Shimizu S, Tsuchiya M, Tomita K, Kouzuma A, Watanabe K Microorganisms. 2024; 12(8).
PMID: 39203487 PMC: 11356707. DOI: 10.3390/microorganisms12081645.
Microbial Biofilms: Features of Formation and Potential for Use in Bioelectrochemical Devices.
Perchikov R, Cheliukanov M, Plekhanova Y, Tarasov S, Kharkova A, Butusov D Biosensors (Basel). 2024; 14(6).
PMID: 38920606 PMC: 11201457. DOI: 10.3390/bios14060302.
Christgen B, Spurr M, Milner E, Izadi P, McCann C, Yu E Front Microbiol. 2023; 14:1199286.
PMID: 38075904 PMC: 10702221. DOI: 10.3389/fmicb.2023.1199286.
Machine Learning in Bioelectrocatalysis.
Huang J, Gao Y, Chang Y, Peng J, Yu Y, Wang B Adv Sci (Weinh). 2023; 11(2):e2306583.
PMID: 37946709 PMC: 10787072. DOI: 10.1002/advs.202306583.