Abstract: Plasma catalysis is an emerging new technology for the electrification and downscaling of NH3 synthesis. Increasing attention is being paid to the optimization of plasma catalysis with respect to the plasma conditions, the catalyst material, and their mutual interaction. In this work we use microkinetic models to study how the total conversion process is impacted by the combination of different plasma conditions and transition metal catalysts. We study how plasma-generated radicals and vibrationally excited N2 (present in a dielectric barrier discharge plasma) interact with the catalyst and impact the NH3 turnover frequencies (TOFs). Both filamentary and uniform plasmas are studied, based on plasma chemistry models that provided plasma phase speciation and vibrational distribution functions. The Langmuir−Hinshelwood reaction rate coefficients (i.e., adsorption reactions and subsequent reactions among adsorbates) are determined using conventional scaling relations. An additional set of Eley−Rideal reactions (i.e., direct reactions of plasma radicals with adsorbates) was added and a sensitivity analysis on the assumed reaction rate coefficients was performed. We first show the impact of different vibrational distribution functions on the catalytic dissociation of N2 and subsequent production of NH3, and we gradually include more radical reactions, to illustrate the contribution of these species and their corresponding reaction pathways. Analysis over a large range of catalysts indicates that different transition metals (metals such as Rh, Ni, Pt, and Pd) optimize the NH3TOFs depending on the population of the vibrational levels of N2. At higher concentrations of plasma-generated radicals, the NH3 TOFs become less dependent on the catalyst material, due to radical adsorptions on the more noble catalysts and Eley−Rideal reactions on the less noble catalysts.

Abstract: Numerous applications have required the study of CO2 plasmas since the 1960s, from CO2 lasers to spacecraft heat shields. However, in recent years, intense research activities on the subject have restarted because of environmental problems associated with CO2 emissions. The present review provides a synthesis of the current state of knowledge on the physical chemistry of cold CO2 plasmas. In particular, the different modeling approaches implemented to address specific aspects of CO2 plasmas are presented. Throughout the paper, the importance of conducting joint experimental, theoretical and modeling studies to elucidate the complex couplings at play in CO2 plasmas is emphasized. Therefore, the experimental data that are likely to bring relevant constraints to the different modeling approaches are first reviewed. Second, the calculation of some key elementary processes obtained with semi-empirical, classical and quantum methods is presented. In order to describe the electron kinetics, the latest coherent sets of cross section satisfying the constraints of “electron swarm” analyses are introduced, and the need for self-consistent calculations for determining accurate electron energy distribution function (EEDF) is evidenced. The main findings of the latest zero-dimensional (0D) global models about the complex chemistry of CO2 and its dissociation products in different plasma discharges are then given, and full state-to-state (STS) models of only the vibrational-dissociation kinetics developed for studies of spacecraft shields are described. Finally, two important points for all applications using CO2 containing plasma are discussed: the role of surfaces in contact with the plasma, and the need for 2D/3D models to capture the main features of complex reactor geometries including effects induced by fluid dynamics on the plasma properties. In addition to bringing together the latest advances in the description of CO2 non-equilibrium plasmas, the results presented here also highlight the fundamental data that are still missing and the possible routes that still need to be investigated.

Abstract: Reactive oxygen and nitrogen species (RONS) are involved in many biochemical processes, including nitrooxidative stress that causes cancer cell death, observed in cancer therapies such as photodynamic therapy and cold atmospheric plasma. However, their mechanisms of action and selectivity still remain elusive due to the complexity of biological cells. For example, it is not well known how RONS generated by cancer therapies permeate the cell membrane to cause nitro-oxidative damage. There are many studies dedicated to the perme­ation of RONS across native and oxidized membranes, but not across nitrated membranes, another lipid product also generated during nitro-oxidative stress. Herein, we performed molecular dynamics (MD) simulations to calculate the free energy barrier of RONS permeation across nitrated membranes. Our results show that hy­drophilic RONS, such as hydroperoxyl radical (HO2) and peroxynitrous acid (ONOOH), have relatively low barriers compared to hydrogen peroxide (H2O2) and hydroxyl radical (HO), and are more prone to permeate the membrane than for the native or peroxidized membranes, and similar to aldehyde-oxidized membranes. Hy­drophobic RONS like molecular oxygen (O2), nitrogen dioxide (NO2) and nitric oxide (NO) even have insignif­icant barriers for permeation. Compared to native and peroxidized membranes, nitrated membranes are more permeable, suggesting that we must not only consider oxidized membranes during nitro-oxidative stress, but also nitrated membranes, and their role in cancer therapies.

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: Filamentous cable bacteria display long-range electron transport, generating electrical currents over centimeter distances through a highly ordered network of fibers embedded in their cell envelope. The conductivity of these periplasmic wires is exceptionally high for a biological material, but their chemical structure and underlying electron transport mechanism remain unresolved. Here, we combine high-resolution microscopy, spectroscopy, and chemical imaging on individual cable bacterium filaments to demonstrate that the periplasmic wires consist of a conductive protein core surrounded by an insulating protein shell layer. The core proteins contain a sulfur-ligated nickel cofactor, and conductivity decreases when nickel is oxidized or selectively removed. The involvement of nickel as the active metal in biological conduction is remarkable, and suggests a hitherto unknown form of electron transport that enables efficient conduction in centimeter-long protein structures.

Abstract: Atomic arrangements in the mixed-valence oxide U3O7 are refined from high-resolution neutron scattering data. The crystallographic model describes a long-range structural order in a U60O140 primitive cell (space group P42/n) containing distorted cuboctahedral oxygen clusters. By combining experimental data and electronic structure calculations accounting for spin–orbit interactions, we provide robust evidence of an interplay between charge localization and the magnetic moments carried by the uranium atoms. The calculations predict U3O7 to be a semiconducting solid with a band gap of close to 0.32 eV, and a more pronounced charge-transfer insulator behavior as compared to the well-known Mott insulator UO2. Most uranium ions (56 out of 60) occur in 9-fold and 10-fold coordinated environments, surrounding the oxygen clusters, and have a tetravalent (24 out of 60) or pentavalent (32 out of 60) state. The remaining uranium ions (4 out of 60) are not contiguous to the oxygen cuboctahedra and have a very compact, 8-fold coordinated environment with two short (2 × 1.93(3) Å) “oxo-type” bonds. The higher Hirshfeld charge and the diamagnetic character point to a hexavalent state for these four uranium ions. Hence, the valence state distribution corresponds to 24/60 × U(IV) + 32/60 U(V) + 4/60 U(VI). The tetravalent and pentavalent uranium ions are predicted to carry noncollinear magnetic moments (with amplitudes of 1.6 and 0.8 μB, respectively), resulting in canted ferromagnetic order in characteristic layers within the overall fluorite-related structure.

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: Single-shot structured light profilometry (SLP) aims at reconstructing the 3D height map of an object from a single deformed fringe pattern and has long been the ultimate goal in fringe projection profilometry. Recently, deep learning was introduced into SLP setups to replace the task-specific algorithm of fringe demodulation with a dedicated neural network. Research on deep learning-based profilometry has made considerable progress in a short amount of time due to the rapid development of general neural network strategies and to the transferrable nature of deep learning techniques to a wide array of application fields. The selection of the employed loss function has received very little to no attention in the recently reported deep learning-based SLP setups. In this paper, we demonstrate the significant impact of loss function selection on height map prediction accuracy, we evaluate the performance of a range of commonly used loss functions and we propose a new mixed gradient loss function that yields a higher 3D surface reconstruction accuracy than any previously used loss functions.

Abstract: The selective oxidation of methane to methanol (SOMTM) by molecular oxygen is a holy grail in catalytic chemistry and remains a challenge in chemical industry. We perform SOMTM in a CH4/O2 plasma, at low temperature and atmospheric pressure, promoted by Ni-based catalysts, reaching 81 % liquid oxygenates selectivity and 50 % CH3OH selectivity, with an excellent catalytic stability. Chemical kinetics modelling shows that CH3OH in the plasma is mainly produced through radical reactions, i.e., CH4 + O(1D) → CH3O + H, fol­lowed by CH3O + H + M→ CH3OH + M and CH3O + HCO → CH3OH + CO. The catalyst characterization shows that the improved production of CH3OH is attributed to abundant chemisorbed oxygen species, originating from highly dispersed NiO phase with strong oxide support interaction with γ-Al2O3, which are capable of promoting CH3OH formation through E-R reactions and activating H2O molecules to facilitate CH3OH desorption.

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: 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: 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: 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.