Three-dimensional atomic structure of supported Au nanoparticles at high temperature

Sampling Real-Time Atomic Dynamics in Metal Nanoparticles by Combining Experiments, Simulations, and Machine Learning

Even at low temperatures, metal nanoparticles (NPs) possess atomic dynamics that are key for their properties but challenging to elucidate. Recent experimental advances allow obtaining atomic-resolution snapshots of the NPs in realistic regimes, but data acquisition limitations hinder the experimental reconstruction of the atomic dynamics present within them. Molecular simulations have the advantage that these allow directly tracking the motion of atoms over time. However, these typically start from ideal/perfect NP structures and, suffering from sampling limits, provide results that are often dependent on the initial/putative structure and remain purely indicative. Here, by combining state-of-the-art experimental and computational approaches, how it is possible to tackle the limitations of both approaches and resolve the atomistic dynamics present in metal NPs in realistic conditions is demonstrated. Annular dark-field scanning transmission electron microscopy enables the acquisition of ten high-resolution images of an Au NP at intervals of 0.6 s. These are used to reconstruct atomistic 3D models of the real NP used to run ten independent molecular dynamics simulations. Machine learning analyses of the simulation trajectories allow resolving the real-time atomic dynamics present within the NP. This provides a robust combined experimental/computational approach to characterize the structural dynamics of metal NPs in realistic conditions.

Machine learning of atomic dynamics and statistical surface identities in gold nanoparticles

It is known that metal nanoparticles (NPs) may be dynamic and atoms may move within them even at fairly low temperatures. Characterizing such complex dynamics is key for understanding NPs' properties in realistic regimes, but detailed information on, e.g., the stability, survival, and interconversion rates of the atomic environments (AEs) populating them are non-trivial to attain. In this study, we decode the intricate atomic dynamics of metal NPs by using a machine learning approach analyzing high-dimensional data obtained from molecular dynamics simulations. Using different-shape gold NPs as a representative example, an AEs' dictionary allows us to label step-by-step the individual atoms in the NPs, identifying the native and non-native AEs and populating them along the MD simulations at various temperatures. By tracking the emergence, annihilation, lifetime, and dynamic interconversion of the AEs, our approach permits estimating a "statistical equivalent identity" for metal NPs, providing a comprehensive picture of the intrinsic atomic dynamics that shape their properties.

A decade of atom-counting in STEM: From the first results toward reliable 3D atomic models from a single projection

Quantitative structure determination is needed in order to study and understand nanomaterials at the atomic scale. Materials characterisation resulting in precise structural information is a crucial point to understand the structure-property relation of materials. Counting the number of atoms and retrieving the 3D atomic structure of nanoparticles plays an important role here. In this paper, an overview will be given of the atom-counting methodology and its applications over the past decade. The procedure to count the number of atoms will be discussed in detail and it will be shown how the performance of the method can be further improved. Furthermore, advances toward mixed element nanostructures, 3D atomic modelling based on the atom-counting results, and quantifying the nanoparticle dynamics will be highlighted.

Surface Rearrangement and Sublimation Kinetics of Supported Gold Nanoparticle Catalysts

Heterogeneous catalysts consisting of supported metallic nanoparticles typically derive exceptional catalytic activity from their large proportion of undercoordinated surface sites which promote adsorption of reactant molecules. Simultaneously, these high energy surface configurations are unstable, leading to nanoparticle growth or degradation and eventually a loss of catalytic activity. Surface morphology of catalytic nanoparticles is paramount to catalytic activity, selectivity, and degradation rates, however it is well-known that harsh reaction conditions can cause the surface structure to change. Still, limited research has focused on understanding the link between nanoparticle surface facets and degradation rates or mechanisms. Here, we study a model Au supported catalyst system over a range of temperatures using a combination of in situ transmission electron microscopy, kinetic Monte Carlo simulations, and density functional theory calculations to establish an atomistic picture of how variations in surface structures and atomic coordination environments lead to shifting evolution mechanisms as a function of temperature. By combining experimental results, which yield direct observation of dynamic shape changes and particle sublimation rates, with computational techniques, which enable understanding the fundamental thermodynamics and kinetics of nanoparticle evolution, we illustrate a two-step evolution mechanism in which mobile adatoms form through desorption from low-coordination facets and subsequently sublimate off the particle surface. By understanding the role of temperature in the competition between surface diffusion and sublimation, we are able to show how individual atomic movements lead to particle scale morphological changes and rationalize why sublimation rates vary between particles in a system of nearly identical nanoparticles.

Fast generation of calculated ADF-EDX scattering cross-sections under channelling conditions

Advanced materials often consist of multiple elements which are arranged in a complicated structure. Quantitative scanning transmission electron microscopy is useful to determine the composition and thickness of nanostructures at the atomic scale. However, significant difficulties remain to quantify mixed columns by comparing the resulting atomic resolution images and spectroscopy data with multislice simulations where dynamic scattering needs to be taken into account. The combination of the computationally intensive nature of these simulations and the enormous amount of possible mixed column configurations for a given composition indeed severely hamper the quantification process. To overcome these challenges, we here report the development of an incoherent non-linear method for the fast prediction of ADF-EDX scattering cross-sections of mixed columns under channelling conditions. We first explain the origin of the ADF and EDX incoherence from scattering physics suggesting a linear dependence between those two signals in the case of a high-angle ADF detector. Taking EDX as a perfect incoherent reference mode, we quantitatively examine the ADF longitudinal incoherence under different microscope conditions using multislice simulations. Based on incoherent imaging, the atomic lensing model previously developed for ADF is now expanded to EDX, which yields ADF-EDX scattering cross-section predictions in good agreement with multislice simulations for mixed columns in a core-shell nanoparticle and a high entropy alloy. The fast and accurate prediction of ADF-EDX scattering cross-sections opens up new opportunities to explore the wide range of ordering possibilities of heterogeneous materials with multiple elements.

Recent Progress on Revealing 3D Structure of Electrocatalysts Using Advanced 3D Electron Tomography: A Mini Review

Electrocatalysis plays a key role in clean energy innovation. In order to design more efficient, durable and selective electrocatalysts, a thorough understanding of the unique link between 3D structures and properties is essential yet challenging. Advanced 3D electron tomography offers an effective approach to reveal 3D structures by transmission electron microscopy. This mini-review summarizes recent progress on revealing 3D structures of electrocatalysts using 3D electron tomography. 3D electron tomography at nanoscale and atomic scale are discussed, respectively, where morphology, composition, porous structure, surface crystallography and atomic distribution can be revealed and correlated to the performance of electrocatalysts. (Quasi) in-situ 3D electron tomography is further discussed with particular focus on its impact on electrocatalysts’ durability investigation and post-treatment. Finally, perspectives on future developments of 3D electron tomography for eletrocatalysis is discussed.

3D Atomic Structure of Supported Metallic Nanoparticles Estimated from 2D ADF STEM Images: A Combination of Atom-Counting and a Local Minima Search Algorithm

Determining the 3D atomic structure of nanoparticles (NPs) is critical to understand their structure-dependent properties. It is hereby important to perform such analyses under conditions relevant for the envisioned application. Here, the 3D structure of supported Au NPs at high temperature, which is of importance to understand their behavior during catalytic reactions, is investigated. To overcome limitations related to conventional high-resolution electron tomography at high temperature, 3D characterization of NPs with atomic resolution has been performed by applying atom-counting using atomic resolution annular dark-field scanning transmission electron microscopy (ADF STEM) images followed by structural relaxation. However, at high temperatures, thermal displacements, which affect the ADF STEM intensities, should be taken into account. Moreover, it is very likely that the structure of an NP investigated at elevated temperature deviates from a ground state configuration, which is difficult to determine using purely computational energy minimization approaches. In this paper, an optimized approach is therefore proposed using an iterative local minima search algorithm followed by molecular dynamics structural relaxation of candidate structures associated with each local minimum. In this manner, it becomes possible to investigate the 3D atomic structure of supported NPs, which may deviate from their ground state configuration.