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: 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.

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: 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: In computed tomography, the reconstruction is typically obtained on a voxel grid. In this work, however, we propose a mesh-based reconstruction method. For tomographic problems, 3D meshes have mostly been studied to simulate data acquisition, but not for reconstruction, for which a 3D mesh means the inverse process of estimating shapes from projections. In this paper, we propose a differentiable forward model for 3D meshes that bridge the gap between the forward model for 3D surfaces and optimization. We view the forward projection as a rendering process, and make it differentiable by extending recent work in differentiable rendering. We use the proposed forward model to reconstruct 3D shapes directly from projections. Experimental results for single-object problems show that the proposed method outperforms traditional voxel-based methods on noisy simulated data. We also apply the proposed method on electron tomography images of nanoparticles to demonstrate the applicability of the method on real data.

Abstract: Four dimensional scanning transmission electron microscopy (4D STEM) records the scattering of electrons in a material in great detail. The benefits offered by 4D STEM are substantial, with the wealth of data it provides facilitating for instance high precision, high electron dose efficiency phase imaging via centre of mass or ptychography based analysis. However the requirement for a 2D image of the scattering to be recorded at each probe position has long placed a severe bottleneck on the speed at which 4D STEM can be performed. Recent advances in camera technology have greatly reduced this bottleneck, with the detection efficiency of direct electron detectors being especially well suited to the technique. However even the fastest frame driven pixelated detectors still significantly limit the scan speed which can be used in 4D STEM, making the resulting data susceptible to drift and hampering its use for low dose beam sensitive applications. Here we report the development of the use of an event driven Timepix3 direct electron camera that allows us to overcome this bottleneck and achieve 4D STEM dwell times down to 100 ns; orders of magnitude faster than what has been possible with frame based readout. We characterize the detector for different acceleration voltages and show that the method is especially well suited for low dose imaging and promises rich datasets without compromising dwell time when compared to conventional STEM imaging.

Abstract: Heterostructures composed of two-dimensional (2D) materials are already opening many new possibilities in such fields of technology as electronics and magnonics, but far more could be achieved if the number and diversity of 2D materials is increased. So far, only a few dozen 2D crystals have been extracted from materials that exhibit a layered phase in ambient conditions, omitting entirely the large number of layered materials that may exist in other temperatures and pressures. Here, we demonstrate how these structures can be stabilized in 2D van der Waals stacks under room temperature via growing them directly in graphene encapsulation by using graphene oxide as the template material. Specifically, we produce an ambient stable 2D structure of copper and iodine, a material that normally only occurs in layered form at elevated temperatures between 645 and 675 K. Our results establish a simple route to the production of more exotic phases of materials that would otherwise be difficult or impossible to stabilize for experiments in ambient.

Abstract: We explored the potential of plasma-based In-Situ Resource Utilization (ISRU) for Mars through the conversion of Martian atmosphere (~96% CO2, 2% N2, and 2% Ar) into life-sustaining chemicals. As the Martian surface pressure is about 1% of the Earth’s surface pressure, it is an ideal environment for plasma-based gas conversion using microwave reactors. At 1000 W and 10 Ln/min (normal liters per minute), we produced ~76 g/h of O2 and ~3 g/h of NOx using a 2.45 GHz waveguided reactor at 25 mbar, which is ~3.5 times Mars ambient pressure. The energy cost required to produce O2 was ~0.013 kWh/g, which is very promising compared to recently concluded MOXIE experiments on the Mars surface. This marks a crucial step towards realizing the extension of human exploration.

Abstract: Upgrading ethane with CO2 as a soft oxidant represents a desirable means of obtaining oxygenated hydrocarbons. This reaction is not thermodynamically feasible under mild conditions and has not been previously achieved as a one-step process. Nonthermal plasma was implemented as an alternative means of supplying energy to overcome activation barriers, leading to the production of alcohols, aldehydes, and acids as well as C1−C5+ hydrocarbons under ambient pressure, with a maximum total oxygenate selectivity of 12%. A plasma chemical kinetic computational model was developed and found to be in good agreement with the experimental trends. Results from this study illustrate the potential to use plasma for the direct synthesis of value-added alcohols, acids, and aldehydes from ethane and CO2 under mild conditions.

Abstract: We present a confined atmospheric pressure glow discharge plasma reactor, with very good performance towards dry reforming of methane, i.e., CO2 and CH4 conversion of 64 % and 94 %, respectively, at an energy cost of 3.5–4 eV/molecule (or 14–16 kJ/L). This excellent performance is among the best reported up to now for all types of plasma reactors in literature, and is due to the confinement of the plasma, which maximizes the fraction of gas passing through the active plasma region. The main product formed is syngas, with H2O and C2H2 as byproducts. We developed a quasi-1D chemical kinetics model, showing good agreement with the experimental results, which provides a thorough insight in the reaction pathways underlying the conversion of CO2 and CH4 and the formation of the different products.

Abstract: Development of low-cost and high-performance oxygen evolution reaction catalysts is key to implementing polymer electrolyte membrane water electrolyzers for hydrogen production. Iridium-based oxides are the state-of-the-art acidic oxygen evolution reactio catalysts but still suffer from inadequate activity and stability, and iridium's scarcity motivates the discovery of catalysts with lower iridium loadings. Here we report a mass-selected iridium-tantalum oxide catalyst prepared by a magnetron-based cluster source with considerably reduced noble-metal loadings beyond a commercial IrO2 catalyst. A sensitive electrochemistry/mass-spectrometry instrument coupled with isotope labelling was employed to investigate the oxygen production rate under dynamic operating conditions to account for the occurrence of side reactions and quantify the number of surface active sites. Iridium-tantalum oxide nanoparticles smaller than 2 nm exhibit a mass activity of 1.2 ± 0.5 kA “g” _“Ir” ^“-1” and a turnover frequency of 2.3 ± 0.9 s-1 at 320 mV overpotential, which are two and four times higher than those of mass-selected IrO2, respectively. Density functional theory calculations reveal that special iridium coordinations and the lowered aqueous decomposition free energy might be responsible for the enhanced performance.

Abstract: It is a generally accepted idea that the particle size distribution strongly affects the optical spectra of colloidal plasmonic nanoparticles. It is often quoted as one of the main reasons while explaining the mismatch between the theoretical and experimental optical spectra of such nanoparticles. In this work, these aspects are critically analyzed by means of a bottom up statistical approach that considers variables such as mean, standard deviation and skewness of the nanoparticle size distribution independently from one another. By assuming normal and log-normal distributions of the particle size, the effect of the statistical parameters on the Mie analytical optical spectra of colloidal nanoparticles was studied. The effect of morphology was also studied numerically in order to understand to what extent it can play a role. It is our finding that the particle polydispersity, skewness and surface morphology in fact only weakly impact the optical spectra. While, the selection of suitable optical constants with regard to the crystallinity of the nanoparticles is a far more influential factor for correctly predicting both the plasmon band position and the plasmon bandwidth in theoretical simulations of the optical spectra. It is shown that the mean particle size can be correctly estimated directly from the plasmon band position, as it is the mean that determines the resonance wavelength. The standard deviation can on the other hand be estimated from the intensity distribution data obtained from dynamic light scattering experiments. The results reported herein clear the ambiguity around particle size distribution and optical response of colloidal plasmonic nanoparticles.

Abstract: Chirality in inorganic nanoparticles and nanostructures has gained increasing scientific interest, because of the possibility to tune their ability to interact differently with left- and right-handed circularly polarized light. In some cases, the optical activity is hypothesized to originate from a chiral morphology of the nanomaterial. However, quantifying the degree of chirality in objects with sizes of tens of nanometers is far from straightforward. Electron tomography offers the possibility to faithfully retrieve the three-dimensional morphology of nanomaterials, but only a qualitative interpretation of the morphology of chiral nanoparticles has been possible so far. We introduce herein a methodology that enables us to quantify the helicity of complex chiral nanomaterials, based on the geometrical properties of a helix. We demonstrate that an analysis at the single particle level can provide significant insights into the origin of chiroptical properties.

Abstract: CO2 conversion by plasma technology is gaining increasing interest. We present a carbon (charcoal) bed placed after a Gliding Arc Plasmatron (GAP) reactor, to enhance the CO2 conversion, promote O/O2 removal and in­ crease the CO fraction in the exhaust mixture. By means of an innovative (silo) system, the carbon is constantly supplied, to avoid carbon depletion upon reaction with O/O2. Using this carbon bed, the CO2 conversion is enhanced by almost a factor of two (from 7.6 to 12.6%), while the CO concentration even increases by a factor of three (from 7.2 to 21.9%), and O2 is completely removed from the exhaust mixture. Moreover, the energy ef­ ficiency of the conversion process drastically increases from 27.9 to 45.4%, and the energy cost significantly drops from 41.9 to 25.4 kJ.L− 1. We also present the temperature as a function of distance from the reactor outlet, as well as the CO2, CO and O2 concentrations and the temperature in the carbon bed as a function of time, which is important for understanding the underlying mechanisms. Indeed, these time-resolved measurements reveal that the initial enhancements in CO2 conversion and in CO concentration are not maintained in our current setup. Therefore, we present a model to study the gasification of carbon with different feed gases (i.e., O2, CO and CO2 separately), from which we can conclude that the oxygen coverage at the surface plays a key role in determining the product composition and the rate of carbon consumption. Indeed, our model insights indicate that the drop in CO2 conversion and in CO concentration after a few minutes is attributed to deactivation of the carbon bed, due to rapid formation of oxygen complexes at the surface.