The scientific field of computational chemistry uses computer simulations to calculate the structures, properties, as well as, reactions of molecules and materials. These simulations can be envisioned as virtual experiments that generate rich information about the materials behavior with a great level of accuracy. Elucidating the mathematical functions that describe how the materials properties depend on their structural characteristics allows us to design optimal materials for targeted applications (structures that maximize a desired property). This methodology significantly reduces the need to perform numerous, time-consuming, and costly experiments in the lab, which are often based on extensive trial and error. This Design of Engineering Material Systems (DEMS) award supports fundamental research to design nanoparticles that consist of two different metals and are able to capture carbon dioxide, a molecule contributing to the greenhouse effect. The predictions from the computational research will be validated with targeted experiments in the lab. Since metal nanoparticles find a wide range of applications, it is expected that results from this research will affect the U.S. economy, society and the environment. A website will be developed to allow free access to a library of simulated structures. The multidisciplinary nature of this research, involving computational chemistry, materials design, optimization, scientific computation, materials synthesis and catalysis, will help broaden engineering education and attract underrepresented students to research. In addition, animation modules will be generated for incorporation in high school classes.
This project creatively integrates first-principles calculations with rigorous engineering design methods, in order to develop a systematic framework to optimize nanoparticles in light of a performance metric (demonstrated via carbon dioxide adsorption), while also taking into account nanoparticle stability aspects. A novel design of experiments approach, tailored to the intricacies of this specific materials class, will be developed, while the computational predictions will be validated experimentally through targeted nanoparticle synthesis, characterization, and carbon dioxide adsorption experiments. Developing the capability to computationally identify nanoparticles that maximize their performance for a given application in a multi-dimensional composition-morphology space is crucial to guide future research efforts and accelerate nanomaterials discovery. However, efforts to-date have been focused entirely on one-dimensional optimizations (almost exclusively focused on metal composition). The present project will demonstrate the first strategy that truly explores the vast parameter space and hence enables true design of functional nanoparticles. In addition, this research advances the state-of-the-art in the study of bimetallic nanoparticles by developing structure-property relationships that will be applicable to any nanoparticle morphology. Finally, this project advances environmental science by designing bimetallic nanostructures for capturing and activating carbon dioxide, a key greenhouse gas.
