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: Out-of-equilibrium self-assembly of metal nanoparticles (NPs) has been devised using different
types of strategies and fuels, but the achievement of finite 3D structures with a controlled
morphology through this assembly mode is still rare. Here we used a spherical peptide-gold
superstructure (PAuSS) as a template to control the out-of-equilibrium self-assembly of Au NPs,
obtaining a transient 3D branched Au-nanoshell (BAuNS) stabilized by sodium dodecyl sulphate
(SDS). The BAuNS dismantled upon concentration gradient equilibration over time in the solution,
leading to NPs disassembly. Notably, BAuNS assembly and disassembly favoured temporary
interparticle plasmonic coupling, leading to a remarkable oscillation of their optical properties.

Abstract: Supplementary information for the article “In-situ Plasma Studies using a Direct Current Microplasma in a Scanning Electron Microscope” containing the videos of in-situ SEM imaging (mp4 files), raw data/images, and Jupyter notebooks (ipynb files) for data treatment and plots. Link to the preprint: https://doi.org/10.48550/arXiv.2308.15123 Explanation of the data files can be found in the Information.pdf file. The Videos folder contains the in-situ SEM image series mentioned in the paper. If there are any questions/bugs, feel free to contact me at lukas.grunewaldatuantwerpen.be

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.