Nanoengineering approaches at the sub-nano or atomic scaleĪre especially interesting, as they allow us to unravel how activity varies as a function of these parameters (shape, size, composition, structure, electronic, and support interaction) and obtain insights into structure–performance relationships in the field of H2 production, allowing not only the optimization of performances but also enabling the rational design of nanocatalysts with desired activities and selectivity for H2 production. The main goal of this review is to discuss the recent contributions in the H2 production field by employing nanomaterials with well-defined and controllable physicochemical features. Although studies in this area have often focused on the fundamental understanding of catalytic processes and the demonstration of their activities towards different strategies, much effort is still needed to develop high-performance technologies and advanced materials to accomplish widespread utilization. ![]() Hydrogen (H2 ) has emerged as a sustainable energy carrier capable of replacing/complementing the global carbon-based energy matrix. Towards the end of the review, the challenges in achieving higher photocatalytic performance and the future potential of single atom pho-tocatalysts in various applications are discussed. Further, there is a side-by-side short discussion on metal-support interaction and various catalyst synthesizing strategies. In the next segment, advancements in the characterization techniques in union with theoretical calculation are briefly discussed. The paper starts with the introduction and fundamentals and discusses the unique characteristic of single-atom photocatalysts in terms of key properties that determine the photocatalytic yields. This review summarizes the role of single atom photocatalyst in energy and environment applications. However, to obtain the mechanistic insights, advanced in-situ/operando methods of characterization and DFT measurements may collectively offer a comprehensive understanding of the nature of active sites and the photocatalyst mechanism at an atomic scale. In this regard, single-atom photocatalyst has shown its unique capabilities in boosting photo-catalytic activity and enhancing the stability. To improve these above-mentioned photocatalytic properties, many strategies are being considered. Nonetheless, owing to low visible-light absorption, the higher recombination rate of electron-hole photogenerated pairs, and slow charge transportations, the performances of photocatalytic systems are significantly compromised. Photocatalysis has gained considerable interest due to changing dynamics and problems pertaining to the environment and energy crisis. ![]() Moreover, separable nested double coordinate system is established to quantitatively evaluate the two effects. Foremost, the geometric effect is firstly spun off through orthogonal relation based on series of linear relationships over various sizes of Pt NPs reflecting the electronic effect. Optimized Fermi levels of the catalysts with large Wf weaken the ability of Pt NPs to fill valence electrons into the antibonding orbital of C–Cl bond, finally suppressing the hydrodehalogenation side reaction. And the selectivity on Pt NPs of similar size and shape is linearly related with the Wf of support. Results suggest Fermi levels of catalysts can be modulated by supports with varied work function (Wf). Here, a novel orthogonal decomposition method is firstly proposed to settle this issue in p-chloronitrobenzene hydrogenation reaction on size- and shape-controlled Pt nanoparticles (NPs) carried on various supports. The present method can be readily extended to other metals and edge orientations as well as arbitrary nanoparticle shapes.ĭecoupling the electronic and geometric effects has been a long cherished goal for heterogeneous catalysis due to their tangled relationship. Calculated vertex energies are about 1 eV/atom. Our results for edge-energy density are 0.22 eV/ for (111)/(111) edges. Furthermore, a clear definition and metric for edge energy is introduced for edge-energy density calculations that avoid the troublesome definition of edge length in nanostructures. The proposed method of total energy calculations using machine learning produces almost ab-initio-like accuracy with minimal computational cost. Assuming that the total energy can be decomposed into contributions from the bulk, surfaces, edges, and vertices, we use machine learning for reliable multi-variant fits of the associated coefficients. We present data-driven simulations for gold nanostructures, and develop a model that links total energy to geometrical features of the particle, with the ultimate goal of deriving reliable edge energies of gold.
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