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: The efficiency of a catalytic process is assessed based on conversion, yield, and time effectiveness. However, these parameters are insufficient for evaluating environmentally sustainable research. As the world is urged to shift towards green catalysis, additional factors such as reaction media, raw material availability, sustainability, waste minimization and catalyst biosafety, need to be considered to accurately determine the efficacy and sustainability of the process. By combining the high porosity and versatility of metal organic frameworks (MOFs) and the activity of gold nanoparticles (AuNPs), efficient, cyclable and biosafe composite catalysts can be achieved. Thus, a composite based on AuNPs and the nanometric flexible porous iron(III) aminoterephthalate MIL-88B-NH2 was successfully synthesized and fully characterized. This nanocomposite was tested as catalyst in the reduction of nitroarenes, which were identified as anthropogenic water pollutants, reaching cyclable high conversion rates at short times for different nitroarenes. Both synthesis and catalytic reactions were performed using green conditions, and even further tested in a time-optimizing one-pot synthesis and catalysis experiment. The sustainability and environmental impact of the catalytic conditions were assessed by green metrics. Thus, this study provides an easily implementable synthesis, and efficient catalysis, while minimizing the environmental and health impact of the process.

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: 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: 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: The interest in 2-dimensional systems with a honeycomb lattice and related Dirac-­type electronic bands has exceeded the prototype graphene1. Currently, 2-­dimensional atomic2,3 and nanoscale4-­8 systems are extensively investigated in the search for materials with novel electronic properties that can be tailored by geometry. The immediate question that arises is how to fabricate 2-­D semiconductors that have a honeycomb nanogeometry, and as a consequence of that, display a Dirac-­type band structure? Here, we show that atomically coherent honeycomb superlattices of rocksalt (PbSe, PbTe) and zincblende (CdSe, CdTe) semiconductors can be obtained by nanocrystal self-­assembly and facet-­to-­facet atomic bonding, and subsequent cation exchange. We present a extended structural analysis of atomically coherent 2-­D honeycomb structures that were recently obtained with self-assembly and facet-­to-­facet bonding9. We show that this process may in principle lead to three different types of honeycomb structures, one with a graphene type-­, and two others with a silicene-­type structure. Using TEM, electron diffraction, STM and GISAXS it is convincingly shown that the structures are from the silicene-­type. In the second part of this work, we describe the electronic structure of graphene-­type and silicene type honeycomb semiconductors. We present the results of advanced electronic structure calculations using the sp3d5s* atomistic tight-­binding method10. For simplicity, we focus on semiconductors with a simple and single conduction band for the native bulk semiconductor. When the 3-­D geometry is changed into 2-­D honeycomb, a conduction band structure transformation to two types of Dirac cones, one for S-­ and one for P-­orbitals, is observed. The width of the bands depends on the honeycomb period and the coupling between the nanocrystals. Furthermore, there is a dispersionless P-­orbital band, which also forms a landmark of the honeycomb structure. The effects of considerable intrinsic spin-­orbit coupling are briefly considered. For heavy-­element compounds such as CdTe, strong intrinsic spin-­‐orbit coupling opens a non-­trivial gap at the P-­orbital Dirac point, leading to a quantum Spin Hall effect10-­12. Our work shows that well known semiconductor crystals, known for centuries, can lead to systems with entirely new electronic properties, by the simple action of nanogeometry. It can be foreseen that such structures will play a key role in future opto-­electronic applications, provided that they can be fabricated in a straightforward way.