Three-dimensional microstructure and critical current properties of ultrafine grain Ba(Fe,Co)2As2 bulk superconductors

Shimada, Yusuke; Tokuta, Shinnosuke; Yamanaka, Akinori; Yamamoto, Akiyasu; Konno, Toyohiko J.

doi:10.1016/j.jallcom.2022.166358

Cited by 6 publications

(2 citation statements)

References 33 publications

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“…In recent years, significant progress has been made in modeling polycrystalline materials 31,32 and evaluating grain boundary characteristics 10,[19][20][21] . However, given the sensitivity of superconducting properties to microstructures and chemical compositions [33][34][35][36][37][38] , the intricate interplay through which these factors dictate the resultant characteristics remains contentious. Consequently, the establishment of process design principles to enhance transport current properties in IBS materials is of great interest.…”

Section: Introductionmentioning

confidence: 99%

Superstrength permanent magnets with iron-based superconductors by data- and researcher-driven process design

Yamamoto,

Tokuta,

Ishii

et al. 2024

NPG Asia Mater

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Iron-based high-temperature (high-Tc) superconductors have good potential to serve as materials in next-generation superstrength quasipermanent magnets owing to their distinctive topological and superconducting properties. However, their unconventional high-Tc superconductivity paradoxically associates with anisotropic pairing and short coherence lengths, causing challenges by inhibiting supercurrent transport at grain boundaries in polycrystalline materials. In this study, we employ machine learning to manipulate intricate polycrystalline microstructures through a process design that integrates researcher- and data-driven approaches via tailored software. Our approach results in a bulk Ba0.6K0.4Fe2As2 permanent magnet with a magnetic field that is 2.7 times stronger than that previously reported. Additionally, we demonstrate magnetic field stability exceeding 0.1 ppm/h for a practical 1.5 T permanent magnet, which is a vital aspect of medical magnetic resonance imaging. Nanostructural analysis reveals contrasting outcomes from data- and researcher-driven processes, showing that high-density defects and bipolarized grain boundary spacing distributions are primary contributors to the magnet’s exceptional strength and stability.

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Section: Introductionmentioning

confidence: 99%

Superstrength permanent magnets with iron-based superconductors by data- and researcher-driven process design

Yamamoto,

Tokuta,

Ishii

et al. 2024

NPG Asia Mater

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“…[19][20][21][22] But to properly address the intrinsic mechanism we need to rule out the extrinsic effects caused by impurity phases introduced by processing. Indeed, recent studies have suggested that the intergrain connectivity of Ba122 can be easily degraded by extrinsic factors such as local or global impurity concentrations and GB cracks [23][24][25][26][27][28][29] that can lead to poor reproducibility of the intergrain J c . Oxygen contamination, even at low levels, degrade GB connectivity in K-Ba122.…”

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confidence: 99%

On the mechanisms of J _c increment and degradation in high-J _c Ba122 tapes made by different processing methods

Kametani,

Su,

Tarantini

et al. 2024

Appl. Phys. Express

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We compared the grain and grain boundary (GB) nanostructures in two Ba122 tapes with similarly high Jc. The Ag-sheathed tape made by hot pressing has the larger, more plate-like grains with better c-axis alignment but has more GBs blocked by FeAs and Ba-O. In contrast, the tape made by cold pressing with Ag-Sn/Stainless steel sheath possesses less plate-like grains and weak grain alignment but has more continuous current paths with clean physically well-connected GBs. Our nanostructural comparison emphasizes the strong need to achieve both good grain alignment and clean GB for further Jc improvement of Ba122 tapes.

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Deep learning for three-dimensional segmentation of electron microscopy images of complex ceramic materials

Hirabayashi,

Iga,

Ogawa

et al. 2024

npj Comput Mater

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The microstructure is a critical factor governing the functionality of ceramic materials. Meanwhile, microstructural analysis of electron microscopy images of polycrystalline ceramics, which are geometrically complex and composed of countless crystal grains with porosity and secondary phases, has generally been performed manually by human experts. Objective pixel-based analysis (semantic segmentation) with high accuracy is a simple but critical step for quantifying microstructures. In this study, we apply neural network-based semantic segmentation to secondary electron images of polycrystalline ceramics obtained by three-dimensional (3D) imaging. The deep-learning-based models (e.g., fully convolutional network and U-Net) by employing a dataset based on a 3D scanning electron microscopy with a focused ion beam is found to be able to recognize defect structures characteristic of polycrystalline materials in some cases due to artifacts in electron microscopy imaging. Owing to the training images with improved depth accuracy, the accuracy evaluation function, intersection over union (IoU) values, reaches 94.6% for U-Net. These IoU values are among the highest for complex ceramics, where the 3D spatial distribution of phases is difficult to locate from a 2D image. Moreover, we employ the learned model to successfully reconstruct a 3D microstructure consisting of giga-scale voxel data in a few minutes. The resolution of a single voxel is 20 nm, which is higher than that obtained using a typical X-ray computed tomography. These results suggest that deep learning with datasets that learn depth information is essential in 3D microstructural quantifying polycrystalline ceramic materials. Additionally, developing improved segmentation models and datasets will pave the way for data assimilation into operando analysis and numerical simulations of in situ microstructures obtained experimentally and for application to process informatics.

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Three-dimensional microstructure and critical current properties of ultrafine grain Ba(Fe,Co)2As2 bulk superconductors

Cited by 6 publications

References 33 publications

Superstrength permanent magnets with iron-based superconductors by data- and researcher-driven process design

Superstrength permanent magnets with iron-based superconductors by data- and researcher-driven process design

On the mechanisms of J _c increment and degradation in high-J _c Ba122 tapes made by different processing methods

Deep learning for three-dimensional segmentation of electron microscopy images of complex ceramic materials

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Three-dimensional microstructure and critical current properties of ultrafine grain Ba(Fe,Co)2As2 bulk superconductors

Cited by 6 publications

References 33 publications

Superstrength permanent magnets with iron-based superconductors by data- and researcher-driven process design

Superstrength permanent magnets with iron-based superconductors by data- and researcher-driven process design

On the mechanisms of J c increment and degradation in high-J c Ba122 tapes made by different processing methods

Deep learning for three-dimensional segmentation of electron microscopy images of complex ceramic materials

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On the mechanisms of J _c increment and degradation in high-J _c Ba122 tapes made by different processing methods