Abstract: Plasma catalysis is a rapidly growing field, often employing a packed-bed dielectric barrier discharge plasma reactor. Such dielectric barrier discharges are complex, especially when a packing material (e.g., a catalyst) is introduced in the discharge volume. Catalysts are known to affect the plasma discharge, though the underlying mechanisms influencing the plasma physics are not fully understood. Moreover, the effect of the catalysts on the plasma discharge and its subsequent effect on the overall performance is often overlooked. In this work, we deliberately design and synthesize catalysts to affect the plasma discharge in different ways. These Ni or Co alumina-based catalysts are used in plasma-catalytic dry reforming of methane and ammonia synthesis. Our work shows that introducing a metal to the dielectric packing can affect the plasma discharge, and that the distribution of the metal is crucial in this regard. Further, the altered discharge can greatly influence the overall performance. In an atmospheric pressure dielectric barrier discharge reactor, this apparently more uniform plasma yields a significantly better performance for ammonia synthesis compared to the more conventional filamentary discharge, while it underperforms in dry reforming of methane. This study stresses the importance of analyzing the plasma discharge in plasma catalysis experiments. We hope this work encourages a more critical view on the plasma discharge characteristics when studying various catalysts in a plasma reactor.

Abstract: We present an investigation of the microstructure changes in V2O3 as it goes through its inherent structural phase transition. Using V2O3 films with a well-defined crystal structure deposited by reactive magnetron sputtering on r-plane Al2O3 substrates, we study the phase coexistence region and its impact on the surface roughness of the films and the magnetic properties of overlying Ni magnetic layers in V2O3/Ni hybrid magnetic heterostructures. The simultaneous presence of two phases in V2O3 during its structural phase transition was identified with high resolution x-ray diffraction and led to an increase in surface roughness observed using x-ray reflectivity. The roughness reaches its maximum at the midpoint of the transition. In V2O3/Ni hybrid heterostructures, we find a concomitant increase in the coercivity of the magnetic layer correlated with the increased roughness of the V2O3 surface. The chemical homogeneity of the V2O3 is confirmed through transmission electron microscopy analysis. High-angle annular dark field imaging and electron energy loss spectroscopy reveal an atomically flat interface between Al2O3 and V2O3, as well as a sharp interface between V2O3 and Ni.

Abstract: The need to develop green and cost-effective industrial catalytic processes has led to growing interest in preparing more robust, efficient, and selective heterogeneous catalysts at a large scale. In this regard, microwave-assisted synthesis is a fast method for fabricating heterogeneous catalysts (including metal oxides, zeolites, metal–organic frameworks, and supported metal nanoparticles) with enhanced catalytic properties, enabling synthesis scale-up. Herein, the synthesis of nanosized UiO-66-NH2 was optimized via a microwave-assisted hydrothermal method to obtain defective matrices essential for the stabilization of metal nanoparticles, promoting catalytically active sites for hydrogenation reactions (760 kg·m–3·day–1 space time yield, STY). Then, this protocol was scaled up in a multimodal microwave reactor, reaching 86% yield (ca. 1 g, 1450 kg·m–3·day–1 STY) in only 30 min. Afterward, Pd nanoparticles were formed in situ decorating the nanoMOF by an effective and fast microwave-assisted hydrothermal method, resulting in the formation of Pd@UiO-66-NH2 composites. Both the localization and oxidation states of Pd nanoparticles (NPs) in the MOF were achieved using high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) and X-ray photoelectron spectroscopy (XPS), respectively. The optimal composite, loaded with 1.7 wt % Pd, exhibited an extraordinary catalytic activity (>95% yield, 100% selectivity) under mild conditions (1 bar H2, 25 °C, 1 h reaction time), not only in the selective hydrogenation of a variety of single alkenes (1-hexene, 1-octene, 1-tridecene, cyclohexene, and tetraphenyl ethylene) but also in the conversion of a complex mixture of alkenes (i.e., 1-hexene, 1-tridecene, and anethole). The results showed a powerful interaction and synergy between the active phase (Pd NPs) and the catalytic porous scaffold (UiO-66-NH2), which are essential for the selectivity and recyclability.

Abstract: A thorough and quantitative understanding of the fate of excitons in covalent–organic frameworks (COFs) after photoexcitation is essential for their augmented optoelectronic and photocatalytic applications via precise structure tuning. The synthesis of a library of COFs having identical chemical backbone with impeded conjugation, but varied morphology and surface topography to study the effect of these physical properties on the photophysics of the materials is herein reported. The variation of crystallite size and surface topography substantified different aggregation pattern in the COFs, which leads to disparities in their photoexcitation and relaxation properties. Depending on aggregation, an inverse correlation between bulk luminescence decay time and exciton binding energy of the materials is perceived. Further transient absorption spectroscopic analysis confirms the presence of highly localized, immobile, Frenkel excitons (of diameter 0.3–0.5 nm) via an absence of annihilation at high density, most likely induced by structural torsion of the COF skeletons, which in turn preferentially relaxes via long‐lived (nanosecond to microsecond) excimer formation (in femtosecond scale) over direct emission. These insights underpin the importance of structural and topological design of COFs for their targeted use in photocatalysis.

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