Abstract: As part of a mitigation and adaptation approach to increasing carbon dioxide atmospheric concentrations, we report superior performance of various metal-nitrogen-doped carbon catalysts, synthesized using an easily up-scalable method, for the electrochemical reduction to carbon monoxide and/or formate at industrially relevant current densities up to 200 mAcm−2. Altering the embedded transition metal (i.e. Sn, Co, Fe, Mn and Ni) allowed to tune the selectivity towards the desired product. Mn-N-C and Fe-N-C performance was compromised by its high CO* binding energy, while Co-N-C catalyzed preferentially the HER. Ni-N-C and Sn-N-C revealed to be promising electrocatalysts, the latter being evaluated for the first time in a flow reactor. A productivity of 589 L CO m-2 h-1 at -1.39 VRHE with Ni-N-C and 751 g HCOO- m-2 h-1 at -1.47 VRHE with Sn-N-C was achieved with no signs of degradation detected after 24 h of operation at industrially relevant current densities (100 mAcm−2). Stable operation at 200 mAcm−2 led to turnover frequencies for the production of carbon products of up to 5176 h-1. These enhanced productivities, in combination with high stability, constitute an essential step towards the scalability and ultimately towards the economical valorization of CO2 electrolyzers using metal-containing nitrogen-doped catalysts.

Abstract: This chapter provides an overview of statistical and quantitative methodologies that have pushed (scanning) transmission electron microscopy ((S)TEM) toward accurate and precise measurements of unknown structure parameters for understanding the relation between the structure of a material and its properties. Hereby, statistical parameter estimation theory has extensively been used which enabled not only measuring atomic column positions, but also quantifying the number of atoms, and detecting atomic columns as accurately and precisely as possible from experimental images. As a general conclusion, it can be stated that advanced statistical techniques are ideal tools to perform quantitative electron microscopy at the atomic scale. In the future, statistical methods will continue to be developed and novel quantification procedures will open up new possibilities for studying material structures at the atomic scale.

Abstract: Polarization fatigue, i.e., the loss of polarization of ferroelectric capacitors upon field cycling, has been widely discussed as an interface related effect. However, mechanism(s) behind the development of fatigue have not been fully identified. Here, we study the fatigue mechanisms in Pt/PbZr0.52Ti0.48O3/SrRuO3 (Pt/PZT/SRO) capacitors in which all layers are fabricated by pulsed laser deposition without breaking the vacuum. With scanning transmission electron microscopy, we observed that in the fatigued capacitor, the Pt/PZT interface becomes structurally degraded, forming a 5 nm-10 nm thick non-ferroelectric layer of crystalline ZrO2 and diffused Pt grains. We then found that the fatigued capacitors can regain the full initial polarization switching if the externally applied field is increased to at least 10 times the switching field of the pristine capacitor. These findings suggest that polarization fatigue is driven by a two-step mechanism. First, the transient depolarization field that repeatedly appears during the domain switching under field cycling causes decomposition of the metal/ferroelectric interface, resulting in a non-ferroelectric degraded layer. Second, this interfacial non-ferroelectric layer screens the external applied field causing an increase in the coercive field beyond the usually applied maximum field and consequently suppresses the polarization switching in the cycled capacitor. Our work clearly confirms the key role of the electrode/ferroelectric interface in the endurance of ferroelectric-based devices.

Abstract: In this chapter, the principles of detection theory are used to quantify the probability of error for atom counting from high-resolution scanning transmission electron microscopy (HRSTEM) images. Binary and multiple hypothesis testing have been investigated in order to determine the limits to the precision with which the number of atoms in a projected atomic column can be estimated. The probability of error has been calculated when using STEM images, scattering cross-sections or peak intensities as a criterion to count atoms. Based on this analysis, we conclude that scattering cross-sections perform almost equally well as images and perform better than peak intensities. Furthermore, the optimal STEM detector design can be derived for atom counting using the expression of the probability of error. We show that for very thin objects the low-angle annular dark-field (LAADF) regime is optimal and that for thicker objects the optimal inner detector angle increases.

Abstract: Functionalization of photocatalytic metal oxide nanoparticles of TiO 2 , ZnO, WO 3 and CuO with amine‐terminated (oleylamine) and thiol‐terminated (1‐dodecanethiol) alkyl chained ligands was studied under ambient conditions. A high selectivity was observed in the binding specificity of a ligand towards nanoparticles of these different oxides. It was observed that oleylamine binds stably to only TiO 2 and WO 3 , while 1‐dodecanethiol binds stably only to ZnO and CuO. Similarly, polar to non‐polar solvent phase transfer of TiO 2 and WO 3 nanoparticles could be achieved by using oleylamine, but not by 1‐dodecanethiol, while the contrary holds for ZnO and CuO. The surface chemistry of ligand functionalized nanoparticles was probed by ATR‐FTIR spectroscopy, that enabled to elucidate the occupation of the ligands at the active sites. The photo‐stability of the ligands on the nanoparticle surface was determined by the photocatalytic self‐cleaning properties of the material. While TiO 2 and WO 3 degrade the ligands within 24 hours under both UV and visible light, ligands on ZnO and CuO remain unaffected. The gathered insights are also highly relevant from an application point of view. As an example, since the ligand functionalized nanoparticles are hydrophobic in nature, they can thus be self‐assembled at the air‐water interface, for obtaining nanoparticle films with demonstrated photocatalytic as well as anti‐fogging properties.

Abstract: Carbon black (CB) supported Ni, Fe, or Fe-Ni alloy catalysts were synthesized by sol-gel to elucidate the reaction pathways over each catalyst, as well as synergistic effects in glucose to sorbitol hydrogenation. The bimetallic materials presented small and alloyed nanoparticles that were richer in reduced metallic sites at the surface than their monometallic counterparts. Glucose isomerization to fructose was favoured over Fe/CB, while glucose hydrogenation to sorbitol is the dominating pathway over Ni/CB catalyst. By contrast, sorbitol production was promoted and undesired isomerization was suppressed when Fe and Ni formed a nanoalloy. In addition, the alloy catalyst presented better stability than the corresponding monometallic catalyst. A comparison with a mechanical mixture of Fe/CB and Ni/CB monometallic catalysts demonstrated the synergy at the nanoscale in the alloy. By comparing different Fe:Ni ratios, the 1:1 formulation was identified as the best compromise to achieve a high activity while maintaining high sorbitol selectivity.

Abstract: Tailoring heat treatments for Laser Powder Bed Fusion (LPBF) processed materials is critical to ensure superior and repeatable material properties for high-end applications. This tailoring requires in-depth understanding of the LPBF-processed material. Therefore, the current study aims at unravelling the threefold interrelationship between the process (LPBF and heat treatment), the microstructure at different scales (macro-, meso-, micro-, and nano-scale), and the macroscopic material properties of AlSi10Mg. A similar solidification trajectory applies at different length scales when comparing the solidification of AlSi10Mg, ranging from mould-casting to rapid solidification (LPBF). The similarity in solidification trajectories triggers the reason why the Brody-Flemings cellular microsegregation solidification model could predict the cellular morphology of the LPBF as-printed microstructure. Where rapid solidification occurs at a much finer scale, the LPBF microstructure exhibits a significant grain refinement and a high degree of silicon (Si) supersaturation. This study has identified the grain refinement and Si supersaturation as critical assets of the as-printed microstructure, playing a vital role in achieving superior mechanical and thermal properties during heat treatment. Next, an electrical conductivity model could accurately predict the Si solute concentration in LPBF-processed and heat-treated AlSi10Mg and allows understanding the microstructural evolution during heat treatment. The LPBF-processed and heat-treated AlSi10Mg conditions (as-built (AB), direct-aged (DA), stress-relieved (SR), preheated (PH)) show an interesting range of superior mechanical properties (tensile strength: 300-450 MPa, elongation: 4-13%) compared to the mould-cast T6 reference condition.

Abstract: Single-atom catalysts (SACs) have recently attracted broad scientific interests due to their unique structural feature, the single-atom dispersion. Optimized electronic structure as well as high stability are required for single-atom catalysts to enable efficient electrochemical production of H2O2. Herein, we report a facile synthesis method that stabilizes atomic Pd species on the reduced graphene oxide/Ndoped carbon hollow carbon nanospheres (Pd1/N-C). Pd1/N-C exhibited remarkable electrochemical H2O2 production rate with high faradaic efficiency, reaching 80%. The single-atom structure and its high H2O2 production rate were maintained even after 10,000 cycle stability test. The existence of single-atom Pd as well as its coordination with N species is responsible for its high activity, selectivity, and stability. The N coordination number and substrate doping around Pd atoms are found to be critical for an optimized adsorption energy of intermediate *OOH, resulting in efficient electrochemical H2O2 production. (C) 2020 Elsevier Inc. All rights reserved.

Abstract: The combination of energy-dispersive X-ray spectroscopy (EDX) and electron tomography is a powerful approach to retrieve the 3D elemental distribution in nanomaterials, providing an unprecedented level of information for complex, multi-component systems, such as semiconductor devices, as well as catalytic and plasmonic nanoparticles. Unfortunately, the applicability of EDX tomography is severely limited because of extremely long acquisition times and high electron irradiation doses required to obtain 3D EDX reconstructions with an adequate signal-to-noise ratio. One possibility to address this limitation is intelligent denoising of experimental data using prior expectations about the objects of interest. Herein, this approach is followed using the deep learning methodology, which currently demonstrates state-of-the-art performance for an increasing number of data processing problems. Design choices for the denoising approach and training data are discussed with a focus on nanoparticle-like objects and extremely noisy signals typical for EDX experiments. Quantitative analysis of the proposed method demonstrates its significantly enhanced performance in comparison to classical denoising approaches. This allows for improving the tradeoff between the reconstruction quality, acquisition time and radiation dose for EDX tomography. The proposed method is therefore especially beneficial for the 3D EDX investigation of electron beam-sensitive materials and studies of nanoparticle transformations.

Abstract: The (photo)catalytic properties of metal–organic frameworks (MOFs) can be enhanced by post-synthetic inclusion of metallic species in their porosity. Due to their extraordinarily high surface area and well defined porous structure, MOFs can be used for the stabilization of metal nanoparticles with adjustable size within their porosity. Originally, we present here an optimized ultrafast photoreduction protocol for the<italic>in situ</italic>synthesis of tiny and monodisperse silver nanoclusters (AgNCs) homogeneously supported on a photoactive porous titanium carboxylate MIL-125-NH₂MOF. The strong metal–framework interaction between –NH₂and Ag atoms influences the AgNC growth, leading to the surfactant-free efficient catalyst AgNC@MIL-125-NH₂with improved visible light absorption. The potential use of AgNC@MIL-125-NH₂was further tested in challenging applications: (i) the photodegradation of the emerging organic contaminants (EOCs) methylene blue (MB-dye) and sulfamethazine (SMT-antibiotic) in water treatment, and (ii) the catalytic hydrogenation of<italic>p</italic>-nitroaniline (4-NA) to<italic>p</italic>-phenylenediamine (PPD) with industrial interest. It is noteworthy that compared with the pristine MIL-125-NH₂, the composite presents an improved catalytic activity and stability, being able to photodegrade 92% of MB in 60 min and 96% of SMT in 30 min, and transform 100% of 4-NA to PPD in 30 min. Aside from these very good results, this study describes for the first time the use of a MOF in a visible light continuous flow reactor for wastewater treatment. With only 10 mg of AgNC@MIL-125-NH₂, high SMT removal efficiency over 70% is maintained after >2 h under water flow conditions found in real wastewater treatment plants, signaling a future real application of MOFs in water remediation.

Abstract: Environmental conditions during real-world application of bimetallic core–shell nanoparticles (NPs) often include the use of elevated temperatures, which are known to cause elemental redistribution, in turn significantly altering the properties of these nanomaterials. Therefore, a thorough understanding of such processes is of great importance. The recently developed combination of fast electron tomography with in situ heating holders is a powerful approach to investigate heat-induced processes at the single NP level, with high spatial resolution in 3D. In combination with 3D finite-difference diffusion simulations, this method can be used to disclose the influence of various NP parameters on the diffusion dynamics in Au@Ag core–shell systems. A detailed study of the influence of heating on atomic diffusion and alloying for Au@Ag NPs with varying core morphology and crystallographic details is carried out. Whereas the core shape and aspect ratio of the NPs play a minor role, twin boundaries are found to have a strong influence on the elemental diffusion.

Abstract: A small sample tilt away from a main zone axis orientation results in an elongation of the atomic columns in ADF STEM images. An often posed research question is therefore whether the ADF STEM image intensities of tilted nanomaterials should be quantified using a parametric imaging model consisting of elliptical rather than the currently used symmetrical peaks. To this purpose, simulated ADF STEM images corresponding to different amounts of sample tilt are studied using a parametric imaging model that consists of superimposed 2D elliptical Gaussian peaks on the one hand and symmetrical Gaussian peaks on the other hand. We investigate the quantification of structural parameters such as atomic column positions and scattering cross sections using both parametric imaging models. In this manner, we quantitatively study what can be gained from this elliptical model for quantitative ADF STEM, despite the increased parameter space and computational effort. Although a qualitative improvement can be achieved, no significant quantitative improvement in the estimated structure parameters is achieved by the elliptical model as compared to the symmetrical model. The decrease in scattering cross sections with increasing sample tilt is even identical for both types of parametric imaging models. This impedes direct comparison with zone axis image simulations. Nonetheless, we demonstrate how reliable atom-counting can still be achieved in the presence of small sample tilt.

Abstract: Strain engineering in semiconductor transistor devices has become vital in the semiconductor industry due to the ever-increasing need for performance enhancement at the nanoscale. Raman spectroscopy is a non-invasive measurement technique with high sensitivity to mechanical stress that does not require any special sample preparation procedures in comparison to characterization involving transmission electron microscopy (TEM), making it suitable for inline strain measurement in the semiconductor industry. Indeed, at present, strain measurements using Raman spectroscopy are already routinely carried out in semiconductor devices as it is cost effective, fast and non-destructive. In this paper we explore the usage of linearized radially polarized light as an excitation source, which does provide significantly enhanced accuracy and precision as compared to linearly polarized light for this application. Numerical simulations are done to quantitatively evaluate the electric field intensities that contribute to this enhanced sensitivity. We benchmark the experimental results against TEM diffraction-based techniques like nano-beam diffraction and Bessel diffraction. Differences between both approaches are assigned to strain relaxation due to sample thinning required in TEM setups, demonstrating the benefit of Raman for nondestructive inline testing.

Abstract: Determining the three-dimensional (3D) atomic structure of nanoparticles (NPs) is critical to understand their structure-dependent properties. It is hereby important to perform such analyses under conditions relevant for the envisioned application. Here, we investigate the 3D structure of supported Au NPs at high temperature, which is of importance to understand their behavior during catalytic reactions. To overcome limitations related to conventional high-resolution electron tomography at high temperature, 3D characterization of NPs with atomic resolution has been performed by applying atom-counting using atomic resolution annular darkfield scanning transmission electron microscopy (ADF STEM) images followed by structural relaxation. However, at high temperatures, thermal displacements, which affect the ADF STEM intensities, should be taken into account. Moreover, it is very likely that the structure of a NP investigated at elevated temperature deviates from a ground state configuration, which is difficult to determine using purely computational energy minimization approaches. In this paper, we therefore propose an optimized approach using an iterative local minima search algorithm followed by molecular dynamics (MD) structural relaxation of candidate structures associated with each local minimum. In this manner, it becomes possible to investigate the 3D atomic structure of supported NPs, which may deviate from their ground state configuration.