Abstract: Biomethane is a renewable natural gas substitute produced from biogas. Storage of this sustainable energy vector in confined clathrate hydrates, encapsulated in the pores of a host material, is a highly promising avenue to improve storage capacity and energy efficiency. Herein, a new type of periodic mesoporous organosilica (PMO) nanotubes, referred to as hollow ring PMO (HR-PMO), capable of promoting methane clathrate hydrate formation under mild working conditions (273 K, 3.5 MPa) and at high water loading (5.1 g water/g HR-PMO) is reported. Gravimetric uptake measurements reveal a steep single-stepped isotherm and a noticeably high methane storage capacity (0.55 g methane/g HR-PMO; 0.11 g methane/g water at 3.5 MPa). The large working capacity throughout consecutive pressure-induced clathrate hydrate formationdissociation cycles demonstrates the material's excellent recyclability (97% preservation of capacity). Supported by ex situ cryo-electron tomography and x-ray diffraction, HR-PMO nanotubes are hypothesized to promote clathrate hydrate nucleation and growth by distribution and confinement of water in the mesopores of their outer wall, along the central channels of the nanotubes and on the external nanotube surface. These findings showcase the potential for application of organosilica materials with hierarchical and interconnected pore systems for pressure-based storage of biomethane in confined clathrate hydrates.

Abstract: Advanced electron microscopy techniques, including scanning electron microscopes (SEM), scanning transmission electron microscopes (STEM), and transmission electron microscopes (TEM), have revolutionized imaging capabilities. However, achieving high-quality experimental images remains a challenge due to various distortions stemming from the instrumentation and external factors. These distortions, introduced at different stages of imaging, hinder the extraction of reliable quantitative insights. In this paper, we will discuss the main sources of distortion in TEM and S(T)EM images, develop models to describe them, and propose a method to correct these distortions using a convolutional neural network. We validate the effectiveness of our method on a range of simulated and experimental images, demonstrating its ability to significantly enhance the signal-to-noise ratio. This improvement leads to a more reliable extraction of quantitative structural information from the images. In summary, our findings offer a robust framework to enhance the quality of electron microscopy images, which in turn supports progress in structural analysis and quantification in materials science and biology.

Abstract: Uncovering an efficient and stable photocatalytic system for seawater splitting is a highly desirable but challenging goal. Herein, Cd0.2Zn0.8S@Silicalite-1 (CZS@S-1) composites, in which CZS is embedded in the hierarchical zeolite S-1, were prepared and show remarkably high activity, stability and salt resistance in seawater.

Abstract: We investigated the structural stability of perovskite solar cells (PSCs) in n-i-p configuration comprising a rubidium-caesium-methylammonium-formamidinium (Rb-Cs-MA-FA) lead iodide/bromide perovskite absorber, interfaced with nanostructured ZnO-nanorod (NR) or mesostructured (MS) TiO2 electron transfer layers (ETL). An in-situ setup was established comprising synchrotron grazing incidence diffraction (GID) and Raman spectroscopy as a function of temperature under ambient and isothermal conditions; measurements of current-voltage (IV) characteristics and electron microscopic investigations were conducted discretely.The aging of the solar cells was performed at ambient conditions or at elevated temperatures directly in the in -situ measurement setup. The diffraction depth profiling results point to different degradation rates for different ETLs; moreover, electron microscopy and atomic force microscopy, as well as energy dispersive spectroscopy clarified surface conditions in terms of the extent of the degradation. Scanning transmission electron microscopy of lamellas, derived by dual beam microscopy, revealed that the origin of the degradation lay in the ETL/ absorber interface. For the case of the nanostructured zincite, the perovskite absorber contained many voids, leading to the conclusion that the investigated quadruple perovskite absorber showed limited compatibility with ZnO NR ETL due to a higher number of defects. Morphological defects promoted the absorber degradation and nullified the advantages initially achieved by nanostructuring. The exchange of the ZnO NR ETL with MS TiO2 improved the stability parameters of the absorber layer.

Abstract: Lanthanide-doped LiYF4 (Ln:YLF) is commonlyused fora broad variety of optical applications, such as lasing, photon upconversionand optical refrigeration. When synthesized as nanocrystals (NCs),this material is also of interest for biological applications andfundamental physical studies. Until now, it was unclear how Ln:YLFNCs grow from their ionic precursors into tetragonal NCs with a well-defined,bipyramidal shape and uniform dopant distribution. Here, we studythe nucleation and growth of ytterbium-doped LiYF4 (Yb:YLF),as a template for general Ln:YLF NC syntheses. We show that the formationof bipyramidal Yb:YLF NCs is a multistep process starting with theformation of amorphous Yb:YLF spheres. Over time, these spheres growvia Ostwald ripening and crystallize, resulting in bipyramidal Yb:YLFNCs. We further show that prolonged heating of the NCs results inthe degradation of the NCs, observed by the presence of large LiFcubes and small, irregular Yb:YLF NCs. Due to the similarity in chemicalnature of all lanthanide ions our work sheds light on the formationstages of Ln:YLF NCs in general.

Abstract: In this study, we explore the possibility to use deep learning for the reconstruction of phase images from 4D scanning transmission electron microscopy (4D-STEM) data. The process can be divided into two main steps. First, the complex electron wave function is recovered for a convergent beam electron diffraction pattern (CBED) using a convolutional neural network (CNN). Subsequently, a corresponding patch of the phase object is recovered using the phase object approximation. Repeating this for each scan position in a 4D-STEM dataset and combining the patches by complex summation yields the full-phase object. Each patch is recovered from a kernel of 3x3 adjacent CBEDs only, which eliminates common, large memory requirements and enables live processing during an experiment. The machine learning pipeline, data generation, and the reconstruction algorithm are presented. We demonstrate that the CNN can retrieve phase information beyond the aperture angle, enabling super-resolution imaging. The image contrast formation is evaluated showing a dependence on the thickness and atomic column type. Columns containing light and heavy elements can be imaged simultaneously and are distinguishable. The combination of super-resolution, good noise robustness, and intuitive image contrast characteristics makes the approach unique among live imaging methods in 4D-STEM.

Abstract: We studied the plasma-catalytic production of NH3 from H2 and N2 in a dielectric barrier discharge plasma reactor using five different Co-based catalysts supported on Al2O3, namely Co/Al2O3, CoCe/Al2O3, CoLa/Al2O3, CoCeLa/Al2O3 and CoCeMg/Al2O3. The catalysts were characterized via several techniques, including SEM-EDX, and their performance was compared. The best performing catalyst was found to be CoLa/Al2O3, but the dif-ferences in NH3 concentration, energy consumption and production rate between the different catalysts were limited under the same conditions (i.e. feed gas, flow rate and ratio, and applied power). At the same time, the plasma properties, such as the plasma power and current profile, varied significantly depending on the catalyst. Taken together, these findings suggest that in the production of NH3 by plasma catalysis, our catalysts act as plasma modifiers, i.e., they change the discharge properties and hence the gas phase plasma chemistry. Importantly, this effect dominates over the direct catalytic effect (as e.g. in thermal catalysis) defined by the chemistry on the catalyst surface.

Abstract: Atomic layer deposition (ALD) typically employs metal precursors and co-reactant pulses to deposit thin films in a layer-by-layer fashion. While conventional ABAB-type ALD sequences implement only two functionalities, namely, a metal source and ligand exchange agent, additional functionalities have emerged, including etching and reduction agents. Herein, we construct gas-phase sequences-coined as ALD+-with complex-ities reaching beyond the classic ABAB-type ALD by freely combining multiple functionalities within irregular pulse schemes, e.g., ABCADC. The possibilities of such combinations are explored as a smart strategy to tailor bimetallic thin films and nanoparticle (NP) properties. By doing so, we demonstrate that bimetallic thin films can be tailored with target thickness and through the full compositional range, while the morphology can be flexibly modulated from thin films to NPs by shuI 1ing the pulse sequence. These complex pulse schemes are expected to be broadly applicable but are here explored for Pd-Ru bimetallic thin films and NPs.

Abstract: Gold nanorods (Au NRs) are an exceptionally promising tool in nanotechnology due to three key factors: (i) their strong interaction with electromagnetic radiation, stemming from their plasmonic nature, (ii) the ease with which the resonance frequency of their longitudinal plasmon mode can be tuned from the visible to the near-infrared region of the electromagnetic spect r u m based on their aspect ratio, and (iii) their simple and cost-effective preparation through seed-mediated chemical growth. In this synthetic method, surfactants play a critical role in controlling the size, shape, and colloidal stabi l i t y of Au NRs. For example, surfactants can stabilize specific crystallographic facets during the formation of Au NRs, leading to t h e formation of NRs with specific morphologies. The process of surfactant adsorption onto the NR surface may result in various assemblies of surfactant molecules, such as spherical micelles, elongated micelles, or bilayers. Again, the assembly mode is critical toward determining the further availabi l i t y of the Au NR surface to the surrounding medium. Despite its importance and a great deal of research effort, the interaction between Au NPs and surfactants remains insufficiently understood, because the assembly process is influenced by numerous factors, including the chemical nature of the surfactant, the surface morphology of Au NPs, and solution parameters. Therefore, gaining a more comprehensive understanding of these interactions is essential to unlock the full potential of the seed-mediated growth method and the applications of plasmonic NPs. A plethora of characterization techniques have been applied to reach such an understanding , but many open questions remain. In this Account, we review the current knowledge on the interactions between surfactants and Au NRs. We briefly introduce the state-of-the-art methods for synthesizing Au NRs and highlight the crucial role of cationic surfactants during this process. The self-assembly and organization of surfactants on the Au NR surface is then discussed to better understand their role in seed-mediated growth. Subsequently, we provide examples and elucidate how chemical additives can be used to modulate micellar assemblies, in turn allowing for a finer control over the growth of Au NRs, including chiral NRs. Next, we review the main experimental characterization and computational modeling techniques that have been applied to shed light on the arrangement of surfactants on Au NRs and summarize the advantages and disadvantages for each technique. The Account ends with a “Conclusions and Outlook” section, outlining promising future research directions and developments that we consider are sti l l required, mostly related to the application of electron microscopy in liquid and in 3D. Finally, we remark on the potential of exploiting machine learning techniques to predict synthetic routes for NPs with predefined structures and properties.

Abstract: CsPbI3 is a promising perovskite material for photovoltaic applications in its photoactive perovskite or black phase. However, the material degrades to a photovoltaically inactive or yellow phase at room temperature. Various mitigation strategies are currently being developed to increase the lifetime of the black phase, many of which rely on inducing strains in the material that hinder the black-to-yellow phase transition. Physical insight into how these strategies exactly induce strain as well as knowledge of the spatial extent over which these strains impact the material is crucial to optimize these approaches but is still lacking. Herein, we combine machine learning potential-based molecular dynamics simulations with our in silico strain engineering approach to accurately quantify strained large-scale atomic structures on a nanosecond time scale. To this end, we first model the strain fields introduced by atomic substitutions as they form the most elementary strain sources. We demonstrate that the magnitude of the induced strain fields decays exponentially with the distance from the strain source, following a decay rate that is largely independent of the specific substitution. Second, we show that the total strain field induced by multiple strain sources can be predicted to an excellent approximation by summing the strain fields of each individual source. Finally, through a case study, we illustrate how this additive character allows us to explain how complex strain fields, induced by spatially extended strain sources, can be predicted by adequately combining the strain fields caused by local strain sources. Hence, the strain additivity proposed here can be adopted to further our insight into the complex strain behavior in perovskites and to design strain from the atomic level onward to enhance their sought-after phase stability.

Abstract: To understand the structure–property relationship of nanostructures, reliably quantifying parameters, such as the number of atoms along the projection direction, is important. Advanced statistical methodologies have made it possible to count the number of atoms for monotype crystalline nanoparticles from a single ADF STEM image. Recent developments enable one to simultaneously acquire multiple ADF STEM images. Here, we present an extended statistics-based method for atom counting from a combination of multiple statistically independent ADF STEM images reconstructed from non-overlapping annular detector collection regions which improves the accuracy and allows one to retrieve precise atom-counts, especially for images acquired with low electron doses and multiple element structures.

Abstract: The dehydrogenation of long-chain alkanes to olefins and alkylaromatics is a challenging endothermic reaction, typically requiring harsh conditions which can lead to low selectivity and coking. More favorable thermodynamics can be achieved by using a hydrogen acceptor, such as ethylene. In this work, the potential of heterogeneous platinum catalysts for the transfer dehydrogenation of long-chain alkanes is investigated, using ethylene as a convenient hydrogen acceptor. Pt/C and Pt–Sn/C catalysts were prepared<italic>via</italic>a simple polyol method and characterized with CO pulse chemisorption, HAADF-STEM, and EDX measurements. Conversion of ethylene was monitored<italic>via</italic>gas-phase FTIR, and distribution of liquid products was analyzed<italic>via</italic>GC-FID, GC-MS, and 1H-NMR. Compared to unpromoted Pt/C, Sn-promoted catalysts show lower initial reaction rates, but better resistance to catalyst deactivation, while increasing selectivity towards alkylaromatics. Both reaction products and ethylene were found to inhibit the reaction significantly. At 250 °C for 22 h, TON up to 28 and 86 mol per mol Pt were obtained for Pt/C and PtSn₂/C, respectively, with olefin selectivities of 94% and 53%. The remaining products were mainly unbranched alkylaromatics. These findings show the potential of simple heterogeneous catalysts in alkane transfer dehydrogenation, for the preparation of valuable olefins and alkylaromatics, or as an essential step in various tandem reactions.

Abstract: In this study, the multi-layer self-assembled molecular network formation of an alkylated tetrathiafulvalene compound is studied at the liquid-solid interface between 1-phenyloctane and graphite. A combined theoretical/experimental approach associating force-field and quantum-chemical calculations with scanning tunnelling microscopy is used to determine the two-dimensional self-assembly beyond the monolayer, but also to further the understanding of the molecular adsorption conformation and its impact on the molecular packing within the assemblies at the monolayer and bilayer level.

Abstract: In this paper, a methodology is presented to count the number of atoms in heterogeneous nanoparticles based on the combination of multiple annular dark field scanning transmission electron microscopy (ADF STEM) images. The different non-overlapping annular detector collection regions are selected based on the principles of optimal statistical experiment design for the atom-counting problem. To count the number of atoms, the total intensities of scattered electrons for each atomic column, the so-called scattering cross-sections, are simultaneously compared with simulated library values for the different detector regions by minimising the squared differences. The performance of the method is evaluated for simulated Ni@Pt and Au@Ag core-shell nanoparticles. Our approach turns out to be a dose efficient alternative for the investigation of beam-sensitive heterogeneous materials as compared to the combination of ADF STEM and energy dispersive X-ray spectroscopy.

Abstract: Metal nanocrystals (NCs) display unique physicochemical features that are highly dependent on nanoparticle dimensions, anisotropy, structure, and composition. The development of synthesis methodologies that allow us to tune such parameters finely emerges as crucial for the application of metal NCs in catalysis, optical materials, or biomedicine. Here, we describe a synthetic methodology to fabricate hollow multimetallic heterostructures using a combination of seed-mediated growth routes and femtosecond-pulsed laser irradiation. The envisaged methodology relies on the coreduction of Ag and Pd ions on gold nanorods (Au NRs) to form Au@PdAg core-shell nanostructures containing small cavities at the Au-PdAg interface. The excitation of Au@PdAg NRs with low fluence femtosecond pulses was employed to induce the coalescence and growth of large cavities, forming multihollow anisotropic Au@PdAg nanostructures. Moreover, single-hollow alloy AuPdAg could be achieved in high yield by increasing the irradiation energy. Advanced electron microscopy techniques, energy-dispersive X-ray spectroscopy (EDX) tomography, X-ray absorption near-edge structure (XANES) spectroscopy, and finite differences in the time domain (FDTD) simulations allowed us to characterize the morphology, structure, and elemental distribution of the irradiated NCs in detail. The ability of the reported synthesis route to fabricate multimetallic NCs with unprecedented hollow nanostructures offers attractive prospects for the fabrication of tailored high-entropy alloy nanoparticles.