Rational Design of Graphene Derivatives for Electrochemical Reduction of Nitrogen to Ammonia

• Published in: ACS nano Vol. 15; no. 11; pp. 17275 - 17298
• Main Authors: Majumder, Mandira, Saini, Haneesh, Dědek, Ivan, Schneemann, Andreas, Chodankar, Nilesh R, Ramarao, Viswanatha, Santosh, Mysore Sridhar, Nanjundan, Ashok Kumar, Kment, Štěpán, Dubal, Deepak, Otyepka, Michal, Zbořil, Radek, Jayaramulu, Kolleboyina
• Format: Journal Article
• Language: English
• Published: American Chemical Society 23.11.2021
• Subjects: electrocatalyst; nitrogen reduction reaction (NRR); hybrid; graphene derivative; defects; doping; machine learning; graphene
• ISSN: 1936-0851; 1936-086X
• DOI: 10.1021/acsnano.1c08455

The conversion of nitrogen to ammonia offers a sustainable and environmentally friendly approach for producing precursors for fertilizers and efficient energy carriers. Owing to the large energy density and significant gravimetric hydrogen content, NH3 is considered an apt next-generation energy carrier and liquid fuel. However, the low conversion efficiency and slow production of ammonia through the nitrogen reduction reaction (NRR) are currently bottlenecks, making it an unviable alternative to the traditional Haber–Bosch process for ammonia production. The rational design and engineering of catalysts (both photo- and electro-) represent a crucial challenge for improving the efficiency and exploiting the full capability of the NRR. In the present review, we highlight recent progress in the development of graphene-based systems and graphene derivatives as catalysts for the NRR. Initially, the history, fundamental mechanism, and importance of the NRR to produce ammonia are briefly discussed. We also outline how surface functionalization, defects, and hybrid structures (single-atom/multiatom as well as composites) affect the N2 conversion efficiency. The potential of graphene and graphene derivatives as NRR catalysts is highlighted using pertinent examples from theoretical simulations as well as machine learning based performance predictive methods. The review is concluded by identifying the crucial advantages, drawbacks, and challenges associated with principal scientific and technological breakthroughs in ambient catalytic NRR.