Tuning Two-Dimensional Phthalocyanine Dual Site Metal–Organic Framework Catalysts for the Oxygen Reduction Reaction

Authors: 
Lingze Wei, Md Delowar Hossain, Gan Chen, Gaurav A. Kamat, Melissa E. Kreider, Junjie Chen, Katherine Yan, Zhenan Bao, Michal Bajdich, Michaela Burke Stevens , Thomas Francisco Jaramilo
Year of publication: 
2024

Metal–organic frameworks (MOFs) offer an interesting opportunity for catalysis, particularly for metal–nitrogen–carbon (M–N–C) motifs by providing an organized porous structural pattern and well-defined active sites for the oxygen reduction reaction (ORR), a key need for hydrogen fuel cells and related sustainable energy technologies. In this work, we leverage electrochemical testing with computational models to study the electronic and structural properties in the MOF systems and their relationship to ORR activity and stability based on dual transitional metal centers. The MOFs consist of two M1 metals with amine nodes coordinated to a single M2 metal with a phthalocyanine linker, where M1/M2 = Co, Ni, or Cu. Co-based metal centers, in particular Ni–Co, demonstrate the highest overall activity of all nine tested MOFs. Computationally, we identify the dominance of Co sites, relative higher importance of the M2 site, and the role of layer M1 interactions on the ORR activity. Selectivity measurements indicate that M1 sites of MOFs, particularly Co, exhibit the lowest (<4%), and Ni demonstrates the highest (>46%) two-electron selectivity, in good agreement with computational studies. Direct in situ stability characterization, measuring dissolved metal ions, and calculations, using an alkaline stability metric, confirm that Co is the most stable metal in the MOF, while Cu exhibits notable instability at the M1. Overall, this study reveals how atomistic coupling of electronic and structural properties affects the ORR performance of dual site MOF catalysts and opens new avenues for the tunable design and future development of these systems for practical electrochemical applications. Full computational dataset available at https://www.catalysis-hub.org/publications/WeiTuning2024

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