Abstract: This dataset contains individual EEL and EDX events where for every event (electron or X-ray), their energy and time of arrival is stored. The experiment was performed in a transmission electron microscope (Tecnai Osiris) at 200 keV. The material investigated is an Al-Mg-Si-Cu alloy. The 'full_dataset.mat' contains the full dataset and the 'subset.mat' has the first five frames of the full dataset. The attached 'EELS-EDX.ipynb' is a jupyter notebook file. This file describes the data processing in order to observe the temporal correlation between the electrons and X-rays.

Abstract: Carbon dioxide electrochemical reduction (CO2ER) has attracted considerable attention as a technology to recycle CO2 into raw materials for chemicals using renewable energies. We recently found that Zn-Al layered double hydroxides (Zn-Al LDH) have the CO-forming CO2ER activity. However, the activity was only evaluated by using the liquid-phase CO2ER. In this study, Ni-Al and Ni-Fe LDHs as well as Zn-Al LDH were synthesized using a facile coprecipitation process and the gas-phase CO2ER with the LDH-loaded gas-diffusion electrode (GDE) was examined. The products were characterized by XRD, STEM-EDX, BF-TEM and ATR-IR spectroscopy. In the ATR-IR results, the interaction of CO2 with Zn-Al LDH showed a different carbonates evolution with respect to other LDHs, suggesting a different electrocatalytic activity. The LDH-loaded GDE was prepared by simple drop-casting of a catalyst ink onto carbon paper. For gas-phase CO2ER, only Zn-Al LDH exhibited the CO2ER activity for carbon monoxide (CO) formation. By using different potassium salt electrolytes affording neutral to strongly basic conditions, such as KCl, KHCO3 and KOH, the gas-phase CO2ER with Zn-Al LDH-loaded GDE showed 1.3 to 2.1 times higher partial current density for CO formation than the liquid-phase CO2ER.

Abstract: Inelastic excitation as exploited in Electron Energy Loss Spectroscopy (EELS) contains a rich source of information that is revealed in the scattering process. To accurately quantify core-loss EELS, it is common practice to fit the observed spectrum with scattering cross-sections calculated using experimental parameters and a Generalized Oscillator Strength (GOS) database [1].   The GOS is computed using Fermi’s Golden Rule and orbitals of bound and excited states. Previously, the GOS was based on Hartree-Fock solutions [2], but more recently Density Functional Theory (DFT) has been used [3]. In this work, we have chosen to use the Dirac equation to incorporate relativistic effects and have performed calculations using Flexible Atomic Code (FAC) [4]. This repository contains a tabulated GOS database based on Dirac solutions for computing double differential cross-sections under experimental conditions.   We hope the Dirac-based GOS database can benefit the EELS community for both academic use and industry integration.   Database Details: – Covers all elements (Z: 1-108) and all edges – Large energy range: 0.01 – 4000 eV – Large momentum range: 0.05 -50 Å-1 – Fine log sampling: 128 points for energy and 256 points for momentum – Data format: GOSH [3]   Calculation Details: – Single atoms only; solid-state effects are not considered – Unoccupied states before continuum states of ionization are not considered; no fine structure – Plane Wave Born Approximation – Frozen Core Approximation is employed; electrostatic potential remains unchanged for orthogonal states when – core-shell electron is excited – Self-consistent Dirac–Fock–Slater iteration is used for Dirac calculations; Local Density Approximation is assumed for electron exchange interactions; continuum states are normalized against asymptotic form at large distances – Both large and small component contributions of Dirac solutions are included in GOS – Final state contributions are included until the contribution of the previous three states falls below 0.1%. A convergence log is provided for reference.   Version 1.1 release note: – Update to be consistent with GOSH data format [3], all the edges are now within a single hdf5 file. A notable change in particular, the sampling in momentum is in 1/m, instead of previously in 1/Å. Great thanks to Gulio Guzzinati for his suggestions and sending conversion script.  Version 1.2 release note: – Add “File Type / File version” information [1] Verbeeck, J., and S. Van Aert. Ultramicroscopy 101.2-4 (2004): 207-224. [2] Leapman, R. D., P. Rez, and D. F. Mayers. The Journal of Chemical Physics 72.2 (1980): 1232-1243. [3] Segger, L, Guzzinati, G, & Kohl, H. Zenodo (2023). doi:10.5281/zenodo.7645765 [4] Gu, M. F. Canadian Journal of Physics 86(5) (2008): 675-689.

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.