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相关概念视频

Preparation of Samples for Electron Microscopy01:20

Preparation of Samples for Electron Microscopy

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To be visualized by an electron microscope, either transmission or scanning, biological samples need to be fixed (stabilized) so the electron beam does not destroy them and dried thoroughly (desiccated/dehydrated) so the vacuum does not affect them. Fixation needs to be done as quickly as possible because the sample properties will start changing as soon as it is removed from its natural environment. For example, in a tissue sample, the oxygen levels begin decreasing, causing an altered...
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Scanning Electron Microscopy01:07

Scanning Electron Microscopy

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A scanning electron microscope (SEM) is used to study the surface features of a sample by using an electron beam that scans the sample surface in a two-dimensional manner. Typically, areas between ~1 centimeter to 5 micrometers in width can be imaged. SEM can be used to image bacteria, viruses, tissues as well as larger samples like insects. Conventional SEM gives a magnification ranging from 20X to 30,000X and spatial resolution of 50 to 100 nanometers.
Fundamental Principles
Accelerated...
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相关实验视频

Updated: Jun 3, 2025

The Evolution of Silica Nanoparticle-polyester Coatings on Surfaces Exposed to Sunlight
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基于计算机视觉的自动评估方法,用SEM图像对Y2O3钢涂层性能进行评估.

Jianhong Zhao1, Huamin Yang2, Yi Sui3

  • 1School of Computer Science and Technology, Changchun University of Science and Technology, Jilin, 130000, China.

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|January 11, 2025
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概括

本研究介绍了一种自动化的深度学习方法,用于使用扫描电子显微镜 (SEM) 分析钢结构. 唐瑞检测 (TRD) 模型有效量化特征,提高了材料科学中的可靠性.

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Applicability Analysis of Assessment Methods for Morphological Parameters of Corroded Steel Bars
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Imaging Corrosion at the Metal-Paint Interface Using Time-of-Flight Secondary Ion Mass Spectrometry
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相关实验视频

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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算机视觉 计算机视觉
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 通过扫描电子显微镜 (SEM) 手动分析钢结构微观结构是耗时且主观的.
  • 目前的方法在量化微观结构特征方面缺乏效率和准确性.

研究的目的:

  • 开发一种基于深度学习的自动化评估方法,用于分析钢结构微观结构.
  • 提高微观结构特征检测和量化的效率和可靠性.

主要方法:

  • 利用先进的计算机视觉算法进行微观结构分析.
  • 开发了一个专门的深度学习模型,Tang Rui Detect (TRD),用于树突凝固结构.
  • 实施了一种用于特征检测和量化的自动化系统.

主要成果:

  • 实现了钢铁微结构特征的高效准确检测和量化.
  • 证明了Tang Rui检测 (TRD) 模型对树突凝固的有效性.
  • 展示了自动化微结构评估和表面修饰分析的潜力.

结论:

  • 基于深度学习的方法显著提高了钢结构微观结构分析的自动化和可靠性.
  • 这种方法简化了训练和损失函数设计,以改善评估.
  • 该研究为材料科学研究和工业应用提供了强大的解决方案.