Salem Maya, Mpourmpakis Giannis
Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, USA.
Nanoscale. 2025 Jan 29;17(5):2830-2840. doi: 10.1039/d4nr04202f.
Single atom alloys (SAAs) have gained tremendous attention as promising materials with unique physicochemical properties, particularly in catalysis. The stability of SAAs relies on the formation of a single active dopant on the surface of a metal host, quantified by the surface segregation and aggregation energy. Previous studies have investigated the surface segregation of non-ligated and ligated SAAs to reveal the driving forces underlying such phenomena. In this work we address another key factor dictating the stability in non-ligated and ligated SAAs: the aggregation energy () of dopants. Specifically, we examine how thiols and amines, commonly found ligands in colloidal bimetallic nanoparticle synthesis, affect the aggregation of dopants (forming dimers and trimers) on the surface of a metal host. Utilizing Density Functional Theory (DFT) and machine learning (ML), we explore the stability patterns of SAAs through the energetics of low-index surfaces, such as (111) and (100), consisting of d-(Pt, Pd, Ni) and d-(Ag, Au, Cu) metals, both in the presence and absence of ligands. Collecting rich and accurate DFT data, we developed a four-feature support vector regression using the radial basis function (SVR RBF) to predict the . The model revealed important and easily accessible (tabulated) thermodynamic stability features that drive metal aggregation in SAAs, such as the bulk cohesive energy of the metal considering the exposed coordination environment on the surface, the charge transfer represented by the difference in electron affinities of metals and the radii of the metals describing strain effects. Additional incorporated features include adsorbate properties, such as the binding energy of the ligand on a single atom considering the coordination environment of the adsorbate. Through our study, we have revealed that stable SAAs are formed in Ni-, Pd-, Pt-based SAAs in the presence of ligands, while Ag-, Au-, Cu- doped with Ni-, Pd-, Pt- lead to aggregation. Finally, we tested our model against several experimental studies and demonstrated its robustness in predicting the formation of SAAs, enabling rapid screening across the vast materials space of SAAs. Additionally, we suggest criteria for stabilization of SAAs, guiding experimental efforts. Overall, our study advances the understanding of thermodynamic stability of colloidal SAAs, paving the way for rational SAA design.
单原子合金（SAA）作为具有独特物理化学性质的有前途的材料，特别是在催化领域，已引起了极大的关注。SAA的稳定性依赖于在金属主体表面形成单个活性掺杂剂，这可通过表面偏析和聚集能来量化。先前的研究已经调查了未配位和配位SAA的表面偏析，以揭示此类现象背后的驱动力。在这项工作中，我们探讨了决定未配位和配位SAA稳定性的另一个关键因素：掺杂剂的聚集能（）。具体而言，我们研究了硫醇和胺（在胶体双金属纳米颗粒合成中常见的配体）如何影响金属主体表面上掺杂剂的聚集（形成二聚体和三聚体）。利用密度泛函理论（DFT）和机器学习（ML），我们通过低指数表面（如（111）和（100））的能量学来探索SAA的稳定性模式，这些表面由d-（Pt、Pd、Ni）和d-（Ag、Au、Cu）金属组成，有无配体均进行研究。收集丰富而准确的DFT数据后，我们使用径向基函数开发了一种四特征支持向量回归（SVR RBF）来预测。该模型揭示了驱动SAA中金属聚集的重要且易于获取（列表形式）的热力学稳定性特征，例如考虑表面暴露配位环境的金属体相内聚能、由金属电子亲和能差异表示的电荷转移以及描述应变效应的金属半径。额外纳入的特征包括吸附质性质，例如考虑吸附质配位环境时配体在单个原子上的结合能。通过我们的研究，我们发现，在有配体存在的情况下，基于Ni、Pd、Pt的SAA中会形成稳定的SAA，而掺杂Ni、Pd、Pt的Ag、Au、Cu会导致聚集。最后，我们针对多项实验研究对我们的模型进行了测试，并证明了其在预测SAA形成方面的稳健性，能够在广阔的SAA材料空间中进行快速筛选。此外，我们提出了SAA稳定化的标准，为实验工作提供指导。总体而言，我们的研究推进了对胶体SAA热力学稳定性的理解，为合理设计SAA铺平了道路。
