Abstract: In epitaxial thin film systems, the crystal structure and its symmetry deviate from the bulk counterpart due to various mechanisms such as epitaxial strain and interfacial structural coupling, which induce an accompanying change in their properties. In perovskite materials, the crystal symmetry can be described by rotations of 6-fold coordinated transition metal oxygen octahedra, which are found to be altered at interfaces. Here, we unravel how the local oxygen octahedral coupling (OOC) at perovskite heterostructural interfaces initiates a different symmetry in epitaxial films and provide design rules to induce various symmetries in thin films by careful selecting appropriate combinations of substrate/buffer/film. Very interestingly we discovered that these combinations lead to symmetry changes throughout the full thickness of the film. Our results provide a deep insight into understanding the origin of induced crystal symmetry in a perovskite heterostructure and an intelligent route to achieve unique functional properties.

Abstract: High angle annular dark field scanning transmission electron microscopy (HAADF STEM) images provide sample information which is sensitive to the chemical composition. The image intensities indeed scale with the mean atomic number Z. To some extent, chemically different atomic column types can therefore be visually distinguished. However, in order to quantify the atomic column composition with high accuracy and precision, model-based methods are necessary. Therefore, an empirical incoherent parametric imaging model can be used of which the unknown parameters are determined using statistical parameter estimation theory (Van Aert et al., 2009, [1]). In this paper, it will be shown how this method can be combined with frozen lattice multislice simulations in order to evolve from a relative toward an absolute quantification of the composition of single atomic columns with mixed atom types. Furthermore, the validity of the model assumptions are explored and discussed.

Abstract: In materials science, high angle annular dark field scanning transmission electron microscopy is often used for tomography at the nanometer scale. In this work, it is shown that a thickness dependent, non-linear damping of the recorded intensities occurs. This results in an underestimated intensity in the interior of reconstructions of homogeneous particles, which is known as the cupping artifact. In this paper, this non-linear effect is demonstrated in experimental images taken under common conditions and is reproduced with a numerical simulation. Furthermore, an analytical derivation shows that these non-linearities can be inverted if the imaging is done quantitatively, thus preventing cupping in the reconstruction.

Abstract: The maximum a posteriori (MAP) probability rule for atom column detection can also be used as a tool to evaluate the relation between scanning transmission electron microscopy (STEM) image quality and atom detectability. In this chapter, a new image-quality measure is proposed that correlates well with atom detectability, namely the integrated contrast-to-noise ratio (ICNR). Furthermore, the working principle of the MAP probability rule is described in detail showing a close relation to the principles of model-selection methods.

Abstract: In this chapter, the principles of detection theory are used to quantify the probability of error for atom counting from high-resolution scanning transmission electron microscopy (HRSTEM) images. Binary and multiple hypothesis testing have been investigated in order to determine the limits to the precision with which the number of atoms in a projected atomic column can be estimated. The probability of error has been calculated when using STEM images, scattering cross-sections or peak intensities as a criterion to count atoms. Based on this analysis, we conclude that scattering cross-sections perform almost equally well as images and perform better than peak intensities. Furthermore, the optimal STEM detector design can be derived for atom counting using the expression of the probability of error. We show that for very thin objects the low-angle annular dark-field (LAADF) regime is optimal and that for thicker objects the optimal inner detector angle increases.

Abstract: An efficient model-based estimation algorithm is introduced to quantify the atomic column positions and intensities from atomic-resolution (scanning) transmission electron microscopy ((S)TEM) images. This algorithm uses the least squares estimator on image segments containing individual columns fully accounting for overlap between neighboring columns, enabling the analysis of a large field of view. To provide end-users with this well-established quantification method, a user friendly program, StatSTEM, is developed which is freely available under a GNU public license. In this chapter, this efficient algorithm is applied to three different nanostructures for which the analysis of a large field of view is required.

Abstract: In epitaxial thin film systems, the crystal structure and its symmetry deviate from the bulk counterpart due to various mechanisms such as epitaxial strain and interfacial structural coupling, which is accompanyed by a change in their properties. In perovskite materials, the crystal symmetry can be described by rotations of sixfold coordinated transition metal oxygen octahedra, which are found to be altered at interfaces. Here, it is unraveled how the local oxygen octahedral coupling at perovskite heterostructural interfaces strongly influences the domain structure and symmetry of the epitaxial films resulting in design rules to induce various structures in thin films using carefully selected combinations of substrate/buffer/film. Very interestingly it is discovered that these combinations lead to structure changes throughout the full thickness of the film. The results provide a deep insight into understanding the origin of induced structures in a perovskite heterostructure and an intelligent route to achieve unique functional properties.

Abstract: Understanding light–matter interactions in nanomaterials is crucial for
optoelectronic, photonic, and plasmonic applications. Specifically, metal
nanoparticles (NPs) strongly interact with light and can undergo shape
transformations, fragmentation and ablation upon (pulsed) laser excitation.
Despite being vital for technological applications, experimental insight into
the underlying atomistic processes is still lacking due to the complexity of
such measurements. Herein, atomic resolution electron tomography is performed
on the same mesoporous-silica-coated gold nanorod, before and after
femtosecond laser irradiation, to assess the missing information. Combined
with molecular dynamics (MD) simulations based on the experimentally
determined 3D atomic-scale morphology, the complex atomistic rearrangements,
causing shape deformations and defect generation, are unraveled.
These rearrangements are simultaneously driven by surface diffusion, facet
restructuring, and strain formation, and are influenced by subtleties in the
atomic distribution at the surface.

Abstract: 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. Experimental and computational techniques are bridged to unveil atomic dynamics in gold nanoparticles (NPs), using annular dark-field scanning transmission electron microscopy and molecular dynamics simulations informed by machine learning. The approach provides unprecedented insights into the real-time structural behaviors of NPs, merging state-of-the-art techniques to accurately characterize their dynamics under realistic conditions. image

Abstract: The presence of iodide ions during the growth of gold nanorods strongly affects the shape of the final products, which is proposed to be due to selective iodide adsorption on certain crystallographic facets. Therefore, a detailed structural and morphological characterization of the starting rods is crucial toward understanding this effect. Electron tomography is used to determine the crystallographic indices of the lateral facets of gold nanorods, as well as those present at the tips. Based on this information, density functional theory calculations are used to determine the surface and interface energies of the observed facets and provide insight into the relationship between the amount of iodide ions in the growth solution and the final morphology of anisotropic gold nanoparticles.