The optical cross sections of plasmonic nanoparticles are intricately linked to their morphologies. Accurately capturing this link could allow determination of particles' shapes from their optical cross sections alone. Electromagnetic simulations bridge morphology and optical properties, provided they are sufficiently accurate. This study examines key factors affecting simulation precision, comparing common methods and detailing the impacts of meshing accuracy, dielectric function selection, and substrate inclusion within the boundary element method. To support the method's complex parameterization, we develop a workflow incorporating reconstruction, meshing, and mesh simplification, to enable the use of electron tomography data. We analyze how choices of reconstruction algorithm and image segmentation affect simulated optical cross sections, relating these to shape errors minimized during data processing. Optimal results are obtained using the total variation minimization (TVM) reconstruction method with Otsu thresholding and light smoothing, ensuring reliable, watertight surface meshes through the marching cubes algorithm, even for complex shapes.

Sample holders for transmission electron microscopy (TEM) based on micro‐electro‐mechanical systems (MEMS) have recently become popular for investigating the behavior of nanomaterials under in situ or environmental conditions. The accuracy and reproducibility of these in situ holders are essential to ensure the reliability of experimental results. In addition, the uniformity of an applied temperature trigger across the MEMS chip is a crucial parameter. In this work, it is measured the temperature homogeneity of MEMS‐based heating sample supports by locally analyzing the dynamics of heat‐induced alloying of Au@Ag nanoparticles located in different regions of the support through quantitative fast high‐angle annular dark‐field scanning TEM tomography. These results demonstrate the superior temperature homogeneity of a microheater design based on a heating element shaped as a circular spiral with a width decreasing outwards compared to a double spiral‐shaped designed microheater. The proposed approach to measure the local temperature homogeneity based on the thermal properties of bimetallic nanoparticles will support the future development of MEMS‐based heating supports with improved thermal properties and in situ studies where high precision in the temperature at a certain position is required.

Many of the recent successes of deep learning-based approaches have been enabled by a framework of flexible, composable computational blocks with their parameters adjusted through an automatic differentiation mechanism to implement various data processing tasks. In this work, we explore how the same philosophy can be applied to existing “classical” (i.e., non-learning) algorithms, focusing on computed tomography (CT) as application field. We apply four key design principles of this approach for CT workflow design: end-to-end optimization, explicit quality criteria, declarative algorithm construction by building the forward model, and use of existing classical algorithms as computational blocks. Through four case studies, we demonstrate that auto-differentiation is remarkably effective beyond the boundaries of neural-network training, extending to CT workflows containing varied combinations of classical and machine learning algorithms.

Many advanced nanomaterials rely on carefully designed morphology and elemental distribution to achieve their functionalities. Among the few experimental techniques that can directly visualise the 3D elemental distribution on the nanoscale are approaches based on electron tomography in combination with energy-dispersive X-ray spectroscopy (EDXS) and electron energy loss spectroscopy (EELS). Unfortunately, these highly informative methods are severely limited by the fundamentally low signal-to-noise ratio, which makes long experimental times and high electron irradiation doses necessary to obtain reliable 3D reconstructions. Addressing these limitations has been the major research question for the development of these techniques in recent years. This short review outlines the latest progress on the methods to reduce experimental time and electron irradiation dose requirements for 3D elemental distribution analysis and gives an outlook on the development of this field in the near future.

Oxidized cobalt ferrite nanocrystals with a modified distribution of the magnetic cations in their spinel structure give place to an unusual exchange-coupled system with a double reversal of the magnetization, exchange bias, and increased coercivity, but without the presence of a clear physical interface that delimits two well-differentiated magnetic phases. More specifically, the partial oxidation of cobalt cations and the formation of Fe vacancies at the surface region entail the formation of a cobalt-rich mixed ferrite spinel, which is strongly pinned by the ferrimagnetic background from the cobalt ferrite lattice. This particular configuration of exchange-biased magnetic behavior, involving two different magnetic phases but without the occurrence of a crystallographically coherent interface, revolutionizes the established concept of exchange bias phenomenology.

Electron tomography (ET) is a powerful tool to determine the three-dimensional (3D) structure of nanomaterials in a transmission electron microscope. However, the acquisition of a conventional tilt series for ET is a time-consuming process and can therefore not provide 3D structural information in a time-efficient manner. Here, we propose surface-sensitive secondary electron (SE) imaging as an alternative to ET for the investigation of the morphology of nanomaterials. We use the SE electron beam induced current (SEEBIC) technique that maps the electrical current arising from holes generated by the emission of SEs from the sample. SEEBIC imaging can provide valuable information on the sample morphology with high spatial resolution and significantly shorter throughput times compared with ET. In addition, we discuss the contrast formation mechanisms that aid in the interpretation of SEEBIC data.

Daniel Arenas Esteban, Hans Vanrompay, Alexander Skorikov, Armand Béché, Johan Verbeeck, Bert Freitag and Sara Bals Electron Microscopy for Materials Science (EMAT), University of Antwerp, 2020 Antwerp, Belgium, United States, Electron Microscopy for Materials Science (EMAT), University of Antwerp, 2020 Antwerp, Belgium, Belgium, Thermo Fisher Scientific, Achtseweg Noord 5, 5651 GG Eindhoven, The Netherlands, Eindhoven, Noord-Brabant, Netherlands

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.

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.

γ- and δ-alumina are popular catalyst support materials. Using a hydrothermal synthesis method starting from aluminum nitrate and urea in diluted solution, spherical core-shell particles with a uniform particle size of about 1 μm were synthesized. Upon calcination at 1000 °C, the particles adopted a core-shell structure with a γ-alumina core and δ-alumina shell as evidenced by 2D and 3D electron microscopy and 27Al magic angle spinning nuclear magnetic resonance spectroscopy. The spherical alumina particles were loaded with Pt nanoparticles with an average size below 1 nm using the strong electrostatic adsorption method. Electron microscopy and energy dispersive X-ray spectroscopy revealed a homogeneous platinum dispersion over the alumina surface. These platinum loaded alumina spheres were used as a model catalyst for bifunctional catalysis. Physical mixtures of Pt/alumina spheres and spherical zeolite particles are equivalent to catalysts with platinum deposited on the zeolite itself facilitating the investigation of the catalyst components individually. The spherical alumina particles are very convenient supports for obtaining a homogeneous distribution of highly dispersed platinum nanoparticles. Obtaining such a small Pt particle size is challenging on other support materials such as zeolites. The here reported and well-characterized Pt/alumina spheres can be combined with any zeolite and used as a bifunctional model catalyst. This is an interesting strategy for the examination of the acid catalytic function without the interference of the supported platinum metal on the investigated acid material.

Understanding light–matter interactions in nanomaterials is crucial for optoelectronic, photonic, and plasmonic applications. Specifically, metal nanoparticles (NPs) strongly interact with light and can undergo shape transformations, fragmentation and ablation upon (pulsed) laser excitation. Despite being vital for technological applications, experimental insight into the underlying atomistic processes is still lacking due to the complexity of such measurements. Herein, atomic resolution electron tomography is performed on the same mesoporous‐silica‐coated gold nanorod, before and after femtosecond laser irradiation, to assess the missing information. Combined with molecular dynamics (MD) simulations based on the experimentally determined 3D atomic‐scale morphology, the complex atomistic rearrangements, causing shape deformations and defect generation, are unraveled. These rearrangements are simultaneously driven by surface diffusion, facet restructuring, and strain formation, and are influenced by subtleties in the atomic distribution at the surface.

This dataset contains 1000 simulated nanoparticle-like 3D structures and noisy EDX-like elemental maps based on them. These data are intended to be used for quantitative analysis of data processing methods in (EDX) tomography of nanoparticles and training the data-driven approaches for these tasks. The dataset is structured as follows: voxel_data/clean 3D voxel grid representation of the simulated nanoparticles. Voxel intensities are adjusted so that the total intensity equals 103. All 3D structures have unique identifiers in 0..999 range. The data derived from a 3D structure preserves this unique identifier. sinograms/clean Tilt series of projection images obtained from the corresponding 3D structures over an angular range of -75..75 degrees with a tilt step of 10 degrees to simulate a typical tilt series used in EDX tomography. Total intensity in each projection image equals 103. sinograms/noisy Tilt series of projection images corrupted with Poisson noise and an additional spatially uniform background noise. projections/clean Projection images extracted from the clean tilt series at 0 degrees tilt angle. projections/noisy Projection images extracted from the noisy tilt series at 0 degrees tilt angle. images/clean Visualizations of the clean projections as PNG images with the intensity range adjusted to 0..255 images/noisy Visualizations of the noisy projections as PNG images with the intensity range adjusted to 0..255 Data formats Voxel data, sinograms and projection images are stored in a compressed Matlab binary MAT format. Images are stores in PNG format.

Raman-encoded gold nanoparticles (NPs) have been widely employed as photostable multifunctional probes for sensing, bioimaging, multiplex diagnostics, and surface-enhanced Raman scattering (SERS)-guided tumor therapy. We report a strategy toward obtaining a particularly large library of Au nanocapsules encoded with Raman codes defined by the combination of different thiol-free Raman reporters, encapsulated at defined molar ratios. The fabrication of SERS tags with tailored size and predefined codes is based on the in situ incorporation of Raman reporter molecules inside Au nanocapsules during their formation via galvanic replacement coupled to seeded growth on Ag NPs. The hole-free closed-shell structure of the nanocapsules is confirmed by electron tomography. The unusually wide encoding possibilities of the obtained SERS tags are investigated by means of either wavenumber-based encoding or Raman frequency combined with signal intensity, leading to an outstanding performance as exemplified by 26 and 54 different codes, respectively. We additionally demonstrate that encoded nanocapsules can be readily bioconjugated with antibodies for applications such as SERS-based targeted cell imaging and phenotyping.

Electron tomography is a widely used experimental technique for analyzing nanometer-scale structures of a large variety of materials in three dimensions. Unfortunately, the acquisition of conventional electron tomography tilt series can easily take up one hour or more, depending on the complexity of the experiment. Using electron tomography, it is therefore far from straightforward to obtain statistically meaningful 3D data, to investigate samples that do not withstand long acquisition, or to perform in situ 3D characterization using this technique. Various acquisition strategies have been proposed to accelerate the tomographic acquisition, and reduce the required electron dose. These methods include tilting the holder continuously while acquiring a projection movie and a hybrid, incremental, methodology which combines the benefits of the conventional and continuous technique. In this paper, the different acquisition strategies will be experimentally compared in terms of speed, resolution and electron dose, based on experimental tilt series acquired for various metallic nanoparticles.

Albumin-coated iron oxide magnetic nanoparticles were designed and synthesized. It can act as contrast agents for MRI diagnostics of various tumors, as well as a nanovehicle for targeted delivery of chemotherapy and immunotherapy drugs.

HAADF-STEM tomography is a widely used experimental technique for analyzing nanometer-scale structures of a large variety of materials in three dimensions. It is especially useful for studying crystalline nanoparticles, where conventional TEM tomography suffers from diffraction-related artefacts. Unfortunately, the acquisition of a HAADF-STEM tilt series can easily take up one hour or more, depending on the complexity of the experiment. It is therefore challenging to investigate samples that do not withstand long electron beam illumination or to acquire a large number of tilt series during a single TEM experiment. The latter would facilitate obtaining more statistically representative 3D data, and enable performing dynamic in situ 3D characterizations with a finer time resolution. Various HAADF-STEM acquisition strategies have been proposed to accelerate the tomographic acquisition and reduce the required electron dose. These methods include tilting the holder continuously while acquiring a projection "movie" and a hybrid, incremental, methodology which combines the benefits of the conventional and continuous technique. However, until now an experimental evaluation of these techniques has been lacking. In this paper, the different acquisition strategies will be experimentally compared in terms of speed, resolution and electron dose. This evaluation will be performed based on experimental tilt series, acquired for various metallic nanoparticles with different shapes and sizes. We discuss the necessary data processing and provide a general guideline that can be used to determine the most optimal acquisition strategy for specific electron tomography experiments.

The nucleation, growth and faceting of Fe3O4–Au nanoparticles is systematically monitored using liquid probes during reaction. Two consecutive processes are obtained, the growth of Fe3O4 spheres on Au seeds and faceting towards octahedral motifs.

Anisotropic bimetallic nanoparticles are promising candidates for plasmonic and catalytic applications. Their catalytic performance and plasmonic properties are closely linked to the distribution of the two metals, which can change during applications in which the particles are exposed to heat. Due to this fact, correlating the thermal stability of complex heterogeneous nanoparticles to their microstructural properties is of high interest for the practical applications of such materials. Here, we employ quantitative electron tomography in high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) mode to measure the 3D elemental diffusion dynamics in individual anisotropic Au-Ag nanoparticles upon heating in situ. This approach allows us to study the elemental redistribution in complex, asymmetric nanoparticles on a single particle level, which has been inaccessible to other techniques so far. In this work, we apply the proposed method to compare the alloying dynamics of Au-Ag nanoparticles with different shapes and compositions and find that the shape of the nanoparticle does not exhibit a significant effect on the alloying speed whereas the composition does. Finally, comparing the experimental results to diffusion simulations allows us to estimate the diffusion coefficients of the metals for individual nanoparticles.