CAREER: Atomistic-Scale Investigation of How Chemical and Physical Heterogeneities Govern Ion-Selective Transport in Metal-Organic Framework Membranes

About This Grant

Modern technologies like lithium-ion batteries, electric vehicles, and advanced manufacturing depend on critical minerals. These minerals are often found in seawater, brines, and wastewater. The minerals of interest mix with other minerals of similar size and chemical behavior. Separating nearly identical ions is very difficult. Membrane-based separation methods are energy-efficient, but designing membranes that can tell similar ions apart is a challenge. This project will improve membrane design by analyzing how the membrane’s chemistry and internal structure control the motion of ions through membranes. The project will use quantum calculations and other simulations to produce models that predict the motion of ions. By identifying chemical features that help certain ions pass more easily than others, the project will support better membrane design. The results will improve U.S. critical mineral recovery, water treatment, and energy technologies, while also training students through coursework and K–12 outreach activities. This project focuses on water-stable metal–organic framework (MOF) membranes as a model platform to investigate how membrane chemistry, structural heterogeneities, and ion–ion interactions control selective transport among chemically and physically similar ions. The study targets technologically important separations relevant to critical mineral recovery, including lithium and sodium ions, as well as selected heavy metal ions. An integrated multiscale modeling framework is employed, combining ab initio quantum calculations, molecular dynamics simulations, and transport models grounded in statistical thermodynamics. Atomistic simulations are used to characterize interactions among ions, membrane atoms, and the local chemical environment that govern ion selectivity. Insights from these simulations inform physically based transport models that link molecular-scale mechanisms to membrane-level selectivity and permeability. In addition, machine-learning models are incorporated in a mechanism-aware manner as surrogate representations of transport behavior learned from physics-based simulations, enabling efficient exploration of chemically and structurally defined parameter spaces. Model accuracy and predictive trends are validated through close collaboration with experimental partners. The expected outcomes include mechanistic insight into selective ion transport in chemically heterogeneous nanoporous membranes, predictive modeling frameworks that bridge atomistic and membrane scales, and broadly applicable computational tools for separation, energy, and water technologies. 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.