Comparison of the Spinels Co3O4 and NiCo2O4 as Bifunctional Oxygen Catalysts in Alkaline Media

Data from experiments with both rotating disc electrodes (RDEs) and gas diffusion electrodes (GDEs) are used to investigate the properties of the spinels, Co3O4 and NiCo2O4, as bifunctional oxygen electrocatalysts. Emphasis is placed on catalyst compositions and electrode structures free of carbon. Oxygen evolution and reduction occur at surfaces where the transition metals are in different oxidation states but the surface can be repeatedly cycled between these two states without significant change. It is shown that carbon-free, NiCo2O4 catalysed GDEs can be fabricated using structures based on stainless steel cloth or nickel foam. Those based on nickel foam can be cycled extensively and allow both O2 evolution and reduction at current densities up to 100 mA cm−2.European Commission (Theme 2010.7.3.1) Energy Storage Systems for Power Distribution NetworksMinistry of National Education, Republic of Turke

Chemical imaging of Fischer-Tropsch catalysts under operating conditions

Although we often understand empirically what constitutes an active catalyst, there is still much to be understood fundamentally about how catalytic performance is influenced by formulation. Catalysts are often designed to have a microstructure and nanostructure that can influence performance but that is rarely considered when correlating structure with function. Fischer-Tropsch synthesis (FTS) is a well-known and potentially sustainable technology for converting synthetic natural gas (“syngas”: CO + H2) into functional hydrocarbons, such as sulfur- and aromatic-free fuel and high-value wax products. FTS catalysts typically contain Co or Fe nanoparticles, which are often optimized in terms of size/composition for a particular catalytic performance. We use a novel, “multimodal” tomographic approach to studying active Co-based catalysts under operando conditions, revealing how a simple parameter, such as the order of addition of metal precursors and promoters, affects the spatial distribution of the elements as well as their physicochemical properties, that is, crystalline phase and crystallite size during catalyst activation and operation. We show in particular how the order of addition affects the crystallinity of the TiO2 anatase phase, which in turn leads to the formation of highly intergrown cubic close-packed/hexagonal close-packed Co nanoparticles that are very reactive, exhibiting high CO conversion. This work highlights the importance of operando microtomography to understand the evolution of chemical species and their spatial distribution before any concrete understanding of impact on catalytic performance can be realized

Automatic processing of multimodal tomography datasets

With the development of fourth-generation high-brightness synchrotrons on the horizon, the already large volume of data that will be collected on imaging and mapping beamlines is set to increase by orders of magnitude. As such, an easy and accessible way of dealing with such large datasets as quickly as possible is required in order to be able to address the core scientific problems during the experimental data collection. Savu is an accessible and flexible big data processing framework that is able to deal with both the variety and the volume of data of multimodal and multidimensional scientific datasets output such as those from chemical tomography experiments on the I18 microfocus scanning beamline at Diamond Light Source

X-ray physico-chemical imaging during activation of cobalt-based Fischer-Tropsch synthesis catalysts

The imaging of catalysts and other functional materials under reaction conditions has advanced significantly in recent years. The combination of the computed tomography (CT) approach with methods such as X-ray diffraction (XRD), X-ray fluorescence (XRF) and X-ray absorption near-edge spectroscopy (XANES) now enables local chemical and physical state information to be extracted from within the interiors of intact materials which are, by accident or design, inhomogeneous. In this work, we follow the phase evolution during the initial reduction step(s) to form Co metal, for Co-containing particles employed as Fischer–Tropsch synthesis (FTS) catalysts; firstly, working at small length scales (approx. micrometre spatial resolution), a combination of sample size and density allows for transmission of comparatively low energy signals enabling the recording of ‘multimodal’ tomography, i.e. simultaneous XRF–CT, XANES–CT and XRD–CT. Subsequently, we show high-energy XRD–CT can be employed to reveal extent of reduction and uniformity of crystallite size on millimetre-sized TiO2 trilobes. In both studies, the CoO phase is seen to persist or else evolve under particular operating conditions and we speculate as to why this is observed

Elucidating the Significance of Copper and Nitrate Speciation in Cu-SSZ-13 for N₂O Formation during NH₃-SCR

Unwanted N2O formation is a problem that has been noted in selective catalytic reduction (SCR) where copper zeolite catalysts are utilized. With its immense global warming potential and long-term stability, elevated atmospheric N2O has already been identified as a future challenge in the war on climate change. This paper explores the phenomenon of N2O formation during NH3-SCR over Cu-SSZ-13 catalysts, which are currently commercialized in automotive emissions control systems, and proposes a link between N2O production and the local copper environment found within the zeolite. To achieve this, a comparison is made between two Cu-SSZ-13 samples with different copper co-ordinations produced via different synthesis methods. A combination of synchrotron X-ray absorption near-edge spectroscopy, UV–vis, Raman, and density functional theory (DFT) is used to characterize the nature of copper species present within each sample. Synchrotron IR microspectroscopy is then used to compare their behavior during SCR under operando conditions and monitor the evolution of nitrate intermediates, which, along with further DFT, informs a mechanistic model for nitrate decomposition pathways. Increased N2O production is seen in the Cu-SSZ-13 sample postulated to contain a linear Cu species, providing an important correlation between the catalytic behavior of Cu-zeolites and the nature of their metal ion loading and speciation

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

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

Unravelling the spatial dependency of the complex solid-state chemistry of Pb in a paint micro-sample from Rembrandt's Homer using XRD-CT

The surface of many Old Master paintings has been affected by the appearance of whitish lead-rich deposits, which are often difficult to fully characterise, thereby hindering conservation. A paint micro-sample from Rembrandt's Homer was imaged using X-ray Diffraction Computed Tomography (XRD-CT) in order to understand the evolving solid-state Pb chemistry from the painting surface and beneath. The surface crust was identified as a complex mixture of lead sulfates. From the S : Pb ratios throughout the paint layer, we can conclude that S is from an external source in the form of SO2, and that the nature of Pb-SO4 product is dependent on the degree of diffusion/absorption of SO2 into the paint layers

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