Abstract: A novel class of nanostructured Zr50Cu50 (%at.) metallic glass films with superior and tunable mechanical
properties is produced by pulsed laser deposition. The process can be controlled to synthetize a wide
range of film microstructures including dense fully amorphous, amorphous embedded with nanocrystals
and amorphous nano-granular. A unique dense self-assembled nano-laminated atomic arrangement
characterized by alternating Cu-rich and Zr/O-rich nanolayers with different local chemical enrichment
and amorphous or amorphous-crystalline composite nanostructure has been discovered, while
significant in-plane clustering is reported for films synthetized at high deposition pressures. This unique
nanoarchitecture is at the basis of superior mechanical properties including large hardness and elastic
modulus up to 10 and 140 GPa, respectively and outstanding total elongation to failure (>9%), leading to
excellent strength/ductility balance, which can be tuned by playing with the film architecture. These
results pave the way to the synthesis of novel class of engineered nanostructured metallic glass films
with high structural performances attractive for a number of applications in microelectronics and
coating industry.

Abstract: In this chapter, a statistical model-based method to count the number of atoms of monotype crystalline nanostructures from high-resolution annular dark-field (ADF) scanning transmission electron microscopy (STEM) images is discussed in detail together with a thorough study on the possibilities and inherent limitations. We show that this method can be applied to nanocrystals of arbitrary shape, size, and atom type. The validity of the atom-counting results is confirmed by means of detailed image simulations and it is shown that the high sensitivity of our method enables us to count atoms with single atom sensitivity.

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: The maximum a posteriori (MAP) probability rule for atom column detection can also be used as a tool to evaluate the relation between scanning transmission electron microscopy (STEM) image quality and atom detectability. In this chapter, a new image-quality measure is proposed that correlates well with atom detectability, namely the integrated contrast-to-noise ratio (ICNR). Furthermore, the working principle of the MAP probability rule is described in detail showing a close relation to the principles of model-selection methods.

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: In this chapter, the principles of statistical parameter estimation theory for a quantitative analysis of atomic-resolution electron microscopy images are introduced. Within this framework, electron microscopy images are described by a parametric statistical model. Here, parametric models are introduced for different types of electron microscopy images: reconstructed exit waves, annular dark-field (ADF) scanning transmission electron microscopy (STEM) images, and simultaneously acquired ADF and annular bright-field (ABF) STEM images. Furthermore, the Cramér-Rao lower bound (CRLB) is introduced, i.e. a theoretical lower bound on the variance of any unbiased estimator. This CRLB is used to quantify the precision of the structure parameters of interest, such as the atomic column positions and the integrated atomic column intensities.

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: The highly energetic electrons in a transmission electron microscope (TEM) can alter or even completely destroy the structure of samples before sufficient information can be obtained. This is especially problematic in the case of zeolites, organic and biological materials. As this effect depends on both the electron beam and the sample and can involve multiple damage pathways, its study remained difficult and is plagued with irreproducibility issues, circumstantial evidence, rumors, and a general lack of solid data. Here we take on the experimental challenge to investigate the role of the STEM scan pattern on the damage behavior of a commercially available zeolite sample with the clear aim to make our observations as reproducible as possible. We make use of a freely programmable scan engine that gives full control over the tempospatial distribution of the electron probe on the sample and we use its flexibility to obtain multiple repeated experiments under identical conditions comparing the difference in beam damage between a conventional raster scan pattern and a newly proposed interleaved scan pattern that provides exactly the same dose and dose rate and visits exactly the same scan points. We observe a significant difference in beam damage for both patterns with up to 11 % reduction in damage (measured from mass loss). These observations demonstrate without doubt that electron dose, dose rate and acceleration voltage are not the only parameters affecting beam damage in (S)TEM experiments and invite the community to rethink beam damage as an unavoidable consequence of applied electron dose.

Abstract: Recent advances in the development of electron and X-ray detectors have opened up the possibility to detect single events from which its time of arrival can be determined with nanosecond resolution. This allows observing time correlations between electrons and X-rays in the transmission electron microscope. In this work, a novel setup is described which measures individual events using a silicon drift detector and digital pulse processor for the X-rays and a Timepix3 detector for the electrons. This setup enables recording time correlation between both event streams while at the same time preserving the complete conventional electron energy loss (EELS) and energy dispersive X-ray (EDX) signal. We show that the added coincidence information improves the sensitivity for detecting trace elements in a matrix as compared to conventional EELS and EDX. Furthermore, the method allows the determination of the collection efficiencies without the use of a reference sample and can subtract the background signal for EELS and EDX without any prior knowledge of the background shape and without pre-edge fitting region. We discuss limitations in time resolution arising due to specificities of the silicon drift detector and discuss ways to further improve this aspect.

Abstract: Dry powder coating is an effective approach to protect the surfaces of layered cathode active materials (CAMs) in lithium-ion batteries. Previous investigations indicate an incorporation of lithium ions in fumed Al2O3, ZrO2, and TiO2 coatings on LiNi0.7Mn0.15Co0.15O2 during cycling, improving the cycling performance. Here, this coating approach is transferred for the first time to fumed ternary LiAlO2, Li4Zr3O8, and Li4Ti5O12 and directly compared with their lithium-free equivalents. All materials could be processed equally and their nanostructured small aggregates accumulate on the CAM surfaces to quite homogeneous coating layers with a certain porosity. The LiNixMnyCozO2 (NMC) coated with lithium-containing materials shows an enhanced improvement in overall capacity, capacity retention, rate performance, and polarization behavior during cycling, compared to their lithium-free analogues. The highest rate performance was achieved with the fumed ZrO2 coating, while the best long-term cycling stability with the highest absolute capacity was obtained for the fumed LiAlO2-coated NMC. The optimal coating agent for NMC to achieve a balanced system is fumed Li4Ti5O12, providing a good compromise between high rate capability and good capacity retention. The coating agents prevent CAM particle cracking and degradation in the order LiAlO2 ≈ Al2O3 > Li4Ti5O12 > Li4Zr3O8 > ZrO2 > TiO2. A schematic model for the protection and electrochemical performance enhancement of high-nickel NMC with fumed metal oxide coatings is sketched. It becomes apparent that physical and chemical characteristics of the coating significantly influence the performance of NMC. A high degree of coating-layer porosity is favorable for the rate capability, while a high coverage of the surface, especially in vulnerable grain boundaries, enhances the long-term cycling stability and improves the cracking behavior of NMCs. While zirconium-containing coatings possess the best chemical properties for high rate performances, aluminum-containing coatings feature a superior chemical nature to protect high-nickel NMCs.