Structurally ordered intermetallic electrocatalysts and anticorrosive carbon materials for durable green hydrogen energy systems: A theory-guided experimental validation

Abstract

The 21st-century modern society widely recognizes the critical importance of developing advanced energy technologies. Nations worldwide are increasingly committed to achieving carbon neutrality by investing in environmentally sustainable energy storage and conversion technologies with renewable energy sources. Among renewable energy sources, hydrogen is attracted as a crucial resource in achieving net-zero and sustainable development goals. Accelerating the transition to a hydrogen society involves water electrolysis systems and fuel cells that produce and utilize green hydrogen. This thesis explores two key electrochemical applications: polymer electrolyte membrane fuel cells (PEMFCs) and anion exchange membrane water electrolyzers (AEMWEs). These were selected from a broad range of electrochemical devices due to their distinctive roles in a hydrogen-based energy ecosystem. Ensuring electrochemical stability is critical to achieving reliable cathode for PEMFC and AEMWE systems. To achieve long-term application systems, this work emphasizes two primary research strategies: (1) intermetallic nanostructuring and (2) the development of anticorrosive carbon materials. In this thesis, computational chemistry is systematically applied to address the aforementioned research challenges and develop efficient energy materials for electrochemical applications, utilizing methods such as density functional theory (DFT) and machine learning (ML) potentials. DFT has proven highly effective in predicting the properties and structures of electrochemical catalysts, becoming an essential tool for research and development in this field. However, due to the required computational resources, its application is generally limited to systems containing a few hundred atoms. To overcome these issues, ML-assisted multi-scale simulations are introduced in this thesis. Advances in computing hardware and the rise of artificial intelligence have enabled the development of ML techniques for multi-scale modeling and the generation of accurate force fields across a wide variety of systems. In Chapter 2, ordered intermetallic nanostructures are employed to enhance the intrinsic activity and durability of PEMFC and AEMWE systems. In PEMFC, machine- learning potentials are developed and applied for real-size simulations of intermetallic PtCo nanostructures to investigate their theoretical durability by comparing dissolution potentials. Through systematic experimental approaches, the predicted theoretical results were clearly validated, demonstrating the enhanced electrocatalytic performance of the intermetallic nanostructures. In AEMWE, the theory-guided design of intermetallic PtNi nanostructures provides critical insights into both fundamental dissolution potential and predictions of catalytic activity. Successful experimental validation, from half-cell tests to large-scale AEMWE stack systems, showed a degradation rate of less than 2 % over 3,000 h of operation. In Chapter 3, fluorine-doped graphene nanoribbons (F-GNR)-based anticorrosive carbon materials are proposed as an effective strategy for developing highly durable PEMFCs. Fluorine is particularly beneficial for corrosion resistance due to the strong carbon-fluorine (C-F) covalent bond. According to the DFT results in this thesis, F-GNR and F-GNR@CNT composites show improved resistance to carbon corrosion, exhibiting lower binding energies with corrosion sources such as H2O and oxygen atoms compared to pristine GNR. Experimentally, F-GNR-based carbon materials demonstrate their potential as cathode additives, enhancing structural stability and water management. In Chapter 4, fundamental guidance and validations based on DFT calculations were provided to effectively develop energy materials for various electrochemical conversion and storage systems. These systems include oxygen/hydrogen electrocatalysts, CO2 reduction reactions (CO2RR), Li/Zn batteries, and supercapacitors. The thesis provides in-depth theoretical insights into the design of computational models, detailing the methods used to optimize these materials. It also offers comprehensive computational analysis on key aspects such as catalytic activity, long-term durability, and electronic structure. This theoretical framework is crucial for understanding the mechanisms and for guiding the development of high-performance energy materials in these electrochemical systems.