General Trends in Core–Shell Preferences for Bimetallic Nanoparticles

Abstract

Surface segregation phenomena dictate core–shell preference of bimetallic nanoparticles and thus play a crucial role in the nanoparticle synthesis and applications. Although it is generally agreed that surface segregation depends on the constituent materials’ physical properties, a comprehensive picture of the phenomena on the nanoscale is not yet complete. Here we use a combination of molecular dynamics (MD) and Monte Carlo (MC) simulations on 45 bimetallic combinations to determine the general trend on the core–shell preference and the effects of size and composition. From the extensive studies over sizes and compositions, we find that the surface segregation and degree of the core–shell tendency of the bimetallic combinations depend on the sufficiency or scarcity of the surface-preferring material. Principal component analysis (PCA) and linear discriminant analysis (LDA) on the molecular dynamics simulations results reveal that cohesive energy and Wigner–Seitz radius are the two primary factors that have an “additive” effect on the segregation level and core–shell preference in the bimetallic nanoparticles studied. When the element with the higher cohesive energy also has the larger Wigner–Seitz radius, its core preference decreases, and thus this combination forms less segregated structures than what one would expect from the cohesive energy difference alone. Highly segregated structures (highly segregated core–shell or Janus-like) are expected to form when both the relative cohesive energy difference is greater than ∼20%, and the relative Wigner–Seitz radius difference is greater than ∼4%. Practical guides for predicting core–shell preference and degree of segregation level are presented.

Understanding the Trend in Core–Shell Preferences for Bimetallic Nanoclusters: A Machine Learning Approach

“…The results of an MC-MD study 11 established the fact that the finite temperature and composition effects do not drastically perturb the general trend found based on T = 0 K, single-atom alloy results. At the same time, as revealed in the same study, 11 small-size nanoclusters appear to offer the cleanest systems in terms of purity of core and shell ordering. Motivated by this, in the present study we carried out DFT calculations of SE for a single-atom alloy of 55 atoms in size by considering a variety of possible binary combinations of metallic elements, including alkali, alkaline, basic, transition metal (TM), and p-block metals (see Figure 1B), to create a large data set of 903 binary combinations.…”

“…9,10 Recently, the issue has been studied by molecular dynamics and Monte Carlo simulations for 45 bimetallic nanoclusters, with subsequent principal component analysis. 11 However, for understanding a general trend, one should use a much larger database compared to what has been used so far and use the machine learning technique, the most suited technique for recognizing patterns. Motivated by this, in this study we consider a large data set of 903 bimetallic nanoclusters with constituents spanning a wide range from alkali, alkaline, 3d, 4d, and 5d TM series, to p-block elements.…”

“…9 In recent times, the segregation behavior of singleatom binary alloys of 12 late transition metals has been studied using DFT, 10 which highlighted the role of magnetism, correlation, and strain. A classical simulation study 11 involving Monte Carlo (MC) molecular dynamics (MD) of 47 binary metallic clusters modeled through embedded atom potentials has been used to understand the general trend in core−shell preference. Principal component analysis of the MC-MD data identified the cohesive energy difference and Wigner−Seitz difference as two primary factors.…”

Virtual Issue on Nanosynthetic Chemistry

“…Nanoparticles containing unique internal interfaces that direct the composite properties can be made using methods that include sequential seeded growth and partial cation exchange . Computational studies of synthetically relevant systems help to predict how elements will be distributed in these multielement systems, for example, predicting which binary systems will prefer to form segregated and/or interfaced structures, such as core@shell nanoparticles. , …”

“…14 Computational studies of synthetically relevant systems help to predict how elements will be distributed in these multielement systems, for example, predicting which binary systems will prefer to form segregated and/or interfaced structures, such as core@shell nanoparticles. 15,16 It is also possible to form interfaces between twodimensional (2D) materials, such as graphene and transition metal dichalcogenides (TMDs), to make "artificial" superlattices having designer properties that are not achievable in single-component, noninterfaced systems. The synthesis of 2D materials has advanced considerably over the past few years, to the point where it is now becoming possible to synthesize highquality 2D films over large areas and with precise control over thickness and other property-defining features.…”