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相关概念视频

Scanning Electron Microscopy01:07

Scanning Electron Microscopy

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...
Preparation of Samples for Electron Microscopy01:20

Preparation of Samples for Electron Microscopy

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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相关实验视频

Updated: Jul 16, 2026

Studying Pre-formed Fibril Induced α-Synuclein Accumulation in Primary Embryonic Mouse Midbrain Dopamine Neurons
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使用SEM成像和先进的深度学习进行Pd/C纳米颗粒的形态分析.

Nguyen Duc Thuan1, Hoang Manh Cuong1, Nguyen Hoang Nam1

  • 1School of Electrical and Electronic Engineering, Hanoi University of Science and Technology Hanoi Vietnam thuan.nguyenduc1@hust.edu.vn hong.hoangsy@hust.edu.vn.

RSC advances
|November 6, 2024
PubMed
概括

本研究介绍了一种深度学习方法,用于使用扫描电子显微镜 (SEM) 图像分析碳 (Pd/C) 纳米颗粒上的. 该方法准确地检测和聚类纳米粒子,揭示了对其形态和分布的关键见解.

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Semi-Quantitative Determination of Dopaminergic Neuron Density in the Substantia Nigra of Rodent Models using Automated Image Analysis
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科学领域:

  • 材料科学 材料科学 材料科学
  • 纳米技术纳米技术
  • 计算机科学 计算机科学

背景情况:

  • 对纳米粒子进行准确的形态分析对于理解它们的特性至关重要.
  • 纳米粒子分析的传统方法可能耗时且主观.
  • 使用深度学习的自动化分析提供了提高精度和效率的潜力.

研究的目的:

  • 开发和验证一种基于深度学习的方法,用于对碳 (Pd/C) 纳米粒子上的的形态分析.
  • 从扫描电子显微镜 (SEM) 图像中准确检测和划分单个纳米粒子.
  • 分析Pd/C纳米颗粒的结构特征和空间分布.

主要方法:

  • 实施深度学习检测模型与注意力机制,用于在SEM图像中识别纳米粒子.
  • 使用基于图形的网络来分析检测到的纳米粒子的结构特征.
  • 基于密度的空间聚类的应用,以确定纳米粒子的模式和分布.

主要成果:

  • 提出的深度学习模型在检测Pd/C纳米粒子方面实现了高精度和可靠性.
  • 聚类分析为纳米粒子的形态分布和结构组织提供了重要的见解.
  • 自动化方法在表征纳米粒子组合方面表现出有效性.

结论:

  • 开发的深度学习框架为纳米粒子形态分析提供了强大而高效的方法.
  • 这种方法增强了对Pd/C纳米粒子特性及其潜在应用的理解.
  • 先进的深度学习技术对自动化纳米材料表征有很大的前景.