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相关概念视频

Overview of Electron Microscopy01:25

Overview of Electron Microscopy

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The wavelengths of visible light ultimately limit the maximum theoretical resolution of images created by light microscopes. Most light microscopes can only magnify 1000X, and a few can magnify up to 1500X. Electrons, like electromagnetic radiation, can behave like waves, but with wavelengths of 0.005 nm, they produce significantly greater resolution up to 0.05 nm as compared to 500 nm for visible light. An electron microscope (EM) can create a sharp image that is magnified up to 2,000,000X.
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Transmission Electron Microscopy01:15

Transmission Electron Microscopy

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In 1931, physicist Ernst Ruska—building on the idea that magnetic fields can direct an electron beam just as lenses can direct a beam of light in an optical microscope—developed the first prototype of the electron microscope. This development led to the development of the field of electron microscopy. In the transmission electron microscope (TEM), electrons are produced by a hot tungsten element and accelerated by a potential difference in an electron gun, which gives them up to 400...
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Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

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Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...
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相关实验视频

Updated: May 14, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
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Deep Learning-Based Segmentation of Cryo-Electron Tomograms

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一个多层次的深度学习模型,用于从低信号噪声比传输电子显微镜图像中识别原子.

Yanyu Lin1, Zhangyuan Yan2, Chi Shing Tsang2

  • 1School of Future Technology South China University of Technology Guangzhou 510641 China.

Small science
|April 11, 2025
PubMed
概括
此摘要是机器生成的。

一个深度神经网络AtomID-Net准确地检测噪音扫描传输电子显微镜图像中的原子位置. 这一进步改善了材料科学中的原子结构分析,克服了传统方法的局限性.

关键词:
这就是U-Net.原子的位置是原子的位置.图像细分 图像细分 图像细分监督学习学习监督学习过渡金属二甲基二甲基化物

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Picometer-Precision Atomic Position Tracking through Electron Microscopy
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Picometer-Precision Atomic Position Tracking through Electron Microscopy

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Preparation and Observation of Thick Biological Samples by Scanning Transmission Electron Tomography
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Preparation and Observation of Thick Biological Samples by Scanning Transmission Electron Tomography

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相关实验视频

Last Updated: May 14, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
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Picometer-Precision Atomic Position Tracking through Electron Microscopy
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Preparation and Observation of Thick Biological Samples by Scanning Transmission Electron Tomography
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Preparation and Observation of Thick Biological Samples by Scanning Transmission Electron Tomography

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科学领域:

  • 材料科学 材料科学 材料科学
  • 电子显微镜电子显微镜
  • 人工智能的人工智能

背景情况:

  • 传输电子显微镜 (TEM) 允许在原子尺度上进行材料结构研究.
  • 由于噪音和污染,在TEM图像中精确检测原子位置具有挑战性.
  • 传统的算法与低信号噪声比 (SNR) 图像作斗争,需要对参数进行调整.

研究的目的:

  • 开发一种可靠的方法来检测低SNR扫描TEM (STEM) 图像中的原子位置.
  • 在杂的实验数据中克服传统峰值查找算法的局限性.
  • 介绍AtomID-Net,一个用于增强原子探测的深度神经网络.

主要方法:

  • 开发AtomID-Net,一个深度神经网络模型.
  • 在真实实验STEM图像上训练模型.
  • 使用多尺度分析用于低SNR图像处理.

主要成果:

  • AtomID-Net实现了强大而高效的原子位置检测.
  • 该模型即使在显著的背景噪音和污染的情况下也表现良好.
  • 在50张图像 (800x800分辨率) 的测试组中获得了0.964的平均F1-Score.

结论:

  • 原子ID-Net显著优于STEM中原子检测的现有峰值查找算法.
  • 深度学习方法提供了一个更可靠的解决方案,用于从杂的实验数据中分析原子结构.
  • 能够在原子尺度上更准确地描述材料.