Size, Shape, and Composition-Dependent Model for Metal Nanoparticle Stability Prediction
Although tremendous applications for metal nanoparticles have been found in modern technologies, the understanding of their stability as related to morphology (size and shape) and chemical ordering (e.g., in bimetallics) remains limited. First-principles methods such as density functional theory (DFT) are capable of capturing accurate nanoalloys energetics; however, they are limited to very small nanoparticle sizes (<2 nm in diameter) due to their computational cost.
Giannis Mpourmpakis and his students have proposed a bond-centric (BC) model able to capture cohesive energy trends over a range of monometallic and bimetallic nanoparticles and mixing behavior (excess energy) of nanoalloys, in great agreement with DFT calculations. This model utilizes to calculate the energetics of any nanoparticle morphology and chemical composition, thus significantly accelerating nanoalloys design.
The BC model is orders of magnitude faster than DFT in evaluating arbitrary alloy MNPs of practically any morphology (size and shape) and metal composition. Importantly, the BC model can identify energetically preferred chemical ordering on alloy MNPs. Additionally, because the BC model does not require training to calculated or experimental parameters, it is uniquely suited to address the energetics in massive nanoalloy structures. While other thermodynamic factors such as entropy and synthesis temperature can influence nanoalloy composition and chemical ordering, they have primarily focused on the enthalpic contributions (largely captured by DFT electronic energies). In future work, they have planned to include configurational entropy and temperature effects in the BC framework.