CAREER: Computational Insights into Interfacial Chemistries in Nonaqueous Batteries

About This Grant

The transition to a wider range of energy solutions is important for addressing global challenges, including mitigating carbon dioxide (CO2) emissions, integrating renewable energy, and electrifying transportation systems. Although lithium-ion batteries dominate the market, the scarcity, high cost, and supply chain vulnerabilities of lithium hinder their scalability for large-scale applications. This CAREER project focuses on nonaqueous magnesium (Mg)-CO2 batteries, an alternative next-generation energy storage technology that combines the natural abundance, low cost, and high capacity of Mg anodes with the ability to harness CO2 consumption for generating electricity. The primary bottlenecks in Mg-CO2 batteries involve designing efficient electrocatalysts and gaining an understanding of the interfacial reaction mechanisms. This research will address these obstacles and provide insights to guide the development of sustainable, high-capacity rechargeable batteries. The educational outreach efforts aim to inspire middle and high school students to pursue STEM careers and engage in cutting-edge scientific research in advanced battery technologies. To achieve this, summer workshops at the PI's lab will introduce students to the domain of energy storage and computational chemistry through immersive virtual reality (VR) demonstrations of battery chemistry, molecular simulations, and computer programming. Graduate and undergraduate students will obtain hands-on experience in advanced materials research that will prepare them as a skilled workforce to drive innovation in sustainable energy technologies. This project aims to develop predictive computational tools for designing electrocatalysts, elucidating electrolyte decomposition chemistries, and investigating interfacial reaction mechanisms in nonaqueous Mg-CO2 batteries. Key challenges, such as sluggish reaction kinetics and the formation of blocking interfaces, currently impede the practical realization of these batteries. To address the limitations, the research integrates multi-scale computational techniques—including density functional theory (DFT), machine learning (ML), and eReaxFF reactive molecular dynamics (MD) simulations—to explore electrocatalyst design strategies and electrode-electrolyte interfacial chemistries at the atomistic level. Specifically, this project will (i) predict structure-activity relationships and establish design principles for single-atom catalysts supported on nitride MXenes and transition metal oxides, (ii) investigate electrolyte decomposition pathways and solid electrolyte interphase formation on Mg anodes, and (iii) study reaction kinetics and mechanistic details of cathode-electrolyte interfaces. The computational results will be validated through experimental collaborations. The knowledge gained from this project will provide foundational guidelines to accelerate the development of sustainable, high-capacity metal-CO2 batteries for renewable energy and transportation electrification. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.