Modelling Electrocatalyst Materials for N2 Reduction Reaction and CO2 Reduction Reaction by High-Throughput Computation

Yan Jiao

School of Chemical Engineering and Advanced Materials, The University of Adelaide

EXTENDED ABSTRACT: The dwindling supply of fossil fuels and increasing climate change gives us great motivation to explore alternative power sources to drive our highly automatized society. Under this background, establishing reliable, clean and sustainable energy supplies are of great importance. The large scale production of green electricity from renewable energy resources is now commercially available, including those generated from solar, wind, tide, and geothermal. However, these renewable energy resources are often intermittent and are not generated in a way that concerts with the need for electricity. Therefore the storage of green electricity is much needed and using electrochemical methods to realize energy conversion and storage hold great promise. These electrochemical methods can help convert green electricity–produced from renewable energy resources–to chemicals and fuels; and vice versa. Among these electrochemical energy conversion reactions, hydrogen evolution reaction (HER), oxygen reduction reaction (ORR), CO2 reduction reaction (CRR), and N2 reduction reaction (NRR) are the most studied, due to their respective roles in hydrogen production, hydrogen utilization by fuel cells, and fuel generation by using carbon or nitrogen as energy carriers. For many of these energy conversion reactions, the current bottleneck still lies in catalyst material performance–electrocatalyst with high activity and high selectivity is much-needed.[1] In the past, the discovery and design of these materials are generally experiment-based, which lead to the low turn-around rate to evaluate new materials. Recently, the research methodology of combining experiment and molecular modelling has greatly accelerated the materials discovery process. Such acceleration can be attributed to (1) the in-depth understanding of the electrochemical reactions that are obtained from the corroboration of experimental observation and theoretical modelling and (2) the ability to evaluate the performance of a group of materials by high throughput computation and (3) the greatly improved characterization technique to identify the catalyst surface and reaction intermediate in Operando. With regarding high-throughput computation, our group has done some work about N2 reduction reaction (NRR) and CO2 Reduction Reaction. About N2 reduction reaction, we employed density functional theory calculations to evaluate the performance of single-atom catalysts (SACs) supported on nitrogen-doped carbons. Based on these calculations, we obtained the activity trends, electronic structure origins, and design strategies of these SAC electrocatalysts. Our work evaluated in total 60 SACS, based on a systematic study of 20 different transition metal (TM) centers coordinated three types of nitrogen-doped carbon substrates. Our study shows that the intrinsic activity trends could be established on the basis of the nitrogen adatom adsorption energy (ΔEN*). Furthermore, the influence of metal and support (ligands) on ΔEN* proved to be related to the bonding/antibonding orbital population and regulating the scaling relations for adsorption of intermediates, respectively. Accordingly, a two-step strategy is proposed for improving the NRR activity of TM-SACs, which involves both selections of the most promising family of SACs, and activity improvement on the best candidate in the identified family-via tuning the adsorption strength of the key intermediates.[2] The electrochemical CO2 reduction reaction (CRR) is by nature quite complex, as many possible reaction pathways and end products exist at the same time. Therefore, selectivity is a persistent challenge for the design of CRR electrocatalysts. By high-throughput computation, we discovered that oxygen-bound intermediates play an important role in directing the CRR selectivity, especially for the selectivity for different C2 products. By calculating the reaction pathway including thermodynamics and kinetics, and adsorption behavior of intermediates, we tried to reveal the link between product selectivity and surface oxygen affinity. Our earliest work shows that providing sites with high oxygen affinity could provide a second active site for C-C coupling toward C2 products generation.[3] Later on, we discovered that selectivity toward different C2 products is related to surface oxygen affinity.[4-6] Based on such design principle obtained from high-throughput computation, we discovered that surface that can retain oxygen atoms could facilitate the production of diols, a highly sought after product.[7] Our work will help with the design strategies of next-generation electrocatalysts with high CRR selectivity. In summary, high-throughput computation harnesses the increasing power of computing capability and can greatly reduce the turn-around time for designing a new electrocatalyst material. By evaluating a group of materials, we can identify the activity descriptor for that group of materials, therefore, being able to identify the best performance and reveal the structure-property relationship. In addition, the high-throughput computation could generate a large dataset that contains information on different chemical structures, adsorption performance, charge distribution, etc. Such adataset paves the way for the next generation of machine learning assisted materials discovery.

REFERENCES
[1] Chemical Society Reviews, 2015, 44, 2060.
[2] Journal of the American Chemical Society 2019, 141, 9664.
[3] Applied Surface Science, 2021, 540, 148293.
[4] Nano Energy, 2020, 71, 104601.
[5] Journal of Materials Chemistry A, 2021, 9, 6345.
[6] Energy and Environmental Science, 2021, 14, 3912-3930.
[7] Chemical Sciences, 2021, 12, 8079.
BIOGRAPHY: Associate Professor Yan Jiao is an Australian Research Council (ARC) Future Fel