CoMn2O4-supported functionalized carbon nanotube: efficient catalyst for oxygen reduction in microbial fuel cells
Source abstract
Abstract
Recently, the synthesis of nonprecious metal catalysts with low cost and high oxygen reduction reaction (ORR) efficiency is paid much attention in field of microbial fuel cells (MFCs). Transition metal oxides (AMn2O4, A = Co、Ni, and Zn) supported on carbon materials such as graphene and carbon nanotube exhibit stronger electroconductivity and more active sites comparing to bare AMn2O4. Herein, we demonstrate an easy operating Hummer’s method to functionalize carbon nanotubes (CNTs) with poly (diallyldimethylammonium chloride) in order to achieve effective loading of CoMn2O4 nanoparticles, named CoMn2O4/PDDA-CNTs (CMODT). After solvothermal treatment, nanoscale CoMn2O4 particles (~ 80 nm) were successfully attached on the noncovalent functionalized carbon nanotube. Results show that such composites possess an outstanding electrocatalytic activity towards ORR comparable to the commercial Pt/C catalyst in neutral media. Electrochemical detections as cyclic voltammogram (CV) and rotating ring-disk electrode tests (RRDE) showed that the potential of oxygen reduction peak of 30% CMODT was at − 0.3 V (vs Ag/AgCl), onset potential was at + 0.4 V. Among them, 30% CMODT composite appeared the best candidate of oxygen reduction via 3.9 electron transfer pathway. When 30% CMODT composite was utilized as cathode catalyst in air cathode MFC, the reactor obtained 1020 mW m−2 of the highest maximum power density and 0.781 V of open circuit voltage. The excellent activity and low cost (0.2 $ g−1) of the hybrid materials demonstrate the potential of transition metal oxide/carbon as effective cathode ORR catalyst for microbial fuel cells.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Microbial Fuel Cells and Bioremediation · Electrochemical sensors and biosensors · Electrochemical Analysis and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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