Microscopy (Oxford, England)最新文献_第3页

Correction to: Low-dose measurement of electric potential distribution in organic light-emitting diode by phase-shifting electron holography with 3D tensor decomposition. 修正：用三维张量分解相移电子全息法测量有机发光二极管的低剂量电位分布。

Microscopy (Oxford, England) Pub Date : 2024-12-28 DOI: 10.1093/jmicro/dfae058

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Correction to: Structures of multisubunit membrane complexes with the CRYO ARM 200. 修正：多亚基膜复合物的结构与CRYO ARM 200。

Microscopy (Oxford, England) Pub Date : 2024-12-26 DOI: 10.1093/jmicro/dfae057

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Momentum-resolved EELS and CL study on 1D-plasmonic crystal prepared by FIB method. 用 FIB 方法制备的一维等离子晶体的动量分辨 EELS 和 CL 研究。

Microscopy (Oxford, England) Pub Date : 2024-12-05 DOI: 10.1093/jmicro/dfae022

Akira Yasuhara, Masateru Shibata, Wakaba Yamamoto, Izzah Machfuudzoh, Sotatsu Yanagimoto, Takumi Sannomiya

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Role of lipids in the organization of tight junction. 脂质在紧密连接组织中的作用。

Microscopy (Oxford, England) Pub Date : 2024-12-05 DOI: 10.1093/jmicro/dfae039

Junichi Ikenouchi, Kenta Shigetomi

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Visualization of Bacillus subtilis spore structure and germination using quick-freeze deep-etch electron microscopy. 利用速冻深描电子显微镜观察枯草芽孢杆菌孢子的结构和萌发。

Microscopy (Oxford, England) Pub Date : 2024-12-05 DOI: 10.1093/jmicro/dfae023

Kiran Jalil, Yuhei O Tahara, Makoto Miyata

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Correction to: Observation and quantitative analysis of dislocations in steel using electron channeling contrast imaging method with precise control of electron beam incident direction. 更正为利用精确控制电子束入射方向的电子通道对比成像方法观测和定量分析钢中的位错。

Microscopy (Oxford, England) Pub Date : 2024-12-05 DOI: 10.1093/jmicro/dfae037

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Bayesian inference of atomic column positions in scanning transmission electron microscopy images. 扫描透射电子显微镜图像中原子柱位置的贝叶斯推断。

Microscopy (Oxford, England) Pub Date : 2024-12-05 DOI: 10.1093/jmicro/dfae025

Yuki Nomura, Satoshi Anada, Shunsuke Kobayashi

{"title":"Bayesian inference of atomic column positions in scanning transmission electron microscopy images.","authors":"Yuki Nomura, Satoshi Anada, Shunsuke Kobayashi","doi":"10.1093/jmicro/dfae025","DOIUrl":"10.1093/jmicro/dfae025","url":null,"abstract":"Atomic-resolution scanning transmission electron microscopy combined with two-dimensional Gaussian fitting enables the accurate and precise identification of atomic column positions within a few picometers. The measurement performance significantly depends on the signal-to-noise ratio of the atomic columns. In areas with low signal-to-noise ratios, such as near surfaces, the measurement performance was lower than that of the bulk. However, previous studies evaluated the accuracy and precision only in bulk areas, underscoring the need for a method that quantitatively evaluates the accuracy and precision of each atomic column position with various signal-to-noise ratios. This study introduced Bayesian inference to assess the accuracy and precision of determining individual atomic column positions under various signals. We applied this method to simulated and experimental images and demonstrated its effectiveness in identifying statistically significant displacements, particularly near surfaces with signal degradation. The use of vector maps and kernel density estimate plots obtained from Bayesian inference provided a probabilistic understanding of the atom displacement. Therefore, this study highlighted the potential benefits of Bayesian inference in high-resolution imaging to reveal material properties.","PeriodicalId":74193,"journal":{"name":"Microscopy (Oxford, England)","volume":" ","pages":"481-487"},"PeriodicalIF":0.0,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140900800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development of silicon-on-insulator direct electron detector with analog memories in pixels for sub-microsecond imaging. 开发用于亚微秒成像的带像素模拟存储器的硅绝缘体直接电子探测器。

Microscopy (Oxford, England) Pub Date : 2024-12-05 DOI: 10.1093/jmicro/dfae029

Takafumi Ishida, Kosei Sugie, Toshinobu Miyoshi, Yuichi Ishida, Koh Saitoh, Yasuo Arai, Makoto Kuwahara

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Sandwich freezing and freeze substitution of Arabidopsis plant tissues for electron microscopy. 拟南芥植物组织的夹心冷冻和冷冻替代，用于电子显微镜观察。

Microscopy (Oxford, England) Pub Date : 2024-12-05 DOI: 10.1093/jmicro/dfae032

Masashi Yamaguchi, Mayuko Sato, Azusa Takahashi-Nakaguchi, Michiyo Okamoto, Kiminori Toyooka, Hiroji Chibana

引用次数: 0

Fast computational approach with prior dimension reduction for three-dimensional chemical component analysis using CT data of spectral imaging. 利用光谱成像 CT 数据进行三维化学成分分析的先验降维快速计算方法。

Microscopy (Oxford, England) Pub Date : 2024-12-05 DOI: 10.1093/jmicro/dfae027

Motoki Shiga, Taisuke Ono, Kenichi Morishita, Keiji Kuno, Nanase Moriguchi

{"title":"Fast computational approach with prior dimension reduction for three-dimensional chemical component analysis using CT data of spectral imaging.","authors":"Motoki Shiga, Taisuke Ono, Kenichi Morishita, Keiji Kuno, Nanase Moriguchi","doi":"10.1093/jmicro/dfae027","DOIUrl":"10.1093/jmicro/dfae027","url":null,"abstract":"Spectral image (SI) measurement techniques, such as X-ray absorption fine structure (XAFS) imaging and scanning transmission electron microscopy (STEM) with energy-dispersive X-ray spectroscopy (EDS) or electron energy loss spectroscopy (EELS), are useful for identifying chemical structures in composite materials. Machine-learning techniques have been developed for automatic analysis of SI data and their usefulness has been proven. Recently, an extended measurement technique combining SI with a computed tomography (CT) technique (CT-SI), such as CT-XAFS and STEM-EDS/EELS tomography, was developed to identify the three-dimensional (3D) structures of chemical components. CT-SI analysis can be conducted by combining CT reconstruction algorithms and chemical component analysis based on machine-learning techniques. However, this analysis incurs high-computational costs owing to the size of the CT-SI datasets. To address this problem, this study proposed a fast computational approach for 3D chemical component analysis in an unsupervised learning setting. The primary idea for reducing the computational cost involved compressing the CT-SI data prior to CT computation and performing 3D reconstruction and chemical component analysis on the compressed data. The proposed approach significantly reduced the computational cost without losing information about the 3D structure and chemical components. We experimentally evaluated the proposed approach using synthetic and real CT-XAFS data, which demonstrated that our approach achieved a significantly faster computational speed than the conventional approach while maintaining analysis performance. As the proposed procedure can be implemented with any CT algorithm, it is expected to accelerate 3D analyses with sparse regularized CT algorithms in noisy and sparse CT-SI datasets.","PeriodicalId":74193,"journal":{"name":"Microscopy (Oxford, England)","volume":" ","pages":"488-498"},"PeriodicalIF":0.0,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140961075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0