Multi-scale microscopy study of 3D morphology and structure of MoNi4/MoO2@Ni electrocatalytic systems for fast water dissociation.

The 3D morphology of hierarchically structured electrocatalytic systems is determined based on multi-scale X-ray computed tomography (XCT), and the crystalline structure of electrocatalyst nanoparticles is characterized using transmission electron microscopy (TEM), supported by X-ray diffraction (XRD) and spatially resolved near-edge X-ray absorption fine structure (NEXAFS) studies. The high electrocatalytic efficiency for hydrogen evolution reaction (HER) of a novel transition-metal-based material system - MoNi4 electrocatalysts anchored on MoO2 cuboids aligned on Ni foam (MoNi4/MoO2@Ni) - is based on advantageous crystalline structures and chemical bonding. High-resolution TEM images and selected-area electron diffraction patterns are used to determine the crystalline structures of MoO2 and MoNi4. Multi-scale XCT provides 3D information of the hierarchical morphology of the MoNi4/MoO2@Ni material system nondestructively: Micro-XCT images clearly resolve the Ni foam and the attached needle-like MoO2 micro cuboids. Laboratory nano-XCT shows that the MoO2 micro cuboids with a rectangular cross-section of 0.5 × 1 µm2 and a length of 10-20 µm are vertically arranged on the Ni foam. MoNi4 nanoparticles with a size of 20-100 nm, positioned on single MoO2 cuboids, were imaged using synchrotron radiation nano-XCT. The application of a deep convolutional neural network (CNN) significantly improves the reconstruction quality of the acquired data.

Deep Learning-based Inaccuracy Compensation in Reconstruction of High Resolution XCT Data.

While X-ray computed tomography (XCT) is pushed further into the micro- and nanoscale, the limitations of various tool components and object motion become more apparent. For high-resolution XCT, it is necessary but practically difficult to align these tool components with sub-micron precision. The aim is to develop a novel reconstruction methodology that considers unavoidable misalignment and object motion during the data acquisition in order to obtain high-quality three-dimensional images and that is applicable for data recovery from incomplete datasets. A reconstruction software empowered by sophisticated correction modules that autonomously estimates and compensates artefacts using gradient descent and deep learning algorithms has been developed and applied. For motion estimation, a novel computer vision methodology coupled with a deep convolutional neural network approach provides estimates for the object motion by tracking features throughout the adjacent projections. The model is trained using the forward projections of simulated phantoms that consist of several simple geometrical features such as sphere, triangle and rectangular. The feature maps extracted by a neural network are used to detect and to classify features done by a support vector machine. For missing data recovery, a novel deep convolutional neural network is used to infer high-quality reconstruction data from incomplete sets of projections. The forward and back projections of simulated geometric shapes from a range of angular ranges are used to train the model. The model is able to learn the angular dependency based on a limited angle coverage and to propose a new set of projections to suppress artefacts. High-quality three-dimensional images demonstrate that it is possible to effectively suppress artefacts caused by thermomechanical instability of tool components and objects resulting in motion, by center of rotation misalignment and by inaccuracy in the detector position without additional computational efforts. Data recovery from incomplete sets of projections result in directly corrected projections instead of suppressing artefacts in the final reconstructed images. The proposed methodology has been proven and is demonstrated for a ball bearing sample. The reconstruction results are compared to prior corrections and benchmarked with a commercially available reconstruction software. Compared to conventional approaches in XCT imaging and data analysis, the proposed methodology for the generation of high-quality three-dimensional X-ray images is fully autonomous. The methodology presented here has been proven for high-resolution micro-XCT and nano-XCT, however, is applicable for all length scales.

Numerical and Experimental Study of the Mechanical Response of Diatom Frustules.

Diatom frustules, with their hierarchical three-dimensional patterned silica structures at nano to micrometer dimensions, can be a paragon for the design of lightweight structural materials. However, the mechanical properties of frustules, especially the species with pennate symmetry, have not been studied systematically. A novel approach combining in situ micro-indentation and high-resolution X-ray computed tomography (XCT)-based finite element analysis (FEA) at the identical sample is developed and applied to Didymosphenia geminata frustule. Furthermore, scanning electron microscopy and transmission electron microscopy investigations are conducted to obtain detailed information regarding the resolvable structures and the composition. During the in situ micro-indentation studies of Didymosphenia geminata frustule, a mainly elastic deformation behavior with displacement discontinuities/non-linearities is observed. To extract material properties from obtained load-displacement curves in the elastic region, elastic finite element method (FEM) simulations are conducted. Young's modulus is determined as 31.8 GPa. The method described in this paper allows understanding of the mechanical behavior of very complex structures.

Anisotropy of Mechanical Properties of Pinctada margaritifera Mollusk Shell.

The mechanical properties such as compressive strength and nanohardness were investigated for Pinctada margaritifera mollusk shells. The compressive strength was evaluated through a uniaxial static compression test performed along the load directions parallel and perpendicular to the shell axis, respectively, while the hardness and Young modulus were measured using nanoindentation. In order to observe the crack propagation, for the first time for such material, the in-situ X-ray microscopy (nano-XCT) imaging (together with 3D reconstruction based on the acquired images) during the indentation tests was performed. The results were compared with these obtained during the micro-indentation test done with the help of conventional Vickers indenter and subsequent scanning electron microscopy observations. The results revealed that the cracks formed during the indentation start to propagate in the calcite prism until they reach a ductile organic matrix where most of them are stopped. The obtained results confirm a strong anisotropy of both crack propagation and the mechanical strength caused by the formation of the prismatic structure in the outer layer of P. margaritifera shell.

3D Diatom-Designed and Selective Laser Melting (SLM) Manufactured Metallic Structures.

Diatom frustules, with their diverse three-dimensional regular silica structures and nano- to micrometer dimensions, represent perfect model systems for biomimetic fabrication of materials and devices. The structure of a frustule of the diatom Didymosphenia geminata was nondestructively visualized using nano X-ray computed tomography (XCT) and transferred into a CAD file for the first time. Subsequently, this CAD file was used as the input for an engineered object, which was manufactured by applying an additive manufacturing technique (3D Selective Laser Melting, SLM) and using titanium powder. The self-similarity of the natural and the engineered objects was verified using nano and micro XCT. The biomimetic approach described in this paper is a proof-of-concept for future developments in the scaling-up of manufacturing based on special properties of microorganisms.