Transformations of supported gold nanoparticles observed by in situ electron microscopy

Interpretability of high-resolution transmission electron microscopy images

High-resolution electron microscopy is a well-suited tool for characterizing the nanoscale structure of materials. However, the interaction of the sample and the high-energy electrons of the beam can often have a detrimental impact on the sample structure. This effect can only be alleviated by decreasing the number of electrons to which the sample is exposed but will come at the cost of a decreased signal-to-noise ratio in the resulting image. Images with low signal to noise ratios are often challenging to interpret as parts of the sample with a low interaction with the electron beam are reproduced with very low contrast. Here we suggest simple measures as alternatives to the conventional signal-to-noise ratio and investigate how these can be used to predict the interpretability of the electron microscopy images. We test the models on a sample consisting of gold nanoparticles supported on a cerium dioxide substrate. The models are evaluated based on series of images acquired at varying electron dose.

Beam induced heating in electron microscopy modeled with machine learning interatomic potentials

We develop a combined theoretical and experimental method for estimating the amount of heating that occurs in metallic nanoparticles that are being imaged in an electron microscope. We model the thermal transport between the nanoparticle and the supporting material using molecular dynamics and equivariant neural network potentials. The potentials are trained to Density Functional Theory (DFT) calculations, and we show that an ensemble of potentials can be used as an estimate of the errors the neural network make in predicting energies and forces. This can be used both to improve the networks during the training phase, and to validate the performance when simulating systems too big to be described by DFT. The energy deposited into the nanoparticle by the electron beam is estimated by measuring the mean free path of the electrons and the average energy loss, both are done with Electron Energy Loss Spectroscopy (EELS) within the microscope. In combination, this allows us to predict the heating incurred by a nanoparticle as a function of its size, its shape, the support material, and the electron beam energy and intensity.

Quantifying noise limitations of neural network segmentations in high-resolution transmission electron microscopy

Motivated by the need for low electron dose transmission electron microscopy imaging, we report the optimal frame dose (i.e.e-/Å2) range for object detection and segmentation tasks with neural networks. The MSD-net architecture shows promising abilities over the industry standard U-net architecture in generalising to frame doses below the range included in the training set, for both simulated and experimental images. It also presents a heightened ability to learn from lower dose images. The MSD-net displays mild visibility of a Au nanoparticle at 20-30 e-/Å2, and converges at 200 e-/Å2 where a full segmentation of the nanoparticle is achieved. Between 30 and 200 e-/Å2 object detection applications are still possible. This work also highlights the importance of modelling the modulation transfer function when training with simulated images for applications on images acquired with scintillator based detectors such as the Gatan Oneview camera. A parametric form of the modulation transfer function is applied with varying ranges of parameters, and the effects on low electron dose segmentation is presented.

Dynamic interplay between metal nanoparticles and oxide support under redox conditions

The dynamic interactions between noble metal particles and reducible metal-oxide supports can depend on redox reactions with ambient gases. Transmission electron microscopy revealed that the strong metal-support interaction (SMSI)-induced encapsulation of platinum particles on titania observed under reducing conditions is lost once the system is exposed to a redox-reactive environment containing oxygen and hydrogen at a total pressure of ~1 bar. Destabilization of the metal-oxide interface and redox-mediated reconstructions of titania lead to particle dynamics and directed particle migration that depend on nanoparticle orientation. A static encapsulated SMSI state was reestablished when switching back to purely oxidizing conditions. This work highlights the difference between reactive and nonreactive states and demonstrates that manifestations of the metal-support interaction strongly depend on the chemical environment.

Direct Observations of Twin Formation Dynamics in Binary Semiconductors

With the increased demand for controlled deterministic growth of III-V semiconductors at the nanoscale, the impact and interest of understanding defect formation and crystal structure switching becomes increasingly important. Vapor-liquid-solid (VLS) growth of semiconductor nanocrystals is an important mechanism for controlling and studying the formation of individual crystal layers and stacking defects. Using in situ studies, combining atomic resolution of transmission electron microscopy and controlled VLS crystal growth using metal organic chemical vapor deposition, we investigate the simplest achievable change in atomic layer stacking-single twinned layers formed in GaAs. Using Au-assisted GaAs nanowires of various diameters, we study the formation of individual layers with atomic resolution to reveal the growth difference in forming a twin defect. We determine that the formation of a twinned layer occurs significantly more slowly than that of a normal crystal layer. To understand this, we conduct thermodynamic modeling and determine that the propagation of a twin is limited by the energy cost of forming the twin interface. Finally, we determine that the slower propagation of twinned layers increases the probability of additional layers nucleating, such that multiple layers grow simultaneously. This observation challenges the current understanding that continuous uniform epitaxial growth, especially in the case of liquid-metal assisted nanowires, proceeds one single layer at a time and that its progression is limited by the nucleation rate.

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.

Nanometal Thermocatalysts: Transformations, Deactivation, and Mitigation

Nanometals have been proven to be efficient thermocatalysts in the last decades. Their enhanced catalytic activity and tunable functionalities make them intriguing candidates for a wide range of catalytic applications, such as gaseous reactions and compound synthesis/decomposition. On the other hand, the enhanced specific surface energy and reactivity of nanometals can lead to configuration transformation and thus catalytic deactivation during the synthesis and catalysis, which largely undermines the activity and service time, thereby calling for urgent research effort to understand the deactivating mechanisms and develop efficient mitigating methods. Herein, the recent progress in understanding the configuration transformation-induced catalytic deactivation within nanometals is reviewed. The major pathways of configuration transformations, and their kinetics controlled by the environmental factors are presented. The approaches toward mitigating the transformation-induced deactivation are also presented. Finally, a perspective on the future academic approaches toward in-depth understanding of the kinetics of the deactivation of nanometals is proposed.

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

Au nanoparticles (NPs) deposited on CeO2 are extensively used as thermal catalysts since the morphology of the NPs is expected to be stable at elevated temperatures. Although it is well known that the activity of Au NPs depends on their size and surface structure, their three-dimensional (3D) structure at the atomic scale has not been completely characterized as a function of temperature. In this paper, we overcome the limitations of conventional electron tomography by combining atom counting applied to aberration-corrected scanning transmission electron microscopy images and molecular dynamics relaxation. In this manner, we are able to perform an atomic resolution 3D investigation of supported Au NPs. Our results enable us to characterize the 3D equilibrium structure of single NPs as a function of temperature. Moreover, the dynamic 3D structural evolution of the NPs at high temperatures, including surface layer jumping and crystalline transformations, has been studied.

Measuring Dynamic Structural Changes of Nanoparticles at the Atomic Scale Using Scanning Transmission Electron Microscopy

We propose a new method to measure atomic scale dynamics of nanoparticles from experimental high-resolution annular dark field scanning transmission electron microscopy images. By using the so-called hidden Markov model, which explicitly models the possibility of structural changes, the number of atoms in each atomic column can be quantified over time. This newly proposed method outperforms the current atom-counting procedure and enables the determination of the probabilities and cross sections for surface diffusion. This method is therefore of great importance for revealing and quantifying the atomic structure when it evolves over time via adatom dynamics, surface diffusion, beam effects, or during in situ experiments.

Divergent influence of {1 1 1} vs. {1 0 0} crystal planes and Yb3+ dopant on CO oxidation paths in mixed nano-sized oxide Au/Ce1−xYbxO2−x/2 (x = 0 or 0.1) systems

In this paper, the fundamental information on interactions in systems concerning nanocrystalline gold disperses on the shaped (octahedron-like or cube-like) Ce_1−xYb_xO_2−x/2 (x = 0 or 0.1) support has been discussed.