Of late, technology involving metal nanoparticles is blossoming with new potential applications emerging every day. This can be attributed to the fact that the properties of metal nanoparticles differ from both the atomic and bulk size extremes; including, optical, magnetic, electrical and adsorption. Recent technological advances have already helped establish that besides composition, chemical ordering at the atomic level is critical when it comes to determining important nanoalloy properties in magnetic and catalytic applications. First principle methods such as the density functional theory have the ability to capture accurate nanoalloy energetics, unfortunately, their applicability is limited to very small nanoparticle sizes as a result of the exorbitant computational cost involved. Therefore, despite the fact that remarkable applications for metal nanoparticles have already been established, a good comprehension of their stability in relation to morphology and chemical ordering has remained hazy.
Recently, University of Pittsburgh researchers (Zihao Yan, Michael Taylor and Ashley Mascareno) led by professor Giannis Mpourmpakis introduced a novel bond centric (BC) model that could accurately and rapidly determine the energetics of alloy metal nanoparticles with arbitrary morphology, composition, and chemical ordering. They anticipated that their model would capture cohesive energy trends over a range of monometallic and bimetallic nanoparticles and mixing behavior of nanoalloys, in great agreement with density functional theory calculations. Their work is currently published in the research journal, Nano Letters.
The research method employed commenced with the evaluation of the performance of the facile square-root bond cutting (SRB) model on calculating cohesive energies of monometallic and bimetallic metal nanoparticles using density functional theory calculations. Next, based on the performance of the SRB model, they introduced scaling factors that corrected the bimetallics energetics by utilizing highly accurate bimetallic bond strength data from literature. Lastly, the researchers demonstrated the successful application of their model where they effectively screened the thermodynamic stability of alloy metal nanoparticles and compared the results obtained with density functional theory calculations and experiments. “We were very excited to successfully apply our model to an FePt alloy nanoparticle that consisted of 23,196 atoms and show that the experimentally synthesized nanostructure was captured as one of the lowest in energy configurations”, said professor Mpourmpakis.
The authors observed that their novel bond centric model was hypothetically suited to capture 298, which represented a modest 85%, of all the bimetallic transition metal alloys. In addition, they noted that beyond the broad applicability of the bond centric model on nanoalloys, its strong physical basis allowed for important comparisons and extraction of physical learnings.
In a nutshell, Giannis Mpourmpakis and his research team introduced a bond centric model capable of accurately capturing the energetics of metal nanoparticles as well as their mixing behavior. The main observation made was that the novel bond centric model was extremely fast in evaluating arbitrary alloy metal nanoparticles of practically any morphology (size and shape) and metal composition and very accurate capturing similar trends with Density Functional Theory calculations. Altogether, this work has established a facile yet very powerful tool for nanoalloy design that can potentially help elucidate the energetics of alloy metal nano particle genomes.
Zihao Yan, Michael G. Taylor, Ashley Mascareno, Giannis Mpourmpakis. Size-, Shape-, and Composition-Dependent Model for Metal Nanoparticle Stability Prediction. Nano Letters. 2018, volume 18, page 2696−2704.Go To Nano Letters