Microwave-assisted preparation of 1D NCs for photodegradation process of organic dyes

[EN] Zinc sulphide (ZnS) and zinc selenide (ZnSe) and manganese-doped and un-doped with different morphologies from 1D do 3D microflowers were successfully fabricated in only a few minutes by solvothermal reactions under microwave irradiation. In order to compare the effect of microwave heating on the properties of obtained nanocrystals, additionally the synthesis under conventional heating was conducted additionally in similar conditions. The obtained nanocrystals were systematically characterized in terms of structural and optical properties using X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), diffuse reflectance UV-Vis spectroscopy (DR UV-Vis), Fourier-transform infrared spectroscopy (FT-IR), photoluminescence spectroscopy (PL), X-ray photoelectron spectroscopy (XPS) and Brunauer-Emmett-Teller (BET) surface area analysis. The photocatalytic activity of ZnSe, ZnS, ZnS:Mn and ZnSe:Mn nanocrystals with different morphologies was evaluated by the degradation of methyl orange (MO) and Rhodamine 6G (R6G), respectively. The results show that Mn doped NCs samples had higher coefficient of degradation of organic dyes under ultraviolet irradiation (UV).This work was financially supported by the National Centre for Research and Development within the V edition of the LIDER program (project number LIDER/009/185/L5/13/NCBR/2014).Zaba, A.; Matras-Postolek, K.; Sovinska, S.; Bogdal, D. (2019). Microwave-assisted preparation of 1D NCs for photodegradation process of organic dyes. En AMPERE 2019. 17th International Conference on Microwave and High Frequency Heating. Editorial Universitat Politècnica de València. 418-424. https://doi.org/10.4995/AMPERE2019.2019.9998OCS41842

In situ X-ray Diffraction Computed Tomography studies examining the thermal and chemical stabilities of working Ba0.5Sr0.5Co0.8Fe0.2O3-δ membranes during oxidative coupling of methane

In this study we present the results from two in situ X-ray diffraction computed tomography experiments of catalytic membrane reactors (CMRs) using Ba0.5Sr0.5Co0.8Fe0.2O3−δ (BSCF) hollow fibre membranes and Na-Mn-W/SiO2 catalyst during the oxidative coupling of methane (OCM) reaction. The negative impact of CO2, when added to the inlet gas stream, is seen to be mainly related to the C2+ yield, while no evidence of carbonate phase(s) formation is found during the OCM experiments. The main degradation mechanism of the CMR is suggested to be primarily associated with the solid-state evolution of the BSCF phase rather than the presence of CO2. Specifically, in situ XRD-CT and post-mortem SEM/EDX measurements revealed a collapse of the cubic BSCF phase, formation of secondary phases, which include needle-like structures and hexagonal Ba6Co4O12, and formation of a BaWO4 layer, the latter being a result of chemical interaction between the membrane and catalyst materials at high temperatures

A multi-scale study of 3D printed Co-Al2O3 catalyst monoliths versus spheres

This study demonstrates the characteristics of two model packing configurations: 3D printed (3DP) catalyst monoliths on the one hand, and their conventional counterparts, packed beds of spheres, on the other. Cobalt deposited on alumina is selected as a convenient model system for this work, due to its wide spread use in many catalytic reactions. 3DP constructs were produced from alumina powder impregnated with cobalt nitrate while the alumina spheres were directly impregnated with the same cobalt nitrate precursor. The form of the catalyst, the impregnation process, as well as the thermal history, were found to have a significant effect on the resulting cobalt phases. Probing the catalyst bodies in situ by XRD-CT indicated that the level of dispersion of identified Co phases (Co3O4 reduced to CoO) across the support is maintained under reduction conditions. The packed bed of spheres exhibits a non-uniform distribution of cobalt phases, including a core-shell morphology with an average crystallite size of 10–14 nm across the sphere, while the 3DP monolith exhibits a uniform distribution of cobalt phases with an average crystallite size of 5–12 nm upon reduction from Co3O4 to CoO. Computational Fluid Dynamics (CFD) modelling was carried out to develop digital twins and assess the effect of the geometry of both configurations on the pressure drop and velocity profiles. Finally, the activity of both Cobalt-based catalyst geometries was assessed in terms of their conversion, selectivity and turn over frequencies under model multiphase (selective oxidation) reaction conditions, which showed that the desired 3D printed monolithic geometries can offer distinct advantages to the reactor design

A deep convolutional neural network for real-time full profile analysis of big powder diffraction data

We present Parameter Quantification Network (PQ-Net), a regression deep convolutional neural network providing quantitative analysis of powder X-ray diffraction patterns from multi-phase systems. The network is tested against simulated and experimental datasets of increasing complexity with the last one being an X-ray diffraction computed tomography dataset of a multi-phase Ni-Pd/CeO2-ZrO2/Al2O3 catalytic material system consisting of ca. 20,000 diffraction patterns. It is shown that the network predicts accurate scale factor, lattice parameter and crystallite size maps for all phases, which are comparable to those obtained through full profile analysis using the Rietveld method, also providing a reliable uncertainty measure on the results. The main advantage of PQ-Net is its ability to yield these results orders of magnitude faster showing its potential as a tool for real-time diffraction data analysis during in situ/operando experiments

Identifying the Origins of Microstructural Defects Such as Cracking within Ni‐Rich NMC811 Cathode Particles for Lithium‐Ion Batteries

The next generation of automotive lithium‐ion batteries may employ NMC811 materials; however, defective particles are of significant interest due to their links to performance loss. Here, it is demonstrated that even before operation, on average, one‐third of NMC811 particles experience some form of defect, increasing in severity near the separator interface. It is determined that defective particles can be detected and quantified using low resolution imaging, presenting a significant improvement for material statistics. Fluorescence and diffraction data reveal that the variation of Mn content within the NMC particles may correlate to crystallographic disordering, indicating that the mobility and dissolution of Mn may be a key aspect of degradation during initial cycling. This, however, does not appear to correlate with the severity of particle cracking, which when analyzed at high spatial resolutions, reveals cracking structures similar to lower Ni content NMC, suggesting that the disconnection and separation of neighboring primary particles may be due to electrochemical expansion/contraction, exacerbated by other factors such as grain orientation that are inherent in such polycrystalline materials. These findings can guide research directions toward mitigating degradation at each respective length‐scale: electrode sheets, secondary and primary particles, and individual crystals, ultimately leading to improved automotive ranges and lifetimes

Multi-length scale 5D diffraction imaging of Ni-Pd/CeO2-ZrO2/Al2O3 catalyst during partial oxidation of methane

A 5D diffraction imaging experiment (with 3D spatial, 1D time/imposed operating conditions and 1D scattering signal) was performed with a Ni–Pd/CeO2–ZrO2/Al2O3 catalyst. The catalyst was investigated during both activation and partial oxidation of methane (POX). The spatio-temporal resolved diffraction data allowed us to obtain unprecedented insight into the behaviour and fate of the various metal and metal oxide species and how this is affected by the heterogeneity across catalyst particles. We show firstly, how Pd promotion although facilitating Ni reduction, over time leads to formation of unstable Ni–Pd metallic alloy, rendering the impact of Pd beyond the initial reduction less important. Furthermore, in the core of the particles, where the metallic Ni is primarily supported on Al2O3, poor resistance towards coke deposition was observed. We identified that this preceded via the formation of an active yet metastable interstitial solid solution of Ni–C and led to the exclusive formation of graphitic carbon, the only polymorph of coke observed. In contrast, at the outermost part of the catalyst particle, where Ni is predominantly supported on CeO2–ZrO2, the graphite formation was mitigated but sintering of Ni crystallites was more severe

Multi‐scale studies of 3d printed Mn–Na–W/SiO2 catalyst for oxidative coupling of methane

This work presents multi-scale approaches to investigate 3D printed structured Mn–Na–W/SiO_{2} atalysts used for the oxidative coupling of methane (OCM) reaction. The performance of the 3D printed catalysts has been compared to their conventional analogues, packed beds of pellets and powder. The physicochemical properties of the 3D printed catalysts were investigated using scanning electron microscopy, nitrogen adsorption and X-ray diffraction (XRD). Performance and durability tests of the 3D printed catalysts were conducted in the laboratory and in a miniplant under real reaction conditions. In addition, synchrotron-based X-ray diffraction computed tomography technique (XRD-CT) was employed to obtain cross sectional maps at three different positions selected within the 3D printed catalyst body during the OCM reaction. The maps revealed the evolution of catalyst active phases and silica support on spatial and temporal scales within the interiors of the 3D printed catalyst under operating conditions. These results were accompanied with SEM-EDS analysis that indicated a homogeneous distribution of the active catalyst particles across the silica support

Type I interferon responses of common carp strains with different levels of resistance to koi herpesvirus disease during infection with CyHV-3 or SVCV

Carp from breeding strains with different genetic background present diverse levels of resistance to viral pathogens. Carp strains of Asian origin, currently being treated as Cyprinus rubrofuscus L., especially Amur wild carp (AS), were proven to be more resistant to koi herpesvirus disease (KHVD; caused by cyprinid herpesvirus 3, CyHV-3) than strains originating from Europe and belonging to Cyprinus carpio L., like the Prerov scale carp (PS) or koi carp from a breed in the Czech Republic. We hypothesised that it can be associated with a higher magnitude of type I interferon (IFN) response as a first line of innate defence mechanisms against viral infections. To evaluate this hypothesis, four strains of common carp (AS, Rop, PS and koi) were challenged using two viral infection models: Rhabdovirus SVCV (spring viremia of carp virus) and alloherpesvirus CyHV-3. The infection with SVCV induced a low mortality rate and the most resistant was the Rop strain (no mortalities), whereas the PS strain was the most susceptible (survival rate of 78%). During CyHV-3 infection, Rop and AS strains performed better (survival rates of 78% and 53%, respectively) than PS and koi strains (survival rates of 35% and 10%, respectively). The evaluation of virus loads and virus replication showed significant differences between the carp strains, which correlated with the mortality rate. The evaluation of type I IFN responses showed that there were fundamental differences between the virus infection models. While responses to the SVCV were high, the CyHV-3 generally induced low responses. Furthermore, the results demonstrated that the magnitude of type I IFN responses did not correlate with a higher resistance in infected carp. In the case of a CyHV-3 infection, reduced type I IFN responses could be related to the potential ability of the virus to interfere with cellular sensing of foreign nucleic acids. Taken together, the results broaden our understanding of how common carp from different genetic lines interact with various viral pathogens

Cycling Rate-Induced Spatially-Resolved Heterogeneities in Commercial Cylindrical Li-Ion Batteries

Synchrotron high-energy X-ray diffraction computed tomography has been employed to investigate, for the first time, commercial cylindrical Li-ion batteries electrochemically cycled over the two cycling rates of C/2 and C/20. This technique yields maps of the crystalline components and chemical species as a cross-section of the cell with high spatiotemporal resolution (550 × 550 images with 20 × 20 × 3 µm3 voxel size in ca. 1 h). The recently developed Direct Least-Squares Reconstruction algorithm is used to overcome the well-known parallax problem and led to accurate lattice parameter maps for the device cathode. Chemical heterogeneities are revealed at both electrodes and are attributed to uneven Li and current distributions in the cells. It is shown that this technique has the potential to become an invaluable diagnostic tool for real-world commercial batteries and for their characterization under operating conditions, leading to unique insights into “real” battery degradation mechanisms as they occur

A scalable neural network architecture for self-supervised tomographic image reconstruction

We present a lightweight and scalable artificial neural network architecture which is used to reconstruct a tomographic image from a given sinogram. A self-supervised learning approach is used where the network iteratively generates an image that is then converted into a sinogram using the Radon transform; this new sinogram is then compared with the sinogram from the experimental dataset using a combined mean absolute error and structural similarity index measure loss function to update the weights of the network accordingly. We demonstrate that the network is able to reconstruct images that are larger than 1024 × 1024. Furthermore, it is shown that the new network is able to reconstruct images of higher quality than conventional reconstruction algorithms, such as the filtered back projection and iterative algorithms (SART, SIRT, CGLS), when sinograms with angular undersampling are used. The network is tested with simulated data as well as experimental synchrotron X-ray micro-tomography and X-ray diffraction computed tomography data