Theoretical Modeling of Metal Nitride/Oxide Pairs for Sustainable Ammonia Production Using Thermochemical Cycles

Kurzfassung

Ammonia is a versatile chemical commodity at global scale. It is required for the production of fertilizers, medicine and it can serve as an energy carrier. However, the common production of ammonia using the Haber-Bosch process requires large amounts of energy, which is typically provided by fossil sources. Therefore, sustainable ammonia synthesis has become an increasingly active research topic over the past years. Therein the solar thermochemical ammonia synthesis route has great potential to provide a green alternative to the current practice. The synthesis is performed by utilizing a metal nitride to metal oxide redox cycle. In order to yield the largest amount of ammonia per cycle, the choice of the right metal nitride/oxide pair is crucial. Previous studies have focused on the reaction thermodynamics, however, failed to show reliable correlation with ammonia production. For a rational selection of suitable compounds, the rate determining step of the underlying reactions needs to be identified first. A comparative ab-initio computation study was performed with a focus on the ammonia formation reaction of binary metal nitrides/oxide surfaces, using H2O as the hydrogen source during the hydrolysis. For selected main group and transition metal nitrides, the stable surfaces appearing in a Wulff construction was determined using density function theory and the hydrolysis mechanism was studied. The favorable reaction pathways involve water adsorption on a surface cation, proton transfers to surface nitrogen, replacement of M-N by M-O and N-H bonds thereby stepwise forming ammonia, which desorbs. Based on the results design parameters can be defined for an optimal nitride carrier for thermochemical ammonia synthesis via hydrolysis reaction. The highest impact parameters were found to be bulk and surface structures, polarity of the nitride bonds and suitable metal elements. These defined criteria enable a selective computational high-throughput screening to identify novel nitride compounds for further experimental validation as part of future work.