Multicomponent materials, such as alloys, serve in critical aspects of the nation's infrastructure, manufactured goods, hardware technologies, and defense apparatus. Common examples are brass (mixture of copper and zinc) and stainless steels (mixtures of iron, carbon and other elements such as chromium and nickel). The main challenge of designing an alloy for a particular application is understanding how its useful characteristics (cost, hardness, density, corrosion resistance, chemical properties, etc.) depend on its specific mix of elements. As the number of elements increases, testing the range of possible combinations requires the preparation and study of an exponentially increasing number of alloy samples. The project will develop and apply modern research tools to vastly increase the rate at which the properties of alloys can be measured experimentally and/or predicted by computational simulation. The application of interest is the catalytic production of propylene oxide, a multibillion-dollar commodity chemical. The investigators will use high-throughput experimental methods to collect catalytic reaction data from 100 different binary or ternary alloy catalyst compositions concurrently, rather than one catalyst composition at a time. These experimental methods will be integrated with machine-learning methods that predict catalytic activity via rapid computational simulations. Once the performance of these computational methods has been benchmarked against experiment, they can be deployed for design of alloy catalysts with more complex compositions, structures and morphologies. More importantly, once their utility in the design of catalytic materials is established, such tools can be applied to a wide variety of applications, accelerating the design of multicomponent alloys for a wide variety of engineering, product design, and hardware technologies.
This DMREF project will use the propylene epoxidation to propylene oxide on CuxAgyAu(1-x-y) catalyst surfaces in order to improve alloy selection and optimization in the context of catalytic surface design. The system investigated here will enable efficient tailoring of variables such as surface orientation and structure; the relationship between elemental compositions in the bulk and on the surface due to segregation; and the influence of operational environments on surface structure and composition. High-throughput methods for alloy catalysis study will make use of composition spread alloy films (CSAFs) as material libraries, containing gradients of Cu, Au, and Ag parallel to their surfaces, such that many compositions are found over a single film. Coupled with a high throughput multichannel microreactor array, the catalytic activity will be measured at 100 alloy compositions concurrently, across a range of temperatures and reactant feed compositions. In addition, the surface composition of the alloy will be measured to determine the effects of segregation on reactivity. These data will serve to verify high-throughput computational simulations of catalytic activity. Machine-learning methods will be developed to expedite atomistic simulations based on interatomic potentials derived from Density Functional Theory, which is expected to accelerate the process of filtering the many possible atomistic configurations of catalytically active sites at an alloy surface. These modeling methods inform further experimental measurements of catalytic activity and in-situ characterization of surfaces spanning alloy composition space; CuxAgyAu(1-x-y) with 0 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.
