Abstract: The understanding of catalyst active sites is a fundamental challenge for the future rational design of optimized and bespoke catalysts. For instance, the partial reduction of Ce4+ surface sites to Ce3+ and the formation of oxygen vacancies are critical for CO2 hydrogenation, CO oxidation, and the water gas shift reaction. Furthermore, metal nanoparticles, the reducible support, and metal support interactions are prone to evolve under reaction conditions; therefore a catalyst structure must be characterized under operando conditions to identify active states and deduce structure-activity relationships. In the present work, temperature-induced morphological and chemical changes in Ni nanoparticle-decorated mesoporous CeO2 by means of in situ quantitative multimode electron tomography and in situ heating electron energy loss spectroscopy, respectively, are investigated. Moreover, operando electron energy loss spectroscopy is employed using a windowed gas cell and reveals the role of Ni-induced hydrogen spillover on active Ce3+ site formation and enhancement of the overall catalytic performance.

Abstract: The electroreduction of carbon dioxide (eCO2RR) to CO using Ag nanoparticles as an electrocatalyst is promising as an industrial carbon capture and utilization (CCU) technique to mitigate CO2 emissions. Nevertheless, the long-term stability of these Ag nanoparticles has been insufficient despite initial high Faradaic efficiencies and/or partial current densities. To improve the stability, we evaluated an up-scalable and easily tunable synthesis route to deposit low-weight percentages of Ag nanoparticles (NPs) on and into the framework of a nitrogen-doped ordered mesoporous carbon (NOMC) structure. By exploiting this so-called nanoparticle confinement strategy, the nanoparticle mobility under operation is strongly reduced. As a result, particle detachment and agglomeration, two of the most pronounced electrocatalytic degradation mechanisms, are (partially) blocked and catalyst durability is improved. Several synthesis parameters, such as the anchoring agent, the weight percentage of Ag NPs, and the type of carbonaceous support material, were modified in a controlled manner to evaluate their respective impact on the overall electrochemical performance, with a strong emphasis on operational stability. The resulting powders were evaluated through electrochemical and physicochemical characterization methods, including X-ray diffraction (XRD), N2-physisorption, Inductively coupled plasma mass spectrometry (ICP-MS), scanning electron microscopy (SEM), SEM-energy-dispersive X-ray spectroscopy (SEM-EDS), high-angle annular dark field scanning transmission electron microscopy (HAADF-STEM), STEM-EDS, electron tomography, and X-ray photoelectron spectroscopy (XPS). The optimized Ag/soft-NOMC catalysts showed both a promising selectivity (∼80%) and stability compared with commercial Ag NPs while decreasing the loading of the transition metal by more than 50%. The stability of both the 5 and 10 wt % Ag/soft-NOMC catalysts showed considerable improvements by anchoring the Ag NPs on and into a NOMC framework, resulting in a 267% improvement in CO selectivity after 72 h (despite initial losses) compared to commercial Ag NPs. These results demonstrate the promising strategy of anchoring Ag NPs to improve the CO selectivity during prolonged experiments due to the reduced mobility of the Ag NPs and thus enhanced stability.

Abstract: The problem of the epitaxial growth of the high temperature superconducting R1+xBa2xCu3O7δ (R = Y or rare earth except Ce and Tb) films has been addressed. Using in situ ultra high vacuum Scanning Tunneling Microscopy (UHV-STM) we have studied the role of cationic substitution and substrate mismatch on the growth mode of stoichiometric and Nd-rich Nd1+xBa2xCu3O7δ thin films. The results are compared to the growth of Y1Ba2Cu3O7δ, Dy1Ba2Cu3O7δ and Gd1Ba2Cu3O7δ epitaxial films. Two main phenomena are investigated: a) the first stage of the direct nucleation on the substrate and b) the crossover between 2D and 3D growth upon increasing the film thickness. At the first stage of the growth, pseudo-cubic perovskite (Re,Ba)CuO3 nuclei are formed. While they disappear after the growth of a few nm in stoichiometric films, they persist on the surface of Nd-rich films of up to 110 nm thickness. Stoichiometric R1+xBa2xCu3O7δ films exhibit a rough morphology with increasing thickness due to island growth mode, whereas Nd-rich films remain smooth and continue to grow layer by layer. It is proposed that linear defects (like anti-phase boundaries), which are formed due to the misalignment of growth fronts, are the source of screw dislocations in stoichiometric films. In Nd-rich films, linear defects are eliminated through the insertion of (Nd,Ba)CuO3 extra layers without introduction of any screw dislocations.

Abstract: This dataset contains 1000 simulated nanoparticle-like 3D structures and noisy EDX-like elemental maps based on them. These data are intended to be used for quantitative analysis of data processing methods in (EDX) tomography of nanoparticles and training the data-driven approaches for these tasks. The dataset is structured as follows: voxel_data/clean 3D voxel grid representation of the simulated nanoparticles. Voxel intensities are adjusted so that the total intensity equals 103. All 3D structures have unique identifiers in 0..999 range. The data derived from a 3D structure preserves this unique identifier. sinograms/clean Tilt series of projection images obtained from the corresponding 3D structures over an angular range of -75..75 degrees with a tilt step of 10 degrees to simulate a typical tilt series used in EDX tomography. Total intensity in each projection image equals 103. sinograms/noisy Tilt series of projection images corrupted with Poisson noise and an additional spatially uniform background noise. projections/clean Projection images extracted from the clean tilt series at 0 degrees tilt angle. projections/noisy Projection images extracted from the noisy tilt series at 0 degrees tilt angle. images/clean Visualizations of the clean projections as PNG images with the intensity range adjusted to 0..255 images/noisy Visualizations of the noisy projections as PNG images with the intensity range adjusted to 0..255

Abstract: Metal halide perovskite (MHP) quantum dots (QDs) offer immense potential for several areas of photonics research due to their easy and low-cost fabrication and excellent optoelectronic properties. However, practical applications of MHP QDs are limited by their poor stability and, in particular, their tendency to aggregate. Here, we develop a two-step double-solvent strategy to grow and confine CsPbBr3 QDs within the three-dimensional (3D) cavities of a mesoporous SBA-16 silica scaffold (CsPbBr3@SBA-16). Strong confinement and separation of the MHP QDs lead to a relatively uniform size distribution, narrow luminescence, and good ambient stability over 2 months. In addition, the CsPbBr3@SBA-16 presents a high activity and stability for visible-light-driven photocatalytic toluene C(sp(3))-H bond activation to produce benzaldehyde with similar to 730 mu mol g(-1) h(-1) yield rate and near-unity selectivity. Similarly, the structural stability of CsPbBr3@SBA-16 QDs is superior to that of both pure CsPbBr3 QDs and those confined in MCM-41 with 1D channels.

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 allows 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.

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