(326i) Developing Multiscale Modeling Approaches to Understand Electrochemical Processes for Energy Conversion

Electrochemical processes are widely used in a variety of applications, including energy storage and conversion, chemical synthesis, and environmental remediation. These processes are expected to play a central role in the transition to net-zero carbon emissions and help address the looming climate crisis.[1] The complex nature of the electrode-electrolyte interface involving interactions across different time- and length-scales makes it extremely challenging to understand electrochemical processes. Rapid advancements in computational methods and high-performance computing in the recent years have enabled simulations to provide unprecedented insights into the atomistic structure of the reaction environment, elucidating reaction mechanisms and guiding catalyst design. However, a number of challenges still remain.These challenges include: (i) understanding the atomistic structure of the electrode-electrolyte interface under operando conditions, (ii) Estimating electrochemical reaction kinetics that are extremely important to understand reaction pathways and predict product selectivity in multi-step electrocatalytic reactions and (iii) Incorporating the effects of various components of the electrolyte environment including mass transport, double-layer charging and solution phase reactions in modeling studies. Accounting for all these aspects necessitates the development of a multiscale approach to understand and predict electrochemical processes for energy conversion.

In my research, we have addressed different aspects of the aforementioned challenges using a combination of simulation techniques including density functional theory based molecular dynamics (DFT-MD), ab-initio microkinetic simulations and continuum transport modeling. For instance, we used DFT-MD simulations of metal-water interfaces to understand their structure, estimate solvation energies and identify trends in the potential of zero charge.[2,3] We identify simple descriptors to predict these quantities, thereby circumventing the need for expensive DFT-MD simulations. Using constant-potential DFT simulations (in combination with experiments), we elucidate reaction mechanisms of complex multi-step electrocatalytic reactions including electrochemical CO2 reduction [4] and electrochemical biomass conversion [5,6]. These studies demonstrate the importance of understanding the pH and potential dependence of reactions for mechanistic analysis. We extend our observations to understand the effects of the pH and potential to establish guidelines for product selectivity in electrochemical CO2 reduction [7]. Furthermore, we present mechanisms behind the effects of the reaction environment (i.e. electrolyte pH, cations and anions) on the activity and selectivity of electrochemical processes. [8, 9] Finally, we develop a multi-scale approach that couples ab-initio microkinetics and continuum transport models to resolve the reaction environment during electrochemical CO2 reduction [10]. This study highlights the need for multi-dimensional, multiscale approaches to develop an in-depth understanding of electrochemical processes.

References (#: Co-first author, *: Corresponding author):

[1] Z. W. Seh , J. Kibsgaard , C. F. Dickens , I. Chorkendorff , J. K. Nørskov and T. F. Jaramillo , Science, 355 (2017)

[2] S. Liu, S. Vijay, M. Xu, A. Cao, H. Prats, G. Kastlunger, H. H. Heenen, N. Govindarajan*, Submitted (2023)

[3] S. R. Kelly#, H. H. Heenen#, N. Govindarajan#, K. Chan, J. K. Nørskov, J. Phys. Chem. C., 126 (2022)

[4] G. Kastlunger, L. Wang, N. Govindarajan, H. H. Heenen, S. Ringe, T. Jaramillo, C. Hahn, K. Chan, ACS Catalysis, 12 (2022)

[5] S. Liu#, N. Govindarajan#, H. Prats, K. Chan, Chem Catalysis, 2 (2022)

[6] S. Liu, Z. Mukadam, S. B. Scott, S. C. Sarma, M. M. Titirici, K.Chan, N. Govindarajan*, I. E. L. Stephens*, G. Kastlunger*, ChemRxiv (2023)

[7] G. Kastlunger, H. H. Heeenen, N. Govindarajan, ACS Catalysis, 13 (2023)

[8] N. Govindarajan, G. Kastlunger, H. H. Heenen, K. Chan, Chemical Science, 13 (2022)

[9] N. Govindarajan, A. Xu, K. Chan, Science, 375 (2022)

[10] N. Govindarajan, et al., In preparation (2023)