(201f) Understanding Catalyst Dynamics in Formate Dehydrogenation for Hydrogen Storage: Insights into Operational Efficiency and Deactivation Mechanisms

Formic acid is being actively pursued as a secure and potentially effective liquid organic hydrogen carrier (LOHC). Given its capability to store hydrogen through chemical bonds, it necessitates catalysis by efficient and durable catalysts for both storage and release processes. However, there remains a lack of comprehensive understanding regarding the mechanisms involved in formate dehydrogenation and catalyst deactivation.

In my presentation, I delve into the exploration of the solution-phase formate dehydrogenation mechanism over Pd(111) and the significant role of operating potential in influencing both dehydrogenation activity and potential deactivation mechanisms. Notably, the operating potential plays a critical role in determining the mechanisms of both dehydrogenation and deactivation during formate dehydrogenation catalysis. Various steps involve charge-transfer processes, and their associated barriers and energies exhibit a strong dependence on the catalyst's work function or potential under reaction conditions. Through implicit solvent and constant-potential density functional theory (DFT) calculations, we observe that under the proposed conditions, formate tends to accumulate on the surface, occupying a substantial portion of it in a tightly bound oxygen-down configuration. This accumulation not only obstructs numerous surface sites but also escalates the activation energy for the process, suggesting that formate might significantly contribute to catalyst deactivation over time. Our analysis of the potential dependency of potential rate-limiting steps provides valuable insights into the prospect of adjusting this parameter, aiming at optimizing the reaction's activation energy and deepening our comprehension of the time-dependent deactivation effects observed experimentally. Additionally, I contrast these operational decisions with conventional thermal conversion pathways, serving as a benchmark for enhancing operational and conversion efficiency. The ultimate objective is to evaluate the hydrogen production rate concerning potential (U), temperature (T), and surface coverage, thereby examining the intricate interplay between thermochemical and electrochemical processes.

Furthermore, I also highlight the role of the Pd phase in influencing dehydrogenation. For instance, Pd²⁺ tends to weaken formate binding and enhance H adsorption relative to Pd⁰, potentially counteracting formate-based catalyst poisoning. Illustrated in Figure 1, the thermal operation mode is represented by the vertical red line (at the PZC of Pd), whereas the room temperature electrochemical route is depicted by the horizontal blue line. The figure underscores that electrochemical conversion routes can significantly enhance dehydrogenation activity by several orders of magnitude, regardless of the maintained temperature. Moreover, activity can be further fine-tuned at these potentials by elevating the operating temperature.

Hence, my research offers a qualitative description of the interplay between operating reaction conditions and operating voltage. Additionally, the dehydrogenation mechanism is slated for further investigation, taking into account the effect of support, composition (Pd/Ag), and associated phase changes as influenced by the operating potentials.

Accelerating Materials Discovery for Electrochemical Reactions Using Iterative Machine Learning Strategies with Theoretical and Experimental Data

Towards the end, I also briefly demonstrate how the integration of physical models, high-throughput simulations, density functional theory (DFT), and ML can accelerate the process of materials discovery and catalyst design. Additionally, I will discuss how an iterative ML approach that utilizes both experimental and theoretical data can reveal insights on the relationship between structure and properties, enabling the prediction of experimentally relevant and stable Oxygen reduction reaction (ORR) catalysts. This ML methodology offers benefits over ML strategies that rely solely on theoretically generated materials data. Furthermore, this strategy can also be applied effectively to other electrochemical reactions. Our efforts also include an integration of these experimental and theoretical data via the CatHub (https://www.catalysis-hub.org/) Python API. The platform includes modules with a command line interface to access and upload data.

Figure 1. A heatmap for the variation of dehydrogenation rate constants against the operational choices of temperature and the electrode potential. the thermal mode of operation is denoted by the vertical red line (at the PZC of Pd), while the room temperature electrochemical route is shown via the horizontal blue line.