Electron tomography imaging methods with diffraction contrast for materials research

Model-based iterative reconstruction with adaptive regularization for artifact reduction in electron tomography

Obtaining high-quality 3D reconstructions from electron tomography of crystalline particles embedded in lighter support elements is crucial for various material systems such as catalysts for fuel cell applications. However, significant challenges arise due to the limited tilt range, sparse and low signal-to-noise ratio of the measurements. In addition, small metal particles can cause strong streaking and shading artifacts in the 3D reconstructions when using conventional reconstruction algorithms due to the presence of Bragg diffraction and the large scattering cross-section difference between the materials of the particles and the background support regions. These artifacts lead to errors in the downstream characterization affecting extraction of critical features such as the size of the metal particles, their distribution and the volume of the lighter support regions. In this paper, we present a two-stage algorithm based on metal artifact reduction, utilizing model-based iterative reconstruction methods with adaptive adjustment of regularization parameters. Our approach yields high-quality 3D reconstructions compared to traditional algorithms, accurately capturing both the metal particles as well as the background support. We demonstrate the effectiveness of our algorithm through simulated and experimental bright-field electron tomography data, showing significant improvements in reconstruction quality compared to traditional methods.

Integrating machine learning with advanced processing and characterization for polycrystalline materials: a methodology review and application to iron-based superconductors

In this review, we present a new set of machine learning-based materials research methodologies for polycrystalline materials developed through the Core Research for Evolutionary Science and Technology project of the Japan Science and Technology Agency. We focus on the constituents of polycrystalline materials (i.e. grains, grain boundaries [GBs], and microstructures) and summarize their various aspects (experimental synthesis, artificial single GBs, multiscale experimental data acquisition via electron microscopy, formation process modeling, property description modeling, 3D reconstruction, and data-driven design methods). Specifically, we discuss a mechanochemical process involving high-energy milling, in situ observation of microstructural formation using 3D scanning transmission electron microscopy, phase-field modeling coupled with Bayesian data assimilation, nano-orientation analysis via scanning precession electron diffraction, semantic segmentation using neural network models, and the Bayesian-optimization-based process design using BOXVIA software. As a proof of concept, a researcher- and data-driven process design methodology is applied to a polycrystalline iron-based superconductor to evaluate its bulk magnet properties. Finally, future challenges and prospects for data-driven material development and iron-based superconductors are discussed.

MT-TN mutations lead to progressive mitochondrial encephalopathy and promotes mitophagy

Mitochondrial encephalopathy is a neurological disorder caused by impaired mitochondrial function and energy production. One of the genetic causes of this condition is the mutation of MT-TN, a gene that encodes the mitochondrial transfer RNA (tRNA) for asparagine. MT-TN mutations affect the stability and structure of the tRNA, resulting in reduced protein synthesis and complex enzymatic deficiency of the mitochondrial respiratory chain. Our patient cohort manifests with epileptic encephalopathy, ataxia, hypotonia, and bilateral basal ganglia calcification, which differs from previously reported cases. MT-TN mutation deficiency leads to decreased basal and maximal oxygen consumption rates, disrupted spare respiratory capacity, declined mitochondrial membrane potential, and impaired ATP production. Moreover, MT-TN mutations promote mitophagy, a process of selective degradation of damaged mitochondria by autophagy. Excessive mitophagy further leads to mitochondrial biogensis as a compensatory mechanism. In this study, we provided evidence of pathogenicity for two MT-TN mutations, m.5688 T > C and m.G5691A, explored the molecular mechanisms, and summarized the clinical manifestations of MT-TN mutations. Our study expanded the genotype and phenotypic spectrum and provided new insight into mt-tRNA (Asn)-associated mitochondrial encephalopathy.

A new method for obtaining the magnetic shape anisotropy directly from electron tomography images

A new methodology to obtain magnetic information on magnetic nanoparticle (MNP) systems via electron tomography techniques is reported in this work. The new methodology is implemented in an under-development software package called Magn3t, written in Python and C++. A novel image-filtering technique that reduces the highly undesired diffraction effects in the tomography tilt-series has been also developed in order to increase the reliability of the correlations between morphology and magnetism. Using the Magn3t software, the magnetic shape anisotropy magnitude and direction of magnetite nanoparticles has been extracted for the first time directly from transmission electron tomography.

Semi-Automated, Object-Based Tomography of Dislocation Structures

The characterization of the three-dimensional arrangement of dislocations is important for many analyses in materials science. Dislocation tomography in transmission electron microscopy is conventionally accomplished through intensity-based reconstruction algorithms. Although such methods work successfully, a disadvantage is that they require many images to be collected over a large tilt range. Here, we present an alternative, semi-automated object-based approach that reduces the data collection requirements by drawing on the prior knowledge that dislocations are line objects. Our approach consists of three steps: (1) initial extraction of dislocation line objects from the individual frames, (2) alignment and matching of these objects across the frames in the tilt series, and (3) tomographic reconstruction to determine the full three-dimensional configuration of the dislocations. Drawing on innovations in graph theory, we employ a node-line segment representation for the dislocation lines and a novel arc-length mapping scheme to relate the dislocations to each other across the images in the tilt series. We demonstrate the method for a dataset collected from a dislocation network imaged by diffraction-contrast scanning transmission electron microscopy. Based on these results and a detailed uncertainty analysis for the algorithm, we discuss opportunities for optimizing data collection and further automating the method.