Correlative Tomography: Three Dimensional Multiscale Imaging and Modelling of Hierarchical Porous Materials

Heterogeneous catalyst based pellets typify a material where functionality is dependant on hierarchical pore structures spanning many orders of magnitude from nanometers up to tens of microns. The total activity, selectivity and lifetime of catalyst based pellets depends on the ability of molecules to flow through a large pellet bed (m), into the pellets (mm) and their pore structure (μm-nm) to/from the active sites. Three dimensional imaging techniques such as tomography allow for the direct characterisation and quantification of pore structures. However, the field of view in tomography decreases as resolution increases. This work circumvents this issue with multiscale tomography (MT) combining x-ray microtomography (XMT), dual beam focused ion beam tomography (DB-FIB) and electron tomography (ET) to probe porous pellet based catalysts. The results show MT as a viable method that offers new insights into the quantification and behaviour of pellet based catalysts across large length scales, all in three dimensions (3D), that no single tomographic technique can adequately capture. MT was successfully used in the characterising of pore sizes, distributions, structures and spatial relationships and this was compared to existing multiscale characterisation techniques to illustrate the new insights that can be obtained. The pore structures were meshed and modeled using MT data to provide results for understanding the transport properties scaled up from the nanometre length scale to the packed bed, through pellet based catalysts produced under different manufacturing conditions. The results show the very strong dependence on the calcining temperature which is important for designing better catalysts in future. The tomography data was also used to determine thermal/mechanical stresses at both a pellet and pellet bed level. Although many stresses are compressive; the packing of the pellets creates local tensile stresses and a potential cause for pellet failure through internal flaws at relatively low loads. In summary, multiscale tomography was demonstrated to be a viable method for obtaining new insights for the development of pellet based catalysts by both improved quantification and allows for the first time direct 3D multiscale simulation of transport and mechanical properties across multiple scales from nanometers to metres to catalyst pellets in beds

Tomografia estendida : do básico até o mapeamento de cérebro de camundongos

Orientador: Mateus Borba CardosoTese (doutorado) - Universidade Estadual de Campinas, Instituto de Física Gleb WataghinResumo: Esta tese apresentará uma introdução a imagens de raios-x e como adquirir e processar imagens usando linhas de luz síncrotron. Apresentará os desafios matemáticos e técnicos para reconstruir amostras em três dimensões usando a reconstrução de Tomografia Computadorizada, uma técnica conhecida como CT. Esta técnica tem seu campo de visão limitado ao tamanho da câmera e ao tamanho da iluminação. Uma técnica para ampliar esse campo de visão vai ser apresentada e os desafios técnicos envolvidos para que isso aconteça. Um \textit{pipeline} é proposto e todos os algoritmos necessários foram empacotados em um pacote python chamado Tomosaic. A abordagem baseia-se em adquirir tomogramas parciais em posiçoes pré definidas e depois mesclar os dados em um novo conjunto de dados. Duas maneiras possíveis são apresentadas para essa mescla, uma no domínio das projeções e uma no domínio dos sinogramas. Experimentos iniciais serão então usadas para mostrar que o método proposto funciona com computadores normais. A técnica será aplicada mais tarde para pesquisar a anatomia de cérebros de camundongo completos. Um estudo será apresentado de como obter informação em diferentes escalas do cérebro completo do rato utilizando raios-xAbstract: This thesis will present an introduction to x-ray images and how to acquire and thread images using synchrotron beamlines. It will present the mathematical and technical challenges to reconstruct samples in three dimensions using Computed Tomography reconstruction, a technique known as CT. This technique has a field of view bounded to the camera size and the illumination size. A technique to extended this field of view is going to be presented and the technical challenges involved in order for that to happen will be described. A pipeline is proposed and all the necessary algorithms are contained into a python packaged called Tomosaic. The approach relies on acquired partial tomogram data in a defined grid and later merging the data into a new dataset. Two possible ways are presented in order to that: in the projection domain, and in the sinogram domain. Initial experiments will then be used to show that the pipeline works with normal computers. The technique will be later applied to survey the whole anatomy of whole mouse brains. A study will be shown of how to get the complete range of scales of the mouse brain using x-ray tomography at different resolutionsDoutoradoFísicaDoutor em Ciências163304/2013-01247445/2013, 1456912/2014CNPQCAPE

Bridging Nano and Micro-scale X-ray Tomography for Battery Research by Leveraging Artificial Intelligence

X-ray Computed Tomography (X-ray CT) is a well-known non-destructive imaging technique where contrast originates from the materials' absorption coefficients. Novel battery characterization studies on increasingly challenging samples have been enabled by the rapid development of both synchrotron and laboratory-scale imaging systems as well as innovative analysis techniques. Furthermore, the recent development of laboratory nano-scale CT (NanoCT) systems has pushed the limits of battery material imaging towards voxel sizes previously achievable only using synchrotron facilities. Such systems are now able to reach spatial resolutions down to 50 nm. Given the non-destructive nature of CT, in-situ and operando studies have emerged as powerful methods to quantify morphological parameters, such as tortuosity factor, porosity, surface area, and volume expansion during battery operation or cycling. Combined with powerful Artificial Intelligence (AI)/Machine Learning (ML) analysis techniques, extracted 3D tomograms and battery-specific morphological parameters enable the development of predictive physics-based models that can provide valuable insights for battery engineering. These models can predict the impact of the electrode microstructure on cell performances or analyze the influence of material heterogeneities on electrochemical responses. In this work, we review the increasing role of X-ray CT experimentation in the battery field, discuss the incorporation of AI/ML in analysis, and provide a perspective on how the combination of multi-scale CT imaging techniques can expand the development of predictive multiscale battery behavioral models.Comment: 33 pages, 5 figure

X-ray and FTIR \u3bc-CTs for morphological and chemical characterization of eco-sustainable insulating foams

Here it is reported a multidisciplinary approach based on tomography and infrared techniques applied to the characterization of tannin porous rigid foams, potentially usable as new insulating materials in green building technology. With conventional x-ray tomography it was possible to preliminary evaluate the homogeneity of the samples at low resolution, while then, thanks to the synchrotron source, it was possible to obtain more detailed information at a micro-scale level. At the same time chemical characterization was done through Fourier Transform infrared (FTIR) imaging. Conventionally, FTIR imaging is limited to a planar projection, not considering the 3D structure of the material. To avoid this limitation, a FTIR 3D-tomography setup was built and the foams characterized by a chemical point of view. The idea is to directly correlate these data with the 3D-structural information obtained with the x-ray computed tomography exploiting the synchrotron radiation as source, allowing a complete characterization of the material morphology and chemistry at the microscale

Effective Constitutive Response of Sustainable Next Generation Infrastructure Materials through High-Fidelity Experiments and Numerical Simulation

Design of novel infrastructure materials requires a proper understanding of the influence of microstructure on the desired performance. The priority is to seek new and innovative ways to develop sustainable infrastructure materials using natural resources and industrial solid wastes in a manner that is ecologically sustainable and yet economically viable. Structural materials are invariably designed based on mechanical performance. Accurate prediction of effective constitutive behavior of highly heterogeneous novel structural materials with multiple microstructural phases is a challenging task. This necessitates reliable classification and characterization of constituent phases in terms of their volume fractions, size distributions and intrinsic elastic properties, coupled with numerical homogenization technique. This paper explores a microstructure-guided numerical framework that derives inputs from nanoindentation and synchrotron x-ray tomography towards the prediction of effective constitutive response of novel sustainable structural materials so as to enable microstructure-guided design

Effective Constitutive Response of Sustainable Next Generation Infrastructure Materials through High-Fidelity Experiments and Numerical Simulation

abstract: Design of novel infrastructure materials requires a proper understanding of the influence of microstructure on the desired performance. The priority is to seek new and innovative ways to develop sustainable infrastructure materials using natural resources and industrial solid wastes in a manner that is ecologically sustainable and yet economically viable. Structural materials are invariably designed based on mechanical performance. Accurate prediction of effective constitutive behavior of highly heterogeneous novel structural materials with multiple microstructural phases is a challenging task. This necessitates reliable classification and characterization of constituent phases in terms of their volume fractions, size distributions and intrinsic elastic properties, coupled with numerical homogenization technique. This paper explores a microstructure-guided numerical framework that derives inputs from nanoindentation and synchrotron x-ray tomography towards the prediction of effective constitutive response of novel sustainable structural materials so as to enable microstructure-guided design

Illuminating the Brain With X-Rays: Contributions and Future Perspectives of High-Resolution Microtomography to Neuroscience

The assessment of three-dimensional (3D) brain cytoarchitecture at a cellular resolution remains a great challenge in the field of neuroscience and constant development of imaging techniques has become crucial, particularly when it comes to offering direct and clear obtention of data from macro to nano scales. Magnetic resonance imaging (MRI) and electron or optical microscopy, although valuable, still face some issues such as the lack of contrast and extensive sample preparation protocols. In this context, x-ray microtomography (μCT) has become a promising non-destructive tool for imaging a broad range of samples, from dense materials to soft biological specimens. It is a new supplemental method to be explored for deciphering the cytoarchitecture and connectivity of the brain. This review aims to bring together published works using x-ray μCT in neurobiology in order to discuss the achievements made so far and the future of this technique for neuroscience

In situ characterization of delamination and crack growth of a CGO–LSM multi-layer ceramic sample investigated by X-ray tomographic microscopy

The densification, delamination and crack growth behavior in a Ce$_{0.9}$Gd$_{0.1}$O$_{1.95}$ (CGO) and (La$_{0.85}$Sr$_{0.15})_{0.9}$MnO$_{3}$ (LSM) multi-layer ceramic sample was studied using in situ X-ray tomographic microscopy (microtomography), to investigate the critical dynamics of crack propagation and delamination in a multilayered sample. Naturally occurring defects, caused by the sample preparation process, are shown not to be critical in sample degradation. Instead defects are nucleated during the debinding step. Crack growth is significantly faster along the material layers than perpendicular to them, and crack growth and delamination only accelerates when sintering occurs.Comment: 9 pages, 8 figure

Large-scale grid-enabled lattice-Boltzmann simulations of complex fluid flow in porous media and under shear

Well designed lattice-Boltzmann codes exploit the essentially embarrassingly parallel features of the algorithm and so can be run with considerable efficiency on modern supercomputers. Such scalable codes permit us to simulate the behaviour of increasingly large quantities of complex condensed matter systems. In the present paper, we present some preliminary results on the large scale three-dimensional lattice-Boltzmann simulation of binary immiscible fluid flows through a porous medium derived from digitised x-ray microtomographic data of Bentheimer sandstone, and from the study of the same fluids under shear. Simulations on such scales can benefit considerably from the use of computational steering and we describe our implementation of steering within the lattice-Boltzmann code, called LB3D, making use of the RealityGrid steering library. Our large scale simulations benefit from the new concept of capability computing, designed to prioritise the execution of big jobs on major supercomputing resources. The advent of persistent computational grids promises to provide an optimal environment in which to deploy these mesoscale simulation methods, which can exploit the distributed nature of compute, visualisation and storage resources to reach scientific results rapidly; we discuss our work on the grid-enablement of lattice-Boltzmann methods in this context.Comment: 17 pages, 6 figures, accepted for publication in Phil.Trans.R.Soc.Lond.