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A new electrocatalyst, NiO–CuO/G, was synthesized and tested as a cost-effective cathode for ORR in MFCs, showing high selectivity and outperforming a benchmark Pt/C catalyst.

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What they did

System
MFC

What worked

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Abstract

Background: Although microbial fuel cells (MFCs) represent a promising technology for capturing renewable energy from wastewater, their scaling-up is significantly limited by a slow-rate cathodic oxygen reduction reaction (ORR) and the development of a resilient anodic microbial community. In this study, mixed transition metal oxides of nickel and copper (Ni and Cu), supported on a graphene (G) (NiO–CuO/G) electrocatalyst, were synthesized and tested as a cost- effective cathode for ORR in MFCs. Electrochemical measurements of electrocatalyst were conducted using a rotating disk electrode (RDE) and linear sweep voltammetry (LSV) in a neutral electrolyte, and compared with a benchmark Pt/C catalyst. Furthermore, the long-term performance of the as-synthesized electrocatalyst was evaluated in a single- chamber MFC by measuring organic matter removal and polarization behavior. The successful enrichment of electro‑ active biofilm was also monitored using transmission electron microscopy and the Vitek2 compact system technique. Results: When compared with the benchmark platinum cathode, the NiO–CuO/G electrocatalyst exhibited high selectivity toward ORR. The rotating disk electrode (RDE) experiments reveal that ORR proceeds via a 4-electron ORR mechanism. Furthermore, the NiO–CuO/G electrocatalyst also exhibited a high power density of 2

Key findings

  • The NiO–CuO/G electrocatalyst exhibited high selectivity toward ORR compared to a benchmark platinum cathode.
  • The long-term performance of the NiO–CuO/G electrocatalyst was evaluated in a single-chamber MFC, showing efficient organic matter removal and polarization behavior.
  • The enrichment of electro-active biofilm was successfully monitored using transmission electron microscopy and the Vitek2 compact system technique.

Keywords

MFC3D modelingextracted

Identifiers

Journal
of Genetic Engineering
Year
2021