High-Throughput Screening Single-Atom Alloy for Electroreduction of Dinitrogen to Ammonia

Abstract

<p></p><p>Exploring electrocatalyst with high activity, selectivity and stability is essential for development of applicable electrocatalytic ammonia synthesis technology. By performing density functional theory calculations, we systematically investigated a series of transition-metal doped Au-based single atom alloys (SAAs) as promising electrocatalysts for nitrogen reduction reaction (NRR). For Au-based electrocatalyst, the first hydrogenation step (*N<sub>2</sub>→*NNH) normally determines the limiting potential of the overall reaction process. Compared with pristine Au(111) surface, introducing single atom can significantly enhance the binding strength of N<sub>2</sub>, leading to decreased energy barrier of the key step, i.e., ΔG(*N<sub>2</sub>→*NNH). According to simulation results, three descriptors were proposed to describe ΔG(*N<sub>2</sub>→*NNH), including ΔG(*NNH), <i>d</i>-band center, and . Eight doped elements (Ti, V, Nb, Ru, Ta, Os, W, and Mo) were initially screened out with limiting potential ranging from -0.75V to -0.30 V. Particularly, Mo- and W-doped systems possess the best activity with limiting potentials of -0.30 V, respectively. Then the intrinsic relationship between structure and the potential performance was further analyzed by using machine-learning. The selectivity, feasibility, stability of these candidates were also evaluated, confirming that SAA containing Mo, Ru ,Ta, and W could be outstanding NRR electrocatalysts. This work not only broadens the understating of SAA application in electrocatalysis, but also devotes to the discovery of novel NRR electrocatalysts.</p><br><p></p>

Non-Noble Single-Atom Alloy for Electrocatalytic Nitrate Reduction Using Hierarchical High-Throughput Screening

“…[27−29] TMs are often characterized by their excellent electronic properties due to their unique d−orbital electron arrangement, which is of great research significance in the synthesis of SAAs and in characterizing their catalytic activity. Up to now, SAAs have been widely used to promote electrocatalytic activity, which can effectively catalyze selective hydrogenations, [30,31] C−C coupling reactions, [32] oxygen reduction, [33,34] NO reduction, [35] CO 2 reduction, [36] nitrogen reduction [37] and CO oxidation [38], among many other reactions. J o u r n a l P r e -p r o o f 5 transition metals, as shown in Fig.…”

A Case Study on the Generative AI Project Life Cycle Using Large Language Models

“…They typically necessitate high-performance CPUs with multiple cores and high clock speeds to efficiently process large text datasets. Graphics Processing Units (GPUs) are commonly employed to accelerate training and inference tasks by parallelizing computations [29]. Alternatively, Tensor Processing Units (TPUs), known for their exceptional speed and energy efficiency, are increasingly utilized, especially in large-scale deep learning applications.…”