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.

Abstract: Being confronted with increasing and expanding urbanisation and the loss of natural green spaces, our living environment is threatened more and more by the effects of global climate change. Green infrastructure is often thought of as the solution to increase climate resilience and reinforce the quality of the lived environment simultaneously. While the benefits, or ecosystem services, that are generated through green infrastructure have been studied intensively, forces that influence green infrastructure decision-making have been far less subjected to thorough research. In this study a discrete choice experiment was conducted with local decision makers in Flemish municipalities to reveal crucial factors in the decision process applied to green infrastructure projects. Flanders is one of the most densely built regions in Europe, stressing the urgency to understand local spatial decision factors to guarantee green space. 568 decision makers active in the local administration of 235 Flemish municipalities participated in the experiment, set in a hypothetical neighbourhood park. Every choice alternative exists of five attributes: investment cost, maintenance cost, deferred investment, recreational value, and climate impact. We find that barriers hampering Flemish munipalities' GI implementation, differ over size of the municipality: smallers municipalities are more affected by knowledge gaps, while larger municipalities are experiencing prioritization issues. Results from hierarchical Bayes choice models indicate that municipal decisions on green infrastructure are highly – almost solely -cost-driven, rarely consider the full range of benefits, and centre around short-term and immediate arguments. Moreover, interaction models reveal that a municipalities' financial result is a key determinant of its willingness to invest in public greening and consider long term benefits, suggesting that GI is a luxury good. The results expose some of the heuristics in GI decision making and can be used to inform higher authorities on ways to overcome barriers towards informed decision-making and to facilitate GI investment.

Abstract: Governments and international organizations are increasingly determined to create more sustainable food value chains (SFVCs). However, only little empirical evidence is available on how SFVCs are understood. Enquiring African food value chain actors allows gathering valuable insights into their perception of sustainability, which characteristics of sustainable food value chains they prioritize, and which obstacles to a sustainable transformation they identify. By means of a Q-methodology involving interviews with 33 Kenyan respondents, four perspectives were distinguished. The first perspective, “economic productivity and growth,” prioritizes economic growth and has only limited attention to the social dimension of sustainability. The second perspective, “food security and food availability,” believes that ensuring food security should be the key goal of SFVCs. The third perspective, “environment first,” is dedicated to the environmental dimension of sustainability; the perspective implies that protecting natural resources is the primary way to sustain this level of production. The fourth perspective, “transformative knowledge,” entails that by innovating and sharing knowledge, food value chains can become more sustainable in different areas. Overall, this study provides reliable insights into how Kenyan food value chain actors perceive sustainability in their sector and which elements they believe should be prioritized when rethinking food systems. The study results are valuable for policy-making to further define an SFVC in Kenya and to pave the way for a sustainable transformation of the food sector in developing countries.

Abstract: Climate change impact, food security concerns, and greenhouse gas emissions are pressuring agricultural production systems in developing countries. There is a need for a shift toward sustainable food systems. One of the concepts introduced to drive this shift is climate-smart agriculture (CSA), endorsed by international organizations to address multifaceted challenges. Despite widespread attention and support, the adoption of CSA among African farmers remains low. This systematic literature review aims to shed light on the factors influencing CSA adoption amongst African farmers. Within the articles identified as relevant, over 50 CSA practices and more than 40 factors influencing CSA adoption were distinguished. These influencing factors can be categorized as personal, farm- related, financial, environmental, and informational. The focus of this review is to identify and explain the overall impact (positive, negative, or mixed) of these factors on CSA adoption. Overall, many factors result in mixed effects, only some factors have an unambiguous positive or negative effect on CSA adoption. For instance, educational level emerges as a key personal factor, positively impacting CSA adoption, along with positive influences from farmers’ experience and farm size among farm-related factors. Financial factors reveal distinct patterns, with income from farming and access to credit positively influencing adoption, while off-farm income exhibits a negative effect. Environmental factors, though less researched, indicate positive impacts related to changes in rainfall patterns, temperature, and droughts. Lastly, informational factors consistently exhibit a positive effect on CSA adoption, with training, access to extension, group memberships, climate information, and CSA awareness playing crucial roles. These findings provide valuable insights for policymakers seeking to enhance CSA adoption in Africa, offering a nuanced understanding of the multifaceted dynamics at play.