Charge Redistribution in Core-shell Nanoparticles to Promote Oxygen Reduction
Bimetallic core-shell nanoparticles are a class of near surface alloy catalyst for which there is a high degree of control over size and composition. A challenge for theory is to understand the relationship between their structure and catalytic function, and provide guidelines to design new catalysts that take advantage of materials properties arising at the nanoscale. In this work, we use density functional theory to calculate the energetics of oxygen dissociative adsorption on 1 nm Pd-shell nanoparticles with a series of core metals. The barrier for this reaction and the binding energy of atomic oxygen is found to correlate well with the d-band level of the surface electrons. Noble metal cores lower the barrier and increase the binding, reducing the activity of the Pd-shell as compared to Pt. Reactive core metals such as Co and Mo, on the other hand, lower the d-band of the shell with respect to the Fermi level, giving the Pd-shelled particles oxygen reduction kinetics similar to that of Pt. While both ligand and strain effects determine the d-band center of the Pd shell, a greater surface relaxation reduces the strain in nanoparticles as compared to single crystal near-surface alloys. Charge redistribution between core and shell then becomes an important factor for lowering the d-band center of Pd-shelled particles and increasing their activity for the oxygen reduction reaction.
Optimizing nanoparticle catalysts with a genetic algorithm
Platinum-based fuel cells offer an attractive alternative to internal combustion engines as a future means of utilizing chemical energy. There are, however, shortcomings of such technologies that must be resolved if they are to become practical and widespread. Some of these difficulties include the CO poisoning, the short lifetime of electrodes in acidic environments, the ~30% energy loss due to slow oxygen reduction kinetics at the cathode, and the high material cost and limited supply of platinum itself. Cheaper, more effective electrocatalysts need to be developed, yet the task of discovering novel platinum alternatives has proven to be extremely challenging.
We have implemented a genetic algorithm (GA) within density functional theory to investigate the catalytic properties of a large number of 38- and 79-atom bimetallic core-shell nanoparticles for the oxygen reduction reaction (ORR) in an effort to identify promising platinum replacement catalysts for use in fuel cells. In the GA, each nanoparticle is represented by a two-gene chromosome that identifies its core and shell metals. The fitness of each particle is specified by how closely the d-band level of the shell electrons matches that of the Pt(111) surface, a catalyst known to be effective for the ORR. The GA starts by creating an initial population of random core-shell particles; the fittest particles are then bred and mutated to replace the least-fit particles in the population and form successive generations. Within a few generations, the average energy of the surface d-band electrons in the nanoparticles is tuned to that of Pt(111). These nanoparticles may possess similar catalytic properties as Pt(111) for the ORR. Promising core-shell metal combinations revealed by the GA are shown above.
References
Wenjie Tang and Graeme Henkelman, Charge redistribution in core/shell nanoparticles to promote oxygen reduction, J. Chem. Phys. 130 194504 (2009).N. S. Froemming and G. Henkelman, Optimizing core-shell nanoparticle catalysts with a genetic algorithm J. Chem. Phys. 131, 234103 (2009).