Ammonia (NH3) is best known as a starting material for fertilizers, but its reaction with oxygen (called oxidation) is required in applications such as ammonia sensing, wastewater treatment, and direct ammonia fuel cells - all of which are carried out electrochemically, and usually assisted by a catalyst material called an electrocatalyst. Even with state-of-the-art platinum-based electrocatalysts, the oxidation reaction is inefficient and requires excess electrical energy. The project will investigate, through theoretical and computational means, the possibility of improving both the energy efficiency and the rate of electrochemical ammonia oxidation by combining platinum with other metals in nano-scale particles known as nano-alloys. The predicted nano-alloy compositions will help guide the design of more efficient electrocatalysts, not only for ammonia-related applications, but also for a broad range of energy and environmental technologies. The project also integrates research with educational and outreach initiatives designed to excite high-school students about STEM opportunities and train undergraduate and graduate students in the application of computer models for energy security and environmental stewardship.
Electrocatalytic reactions at the core of artificial photosynthesis involve multiple proton-coupled electron transfer steps. Arguably, for a given type of catalysts, e.g., d-block metals, the scaling relations among adsorption energies of atoms and their hydrogenated species limit the efficiency of electrical/chemical energy conversion. To overcome those obstacles for the ammonia oxidation reaction, the project will utilize a Bayesian framework for advancing the orbital-level understanding of adsorbate-surface interactions and catalytic processes at the metal-electrolyte interfaces, paving the path toward adsorbate-specific tuning of electrocatalysis. The free formation energies of key reaction species will be selectively tuned via orbital-wise perturbation of chemical bonding, e.g., nano-alloying, such that the activation barrier of the rate-limiting N-N bond formation or N-H cleavage step is reduced without poisoning the surface with adsorbed N adatoms. Catalysis theory, quantum chemistry, and machine learning will be combined to unravel atomistic mechanisms of sluggish NH3 electro-oxidation kinetics and develop the Bayesian model of chemisorption with machine-learned Hamiltonians. Modulation of adsorbed species by engineering their interactions with atomically-tailored metal sites guided by the Bayesian models will further advance the theory of chemisorption and its applications in catalysis, enabling design of catalytic systems with physically interpretable insights rather than trial-and-error searches. The educational component of this CAREER plan aims to further develop the informatics for photon harvesting at nano-engineered structures, via a mobile device application, iPhanes, developed by the investigator. This effort will energize student learning using materials informatics on mobile devices, demonstrate a multidisciplinary perspective of energy issues, and stimulate the students' collaborative learning via materials design projects. This "experiment" will enhance recruitment and retention of women, minorities, and persons with disabilities in STEM fields, and will motivate the students towards lifelong learning and careers related to advanced renewable energy and environmental 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.
