Heterogeneous catalysts accelerate chemical reactions in more than 70% of all chemical manufacturing processes. In these materials, the active sites (single atoms or nanoparticles) lie within the porous network of a solid support, which may be crystalline, e.g. a zeolite, or amorphous, e.g. an activated carbon. For these materials to function properly, gas or liquid reactants must transfer through the pore openings, diffuse along the porous networks, and adsorb on the active sites where reactions occur. The products of the reaction must diffuse in the other direction from the active sites, through the pores, and to the outside of the material. Optimal performance requires a balance between reactants and products transport, which depends on the molecular properties of the fluids and the structural properties of the catalyst. This project will develop a fully integrated–multiscale kinetic modelling framework for industrial applications that accounts for the relevant reactive and transport phenomena. This project brings together investigators from the University of Oklahoma, the University of Oxford, and the University of Manchester, who will achieve the following aims: 1) Bridge the knowledge gap between local pore-scale phenomena (electronic/molecular) and global/bulk phenomena (performance) in porous catalysts. The kinetic Monte Carlo (kMC) method will be the backbone of the envisioned integrated framework, which will link reactive kMC and diffusive kMC approaches. The outcome of this aim will be a stochastic, multiscale modelling framework for high-fidelity simulations that will rationalize trends and inform materials design. 2) Obtain fundamental insight into the relation between porous catalyst structure and performance for important future industrial processes. The use-case will be represented by biomass-related transformations, and specifically the conversion of fructose to hydroxymethylfurfural (HMF), in which selectivity challenges are prominent, with humins obtained as the undesired by-products. The kMC framework of aim 1 will be parameterized with density functional theory (DFT) (reactive component) and molecular dynamics (MD) (transport component), towards predictive simulations. The outcome of this aim will be guidance for the development of superior catalysts. 3) Experimentally validate the framework and the predictions of the models of aims 1 and 2 via a variety of approaches encompassing catalyst synthesis, physicochemical properties modification, characterization, diffusion measurements and kinetic studies. The outcome of this aim will be the step-by-step improvement of the computational/theoretical methods developed throughout this project. The successful completion of the project will deliver much anticipated know-how for the design of advanced materials and chemical processes with unprecedented accuracy and spatial resolution. In the long run, the fundamental insights obtained via MD and DFT calculations, once validated against nuclear magnetic resonance (NMR) experiments, will help identify the rate-limiting steps affecting the performance of several chemical processes of industrial and societal relevance. This collaborative U.S.- U.K. project is supported by the U.S. National Science Foundation (NSF) and the Engineering and Physical Sciences Research Council (EPSRC) of United Kingdom Research and Innovation (UKRI), where NSF funds the U.S. investigator and EPSRC funds the partners in the U.K. 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.

Up to $398K

2028-08-31

engineering

CBET-EPSRC: Computationally guided design of novel metal-organic frameworks for enhanced proton conductivity and photocatalytic water splitting

open

NSF

Hydrogen is a highly promising clean energy fuel. This project aims to accelerate the discovery of advanced materials for advanced hydrogen-based energy technologies. The research focuses on Metal–Organic Frameworks (MOFs), a class of porous materials with highly tunable structures and chemistry. These materials show exceptional promise in applications such as water splitting and proton conductivity, which are critical for efficient producing, storing, and using hydrogen. The project brings together a multidisciplinary, international team from the University of Manchester (UK) and Rutgers University (USA), which combines theoretical modeling, molecular simulations, machine learning, and experimental techniques to better understand how the structure of these materials influences their function at the molecular level. These insights will guide the design and synthesis of new MOFs with enhanced performance, ultimately contributing to advanced energy solutions. The success of the proposed interdisciplinary research program will have significant intellectual merit and broad societal and environmental impact. The project is expected to have long-term benefits on both fundamental science and potential commercial innovation. This project seeks to establish practical design rules for synthesizing Metal–Organic Frameworks (MOFs) with enhanced performance in hydrogen-related processes, specifically proton conductivity and photocatalytic water splitting. Building on recent discoveries at the University of Manchester of MFM-300(Cr)·SO4(H3O)2 and MFM-808-SO4 structures that exceed the proton conductivity of benchmark materials and demonstrate efficient hydrogen evolution under visible light, the research will combine ab initio modeling, molecular dynamics and Monte Carlo molecular simulations with experimental synthesis and characterization. The specific objectives of this research proposal are i) to elucidate molecular phenomena associated with water adsorption, proton transport, and water splitting in these materials on a fundamental molecular level, as well as the effects of structural flexibility and sulfonation on conductivity and catalytic activity ii) to use the identified patterns and factors responsible for the enhanced proton conductivity and water splitting activity to design and discover new MOF architectures with advanced properties iii) to synthesize and characterize new promising MOFs, expanding the range of materials and operation conditions for proton conductivity and hydrogen evolution. Fundamental questions, such as how framework topology, hydration states, and functional group distribution influence transport behavior, will be addressed through a series of research tasks focused on modeling water cluster networks, tuning chemical functionality, and simulating deformation-driven transport effects. These insights will be translated into computational screening and machine learning-guided discovery of new MOF candidates with superior properties, followed by targeted synthesis and performance testing. The outcomes of this research will push the boundaries of materials design for hydrogen technologies and provide a deeper understanding of the structure–function relationships that govern MOF behavior under a broad range of engineering conditions. This collaborative U.S.- U.K. project is supported by the U.S. National Science Foundation (NSF) and the Engineering and Physical Sciences Research Council (EPSRC) of United Kingdom Research and Innovation (UKRI), where NSF funds the U.S. investigator and EPSRC funds the partners in the U.K. 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.

Up to $400K

2028-07-31

machine learningengineeringchemistry

CBET-EPSRC: Wall Similarity of 2D and 3D Non-Equilibrium Rough Wall Boundary Layers

open

NSF

Predicting the flow of air or water over a vehicle or other device is key to making engineering improvements. When a rough surface is exposed to the flow, it is assumed that the only effect of the roughness is to increase the friction without otherwise changing details about the flow. However, this assumption is unproven in all but the simplest situations. The purpose of this project is to definitively resolve this issue, using the novel approach of comparing flows over different surfaces that generate the same friction. This work will enable more reliable analysis and design of many systems and vehicles. The project will engage and train researchers at postdoctoral, doctorate, undergraduate, and high-school levels, enable interdisciplinary international exchanges, and provide unique research experiences for hundreds of undergraduate engineers. The approach will combine state-of-the-art experimental capabilities and experience at Virginia Tech with leading edge computational expertise at the University of Cambridge. The goal is to reveal the limits and mechanisms of rough wall similarity, by comparing the properties and mechanics of non-equilibrium boundary layers generated over rough walls with different geometry but with the same effective sand-grain roughness. This comparison will be conducted for a systematic set of two- and three-dimensional flow geometries that include many of the non-equilibrium complexities found in practical applications. At Virginia Tech, experiments performed in the Stability Tunnel will include detailed mean flow and turbulence measurements at friction Reynolds numbers up to 20,000. Coordinated and complementary scale-resolving simulations at Cambridge will provide intricately detailed views of the turbulence dynamics that mediate the critical relationship between the roughness layer and outer region. The project will form the focus of post-doctoral training at the University of Cambridge and doctoral-student training at Virginia Tech. In addition, the project includes a series of sub-projects to be conducted by groups of undergraduate and high school students drawn from the Southwest Virginia Governor’s School. Furthermore, wind tunnel tests will be integrated with required undergraduate laboratory courses in Aerospace and Ocean Engineering and Mechanical Engineering, providing substantive research experiences to large numbers of undergraduate students annually. This project is jointly funded by the U.S. National Science Foundation Fluid Dynamics program and the UK Research and Innovation (UKRI)/ Engineering and Physical Sciences Research Council (EPSRC) under the NSF Directorate for Engineering – UKRI Engineering and Physical Science Research Council Lead Agency Opportunity. 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.

Up to $816K

2029-06-30

engineering