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The synthesis of supported multielement transition metal phosphides (TMPs) to exploit the synergistic interplay between electronic and geometric effects resulting from the presence of different metals in the material and the arrangement of heterogeneous atoms is pivotal for reducing metal content while offering multiple active sites. However, the integration of Ni, Co, and P, for example, into a nanostructured carbon network to develop self‐supporting NixCoyP bimetallic phosphides is limited by several factors, including the synthesis and the discrepancy between the crystal structure of the respective monometallic phosphides. Moreover, conventional synthesis of supported TMPs often separates nanoparticles, support and phosphidation steps, which do not allow tailoring of physical and catalytic properties via particle support, electronic and geometric interactions. Herein, an innovative solid‐state, ex situ phosphidation‐free approach tailored to synthesize a library of self‐supporting NixCoyP TMPs in N,S,P‐modified nanostructured carbon networks generated together with NixCoyP particles is presented. Extensive multivariate characterization validates the unique properties of NixCoyP bimetallic materials with enhanced electrocatalytic performance for the hydrogen evolution reaction and the selective electroconversion of biomass‐derived 5‐hydroxymethylfurfural (5‐HMF) to value‐added 2,5‐furandicarboxylic acid (FDCA) with 90–100% Faradaic efficiency. Overall, the synthesis expands the possibilities for tailoring the microstructure of supported TMPs for improved physical/catalytic properties.

Embedding energetically stable single metal atoms in the surface of Pt nanocatalysts exposed to varied temperature (T) and hydrogen pressure (P) could open up new possibilities in selective and dynamical engineering of alloyed Pt catalysts, particularly interesting for hydrogenation reactions. In this work, an environmental segregation energy model is developed to predict the stability and the surface composition evolution of 24 Metal M-promoted Pt surfaces (with M: Cu, Ag, Au, Ni, Pd, Co, Rh and Ir) under varied T and P. Counterintuitive to expectations, the results show that the more reactive alloy component (i.e., the one forming the strongest chemical bond with the hydrogen) is not the one that segregates to the surface. Moreover, using DFT-based Multi-Scaled Reconstruction (MSR) method and by extrapolation of M-promoted Pt nanoparticles (NPs), the shape dynamics of M-Pt are investigated under the same ranges of T and P. The results show that under low hydrogen pressure and high temperature ranges, Ag and Au—single atoms (and Cu to a less extent) are energetically stable on the surface of truncated octahedral and/or cuboctahedral shaped NPs. This indicated that coinage single-atoms might be used to tune the catalytic properties of Pt surface under hydrogen media. In contrast, bulk stability within wide range of temperature and pressure is predicted for all other M-single atoms, which might act as bulk promoters. This work provides insightful guides and understandings of M-promoted Pt NPs by predicting both the evolution of the shape and the surface compositions under reaction gas condition.

Machine learning approaches can drastically decrease the computational time for the predictions of spectroscopic properties in materials, while preserving the quality of the computational approaches. We studied the performance of kernel-ridge regression (KRR) and gradient boosting regressor (GBR) models trained on the isotropic shielding values, computed with density-functional theory (DFT), in a series of different known zeolites containing out-of-frame metal cations or fluorine anion and organic structure-directing cations. The smooth overlap of atomic position descriptors were computed from the DFT-optimised Cartesian coordinates of each atoms in the zeolite crystal cells. The use of these descriptors as inputs in both machine learning regression methods led to the prediction of the DFT isotropic shielding values with mean errors within 0.6 ppm. The results showed that the GBR model scales better than the KRR model.

The focus of this short review is directed towards investigations of the dynamics of nanostructured metallic heterogeneous catalysts and the evolution of interfaces during reaction—namely, the metal–gas, metal–liquid, and metal–support interfaces. Indeed, it is of considerable interest to know how a metal catalyst surface responds to gas or liquid adsorption under reaction conditions, and how its structure and catalytic properties evolve as a function of its interaction with the support. This short review aims to offer the reader a birds-eye view of state-of-the-art methods that enable more realistic simulation of dynamical phenomena at nanostructured interfaces by exploiting resource-efficient methods and/or the development of computational hardware and software.