Enhanced ORR Activity of Fepc Functionalized Graphene Via Substrate Doping and/or Ligand Exchange: A First Principles Study

Platinum, an expensive and highly precious metal, is currently used for both electrodes of a Proton-exchange membrane (PEM) fuel cell. The cathode reaction, in which the O₂ molecule is reduced (oxygen reduction reaction (ORR)), has been a major barrier in the development of economically enticing technology. High-performance catalysts fabricated with little to no platinum for the cathode of a PEM fuel cell remain a significant challenge for commercial application due to the sluggish kinetics. Because of this, there are many non-precious catalysts currently being investigated to find suitable alternative that offers better integrity and stability than the standard platinum-based catalysts. A single-metal atom fuel cell catalyst, iron phthalocyanine supported on graphene (GFePc) has showed promise as a potential alternative through its performance and stability. In the present work, ligand-exchanges (F-, NH₂-) of FePc and substrate doping (B-doping, N-doping) of graphene are explored to tune the performance of GFePc using spin-polarized density functional theory (DFT) calculations for the associative mechanism, where ORR and water formation reaction (WFR) occur. Additionally, increased doping percentages and a combination of a ligand-exchange and substrate doping were also explored. Some of the calculations performed in this study include ORR and water formation reaction (WFR) processes in mild practical fuel cell operating conditions, elementary electrochemical catalytic reaction steps, and free energy landscapes. Based on our calculations and analysis, ~1 at. % boron doping seems to improve the performance of GFePc by lowering the over potential required for ORR and WFR to take place to be ~0.63 eV.

Acknowledgements: The work is supported by ACS-PRF [58740-UR6] and used the Extreme Science and Engineering Discovery Environment (XSEDE) TACC at the stampede2 through allocation [TGDMR140131]. This work utilized resources from the University of Colorado Boulder Research Computing Group, which is supported by the National Science Foundation (awards ACI-1532235 and ACI-1532236), the University of Colorado Boulder, and Colorado. PCC Cluster, NM Consortium, NM.