In oxide heterostructures, different materials are integrated into a single artificial crystal, resulting in a breaking of inversion symmetry across the heterointerfaces. A notable example is the interface between polar and nonpolar materials, where valence discontinuities lead to otherwise inaccessible charge and spin states. This approach paved the way for the discovery of numerous unconventional properties absent in the bulk constituents. However, control of the geometric structure of the electronic wave functions in correlated oxides remains an open challenge. Here, we create heterostructures consisting of ultrathin ${\mathrm{SrRuO}}_{3}$, an itinerant ferromagnet hosting momentum-space sources of Berry curvature, and ${\mathrm{LaAlO}}_{3}$, a polar wide-band-gap insulator. Transmission electron microscopy reveals an atomically sharp $\mathrm{LaO}/{\mathrm{RuO}}_{2}/\mathrm{SrO}$ interface configuration, leading to excess charge being pinned near the ${\mathrm{LaAlO}}_{3}/{\mathrm{SrRuO}}_{3}$ interface. We demonstrate through magneto-optical characterization, theoretical calculations and transport measurements that the real-space charge reconstruction drives a reorganization of the topological charges in the band structure, thereby modifying the momentum-space Berry curvature in ${\mathrm{SrRuO}}_{3}$. Our results illustrate how the topological and magnetic features of oxides can be manipulated by engineering charge discontinuities at oxide interfaces.
We report an innovative method to explore the optimal experimental settings to detect light atoms from scanning transmission electron microscopy (STEM) images. Since light elements play a key role in many technologically important materials, such as lithium-battery devices or hydrogen storage applications, much effort has been made to optimize the STEM technique in order to detect light elements. Therefore, classical performance criteria, such as contrast or signal-to-noise ratio, are often discussed hereby aiming at improvements of the direct visual interpretability. However, when images are interpreted quantitatively, one needs an alternative criterion, which we derive based on statistical detection theory. Using realistic simulations of technologically important materials, we demonstrate the benefits of the proposed method and compare the results with existing approaches.
It is shown that the ultimate resolution is not limited by the bandwidth of the microscope but by the bandwidth (i.e., the scattering power) of the object. In the case of a crystal oriented along a zone axis, the scattering is enhanced by the channeling of the electrons. However, if the object is aperiodic along the beam direction, the bandwidth is much more reduced. A particular challenge are the amorphous objects. For amorphous materials, the natural bandwidth is that of the single atom and of the order of 1 Å −1 , which can be reached with the present generation of medium voltage microscopes without aberration correctors. A clear distinction is made between resolving a structure and refining, that is, between resolution and precision. In the case of an amorphous structure, the natural bandwidth also puts a limit on the number of atom coordinates that can be refined quantitatively. As a consequence, amorphous structures cannot be determined from one projection, but only by using atomic resolution tomography. Finally a theory of experiment design is presented that can be used to predict the optimal experimental setting or the best instrumental improvement. Using this approach it is suggested that the study of amorphous objects should be done at low accelerating voltage with correction of both spherical and chromatic aberration.
Various strategies have been proposed to engineer the band gap of metal halide perovskite nanocrystals (NCs) while preserving their structure and composition and thus ensuring spectral stability of the emission color. An aspect that has only been marginally investigated is how the type of surface passivation influences the structural/color stability of AMX3 perovskite NCs composed of two different M2+ cations. Here, we report the synthesis of blue-emitting Cs-oleate capped CsCdxPb1–xBr3 NCs, which exhibit a cubic perovskite phase containing Cd-rich domains of Ruddlesden–Popper phases (RP phases). The RP domains spontaneously transform into pure orthorhombic perovskite ones upon NC aging, and the emission color of the NCs shifts from blue to green over days. On the other hand, postsynthesis ligand exchange with various Cs-carboxylate or ammonium bromide salts, right after NC synthesis, provides monocrystalline NCs with cubic phase, highlighting the metastability of RP domains. When NCs are treated with Cs-carboxylates (including Cs-oleate), most of the Cd2+ ions are expelled from NCs upon aging, and the NCs phase evolves from cubic to orthorhombic and their emission color changes from blue to green. Instead, when NCs are coated with ammonium bromides, the loss of Cd2+ ions is suppressed and the NCs tend to retain their blue emission (both in colloidal dispersions and in electroluminescent devices), as well as their cubic phase, over time. The improved compositional and structural stability in the latter cases is ascribed to the saturation of surface vacancies, which may act as channels for the expulsion of Cd2+ ions from NCs.
We present a computational imaging mode for large scale electron microscopy data, which retrieves a complex wave from noisy/sparse intensity recordings using a deep learning approach and subsequently reconstructs an image of the specimen from the Convolutional Neural Network (CNN) predicted exit waves. We demonstrate that an appropriate forward model in combination with open data frameworks can be used to generate large synthetic datasets for training. In combination with augmenting the data with Poisson noise corresponding to varying dose-values, we effectively eliminate overfitting issues. The U-NET based architecture of the CNN is adapted to the task at hand and performs well while maintaining a relatively small size and fast performance. The validity of the approach is confirmed by comparing the reconstruction to well-established methods using simulated, as well as real electron microscopy data. The proposed method is shown to be effective particularly in the low dose range, evident by strong suppression of noise, good spatial resolution, and sensitivity to different atom types, enabling the simultaneous visualisation of light and heavy elements and making different atomic species distinguishable. Since the method acts on a very local scale and is comparatively fast it bears the potential to be used for near-real-time reconstruction during data acquisition.
The atomic dynamics of metal nanoparticles (NPs), prominent already at low temperatures, is crucial for their properties but also challenging to elucidate. Recent advances in experimental approaches may provide atomically resolved snapshots of the structure of NPs in relevant regimes, but limitations in experimental data acquisition hinder the reconstruction of the atomic dynamics present within them. Molecular simulations -- typically starting from ideal/perfect NP structures -- allow tracking the motion of atoms over time, but suffer from limited sampling and provide results that, being dependent on the initial (putative) structure, are often only indicative. Here, combining state-of-the-art experimental and computational approaches, we demonstrate how it is possible to tackle the inherent limitations of both methods and resolve the atomistic dynamics present in metal NPs under realistic conditions. Annular dark-field scanning transmission electron microscopy (ADF-STEM) enables the acquisition of a time series of ten high-resolution images of an Au NP. Each image is taken at intervals of 0.6 seconds, providing data on a second timescale during the experimental sampling. These are used to reconstruct atomistic 3D structures of the real NP that are then used as starting configurations for ten independent molecular dynamics (MD) simulations. Unsupervised machine learning analysis of the data extracted from the MD trajectories using advanced structural and dynamical descriptors allows tracking and resolving the real-time atomic dynamics present within the NP under relevant conditions. This provides new perspectives into the realistic atomic dynamics within such NPs. We expect that such integrated experimental/computational approaches will become fundamental in various fields where the dynamics of NPs plays a key role, from catalysis to, e.g., nanoelectronics and biomedicine.
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.
Correction for ‘Atomic-scale detection of individual lead clusters confined in linde type A zeolites’ by Jarmo Fatermans et al. , Nanoscale , 2022, 14 , 9323–9330, https://doi.org/10.1039/D2NR01819E.
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