Correction for 'Supported metal nanoparticles on porous materials. Methods and applications' by Robin J. White et al., Chem. Soc. Rev., 2009, 38, 481-494, https://doi.org/10.1039/B802654H.

Journal Article A Multiscale, Correlative, Air Free Workflow for the Analysis of Li Distribution in Batteries via ToF-SIMS Get access Stephen T Kelly, Stephen T Kelly Carl Zeiss Research Microscopy Solutions, Oberkochen, Germany Corresponding author: steve.kelly@zeiss.com Search for other works by this author on: Oxford Academic Google Scholar Robin White, Robin White Carl Zeiss Research Microscopy Solutions, Oberkochen, Germany Search for other works by this author on: Oxford Academic Google Scholar Benjamin Tordoff, Benjamin Tordoff Carl Zeiss Research Microscopy Solutions, Oberkochen, Germany Search for other works by this author on: Oxford Academic Google Scholar Sebastian Schadler, Sebastian Schadler Carl Zeiss Research Microscopy Solutions, Oberkochen, Germany Search for other works by this author on: Oxford Academic Google Scholar Thomas Vorauer, Thomas Vorauer Materials Center Leoben Forschung GmbH, Leoben, Austria Search for other works by this author on: Oxford Academic Google Scholar Bernd Fuchsbichler, Bernd Fuchsbichler Varta Innovation GmbH, Graz, Austria Search for other works by this author on: Oxford Academic Google Scholar Stefan Koller, Stefan Koller Varta Innovation GmbH, Graz, Austria Search for other works by this author on: Oxford Academic Google Scholar Roland Brunner Roland Brunner Materials Center Leoben Forschung GmbH, Leoben, Austria Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 28, Issue S1, 1 August 2022, Pages 866–867, https://doi.org/10.1017/S1431927622003841 Published: 01 August 2022

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A novel automated workflow for the recovery of image resolution using deep convolutional neural networks (CNNs) trained using spatially registered multiscale data is presented. Spatial priors, coupled with high order voxel-based image registration, are used to correct for uncertainties in image magnification and position. A network is then trained to remove the effects of point spread from the low-resolution data, improving resolution while reducing image noise & artefact levels. While benchmarking on real materials, including biological, materials science and electronics samples, we find that resolution recovery improves quantitative and qualitative measurements, even if certain image details cannot be easily identified from the original low-resolution data.

Journal Article Multimodal 3D Characterisation of Carbon-based Perovskite Solar Cells Get access Jebin Jestine, Jebin Jestine Faculty of Science and Engineering, Swansea University, Swansea, United Kingdom Corresponding authors: 872955@swansea.ac.uk & r.johnston@swansea.ac.uk Search for other works by this author on: Oxford Academic Google Scholar Richard E Johnston, Richard E Johnston Faculty of Science and Engineering, Swansea University, Swansea, United Kingdom Corresponding authors: 872955@swansea.ac.uk & r.johnston@swansea.ac.uk Search for other works by this author on: Oxford Academic Google Scholar Cameron Pleydell Pearce, Cameron Pleydell Pearce Faculty of Science and Engineering, Swansea University, Swansea, United Kingdom Search for other works by this author on: Oxford Academic Google Scholar Pete Davies, Pete Davies Faculty of Science and Engineering, Swansea University, Swansea, United Kingdom Search for other works by this author on: Oxford Academic Google Scholar Trystan Watson, Trystan Watson Faculty of Science and Engineering, Swansea University, Swansea, United Kingdom Search for other works by this author on: Oxford Academic Google Scholar Simone Meroni, Simone Meroni Faculty of Science and Engineering, Swansea University, Swansea, United Kingdom Search for other works by this author on: Oxford Academic Google Scholar Carys Worsley, Carys Worsley Faculty of Science and Engineering, Swansea University, Swansea, United Kingdom Search for other works by this author on: Oxford Academic Google Scholar Raptis Dimitrios, Raptis Dimitrios Faculty of Science and Engineering, Swansea University, Swansea, United Kingdom Search for other works by this author on: Oxford Academic Google Scholar Tom Dunlop, Tom Dunlop Faculty of Science and Engineering, Swansea University, Swansea, United Kingdom Search for other works by this author on: Oxford Academic Google Scholar Ria Mitchell, Ria Mitchell Sheffield Tomography Centre (STC), The University of Sheffield, Sheffield, United Kingdom Search for other works by this author on: Oxford Academic Google Scholar ... Show more Tobias Volkenandt, Tobias Volkenandt Carl Zeiss Microscopy GmbH, Oberkochen, Germany Search for other works by this author on: Oxford Academic Google Scholar Ben Tordoff, Ben Tordoff Carl Zeiss Microscopy GmbH, Oberkochen, Germany Search for other works by this author on: Oxford Academic Google Scholar Stephen Kelly, Stephen Kelly Carl Zeiss X-ray Microscopy, Pleasanton, CA, USA Search for other works by this author on: Oxford Academic Google Scholar Robin White, Robin White Carl Zeiss X-ray Microscopy, Pleasanton, CA, USA Search for other works by this author on: Oxford Academic Google Scholar Mansoureh Norouzi Rad Mansoureh Norouzi Rad Carl Zeiss X-ray Microscopy, Pleasanton, CA, USA Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 28, Issue S1, 1 August 2022, Pages 204–206, https://doi.org/10.1017/S1431927622001684 Published: 01 August 2022

This study demonstrates the benefit of convolutional neural networks to accurately classify the different materials of proton exchange membrane fuel cells using X-ray micro-computed tomography. Nineteen 2D orthoslices from a 3D tomography dataset were segmented with high quality and used to train a novel UResNet convolutional neural network (CNN) to segment the complete volume. The results were compared with a 3D manual segmentation performed under time constraints. The CNN segmented all phases with equal or greater accuracy in comparison to the manual segmentation. In particular, the CNN excelled in separating the carbon fibres and binder phase in the gas diffusion layer, which is usually completely avoided due to difficulty. Further, permeability calculations were performed on the binder void space for both segmentations, with the CNN displaying realistic results. Therefore, CNNs have been shown to be a viable and valuable method in segmenting such fuel cells with increased efficiency and accuracy. (c) 2022 Elsevier Ltd. All rights reserved.

The Cover Feature shows a generalised illustration of the dynamics of chemical vapour deposition for the preparation of proton exchange membranes. More information can be found in the Review by N. Bellomo et al.

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Fuel cells will play a critical role in a renewable energy powered society, whether in mobility or stationary applications. Certain challenges remain to be addressed if performance and cost dynamics of these systems is to achieve large scale, high unit number roll out. This review deals with polymer electrolyte membrane-based fuel cells with a specific focus on membrane materials and their innovative fabrication based on chemical vapor deposition (CVD) approaches. These deposition techniques are introduced, as are the potential improvements relating to cell performance, namely proton conductivity and stability. The potential benefits of applying CVD-based synthesis in the fabrication of polymer electrolytes is also discussed with respect to future fuel cell development.

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Graphite is a key material in the design and operation of a wide range of nuclear reactors because of its attractive combination of thermal, mechanical, and neutron interaction properties. In all its applications, the microstructural evolution of nuclear graphite under operating conditions will strongly influence reactor lifetime and performance. However, measuring the 3D microstructural characteristics of nuclear graphite has traditionally faced many challenges. X-ray tomographic techniques face limitations in achievable resolution on bulk (mm-sized) specimens while serial sectioning techniques like FIB-SEM struggle to achieve adequate milling rates for tomographic imaging over representative volumes. To address these shortcomings, we present here a multiscale, targeted, correlative microstructural characterization workflow for nuclear graphite employing micro-scale and nano-scale x-ray microscopy with a connected laser milling step in between the two modalities. We present details of the microstructure, including porosity analysis, spanning orders of magnitude in feature size for nuclear graphite samples including IG-110.

Myelination in the central nervous system depends on interactions between axons and oligodendrocyte precursor cells (OPCs). Action potentials in an axon can be followed by release of biologically active substances, like glutamate, which can instruct OPCs to start myelination. Myelin Basic Protein (MBP) is an "executive molecule of myelin" required for the formation of compact myelin. As cells of the oligodendrocyte lineage (OLCs) are capable of producing MBP in pure oligodendrocyte cultures, i.e. without neurons, we investigated Ca2+ signaling in developing OLCs in cultures. We show that spontaneous Ca2+ transients (CTs) occur at very low frequency in both bipolar OPCs and mature oligodendrocytes. In contrast immature OLCs (imOLCs), cells with several thick processes, demonstrate a relatively high frequency of CTs. Moreover, CT frequency in imOLC processes is much higher as compared with the somatic CT frequency. Somatic CTs are almost completely blocked by thapsigargin, an antagonist of sarco-(endo-) plasmic reticulum Ca2+ ATPase, and ryanodine, a blocker of ryanodine receptors, indicating an involvement of Ca2+ release from the endoplasmic reticulum. Ryanodine strongly reduces CT frequency in imOLC processes. Ouabain, an antagonist of Na+, K+-ATPase (NKA), applied at low concentration increases CT frequency, while KB-R7943, a blocker of reverse mode of Na+, Ca2+ exchanger (NCX), decreases CT frequency. We suggest that local RyR-NCX-(NKA?) interaction might underlie the generation of CTs in imOLC in the absence of neurons, and this activity influences oligodendrocyte maturation.

AbstractDas Vergrößern des Recyclingstroms erfordert einen neuen Ansatz, der über das Schmelzen und Umformen hinausgeht. Es gibt einige Techniken des chemischen Recyclings mit dem Potential, bestehende Recyclingmöglichkeiten zu ergänzen. In diesem Aufsatz sind die Methoden des chemischen Recyclings dargestellt und anhand einer Ökobilanz bewertet, ergänzt durch eine Aufzählung von Prozessen und Firmen in diesem Bereich. Wir zeigen, dass bestimmte Techniken besonders für spezifische Müllströme geeignet sind und dass nur eine Kombination aus den vorhandenen Methoden geeignet ist, das Kunststoffmüllproblem zu lösen. Aktuelle Forschung sollte realistischeren und weniger reinen Mischströmen größere Aufmerksamkeit widmen, während Trenn‐ und Sortierprozesse z. B. durch effektivere Regularien verbessert werden müssen. Dieser Aufsatz soll zur Entwicklung von Verfahren inspirieren, mit denen hochwertige Produkte hergestellt werden können, die die Kreislaufwirtschaft antreiben, indem sie nötige Wirtschaftsanreize und Erleichterungen für die Umwelt bieten.

Influencing stability and performance through directing nitrogen-doping in carbon support materials.

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