Core-shell nanoparticles composed of a nonnoble metal core and a noble metal shell are of great significance in many areas including chemical catalysis, optical detection, and magnetic separation. Through a modification of the commonly used polyol process, Ni@Pt core-shell nanoparticles that are less than 10 nm in total size and have a very thin Pt shell can be fabricated by a sequential reduction approach. The prepared core-shell nanoparticles were characterized with TEM, XRD, molecular dynamics (MD) simulations, and electrochemical method. It was found that these core-shell particles exhibit the structural characteristics of fcc Ni nanocrystals with a slightly expanded lattice constant but the electrochemical properties of a Pt surface with a significantly shortened Pt-Pt interatomic distance than for pure Pt nanoparticles. The structural characteristics of the prepared core-shell particles revealed by the TEM, XRD, and electrochemical analyses were well verified by MD simulations of a Ni@Pt core-shell particle with a monolayer Pt shell. It is believed that the prepared Ni@Pt core-shell nanoparticles could be promising cathode catalysts in PEM fuel cells with much reduced Pt content but significantly increased catalytic activity.

Adsorption-energy scaling relations are widely used for the design of catalytic materials. To date, only linear scaling relations are known in which the slopes are positive. Considering the adsorption energies of F, O, N, C, and B on transition metals, we show here that scaling relations with negative slopes also exist between certain adsorbates. The origin of such unconventional scaling relations is analyzed in terms of common descriptors such as d-band center, work function, number of outer electrons, electronic charge on the adsorbates, integrated crystal orbital overlap populations, and crystal orbital Hamilton populations. Conventional scaling relations are formed between adsorbates such as F, O, N, and C, which create ionic-like bonds with surfaces. Conversely, anomalous scaling relations are established between those and covalently bound adsorbates such as B. This widens the theory of adsorption-energy scaling relations and opens new avenues in physical chemistry and catalysis, for instance, in direct borohydride fuel cells.

Gold nanoclusters have been the focus of numerous computational studies, but an atomistic understanding of their structural and dynamical properties at finite temperature is far from satisfactory. To address this deficiency, we investigate gold nanoclusters via ab initio molecular dynamics, in a range of sizes where a core–shell morphology is observed. We analyze their structure and dynamics using state-of-the-art techniques, including unsupervised machine-learning nonlinear dimensionality reduction (sketch-map) for describing the similarities and differences among the range of sampled configurations. In the examined temperature range between 300 and 600 K, we find that whereas the gold nanoclusters exhibit continuous structural rearrangement, they are not amorphous. Instead, they clearly show persistent motifs: a cationic core of 1–5 atoms is loosely bound to a shell which typically displays a substructure resulting from the competition between locally spherical versus planar fragments. Besides illuminating the properties of core–shell gold nanoclusters, the present study proposes a set of useful tools for understanding their nature in operando.

An approach based on ab initio statistical mechanics is demonstrated for autoconstructing complex reaction networks. Ab initio molecular dynamics combined with Markov state models are employed to study relevant transitions and corresponding thermodynamic and kinetic properties of a reaction. To explore the capability and flexibility of this approach, we present a study of oxygen activation on Ag 4 as a model reaction. Specifically, with the same sampled trajectories, it is possible to study the structural effects and the reaction rate of the cited reaction. The results show that this approach is suitable for automatized construction of reaction networks, especially for non-well-studied reactions, which can benefit from this ab initio molecular dynamics based approach to construct comprehensive reaction networks with Markov state models without prior knowledge about the potential energy landscape.

In this work, we explore the role of chemical reactions on the properties of buffer gas cooled molecular beams. In particular, we focus on scenarios relevant to the formation of AlF and CaF via chemical reactions between the Ca and Al atoms ablated from a solid target in an atmosphere of a fluorine-containing gas, in this case, SF6 and NF3. Reactions are studied following an ab initio molecular dynamics approach, and the results are rationalized following a tree-shaped reaction model based on Bayesian inference. We find that NF3 reacts more efficiently with hot metal atoms to form monofluoride molecules than SF6. In addition, when using NF3, the reaction products have lower kinetic energy, requiring fewer collisions to thermalize with the cryogenic helium. Furthermore, we find that the reaction probability for AlF formation is much higher than for CaF across a broad range of temperatures.