Tim Mueller and Chao Wang of Johns Hopkins University are supported by an award funded by the Division of Chemistry in the Designing Materials to Revolutionize and Engineer our Future (DMREF) program to develop a way to rationally design new alloy nanoparticle catalysts. Alloy nanoparticles are promising materials for use as advanced catalysts that increase the energy efficiency of chemical processes at relatively low cost. It is possible to modify the performance of alloy nanoparticle catalysts by adjusting their size, shape, atomic structure, and chemical composition. The research approach used by Mueller and Wang takes advantage of this flexibility. They create computational models that are able to accurately predict how the conditions under which nanoparticles are made affect their catalytic performance. These models are refined and validated by iterative comparisons with experimental results. As a proof of concept, this approach is being used to design catalysts that facilitate the conversion of carbon dioxide into hydrocarbon fuels. The conversion of carbon dioxide into fuels could simultaneously increase global fuel supply and reduce the amount of carbon dioxide in the atmosphere; but it is not yet economically viable due to the lack of suitable catalysts. This research is integrated with a comprehensive educational outreach program that includes research opportunities for a female high school student, a workshop on energy technologies, and a new course on renewable energy technologies at Johns Hopkins University.
This project has two primary thrusts. In the first, the researchers are developing a predictive model that relates synthesis conditions to nanoparticle structure. To accomplish this, computational models based on cluster expansions are trained on ab-initio data and used to predict the atomic structures of alloy nanoparticles. The predicted structures are then compared to the experimentally-determined structural characteristics of monodisperse and homogeneous alloy nanoparticles created from organic solution synthesis. The computational and experimental approaches are iteratively refined until they are consistent, resulting in a model with strong predictive power. In the second thrust, the researchers are developing and validating a predictive model that relates nanoparticle structure to catalytic properties. Density functional theory is used to calculate the binding energies of key intermediate adsorbates on the surfaces of nanoparticles, and these energies are used in the computational hydrogen electrode model to calculate catalytic properties of the alloy nanoparticles. To iteratively refine the computational models, computational predictions for nanoparticles of different sizes, compositions, and surface structures are compared to experimental catalytic and spectroscopic studies. To demonstrate and validate the effectiveness of this approach, the researchers are designing and testing Cu-alloy nanoparticle catalysts for electrochemical CO2 reduction.
