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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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Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

12.2K
Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...
12.2K
Overview of Electron Microscopy01:25

Overview of Electron Microscopy

12.9K
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.
12.9K
Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

2.8K
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...
2.8K
Immunogold Electron Microscopy01:20

Immunogold Electron Microscopy

5.3K
Immunoelectron microscopy utilizes immunogold labeling of endogenous proteins with specific antibodies to detect and localize these proteins in cells and tissues. The procedure provides insights into the distribution and quantification of protein under different stimulation conditions offering clues about their functions. Conjugating highly electron-dense gold particles with primary or secondary antibodies allow antigen detection on and within cells, with high resolution and specificity.
5.3K
Confocal Fluorescence Microscopy01:16

Confocal Fluorescence Microscopy

20.0K
Confocal microscopy is an advanced microscopic technique. The prime advantage of the confocal microscope over other microscopy techniques is its ability to block the out-of-focus light from the illuminated samples using pinholes. It is widely used with fluorescence optics to obtain high-resolution, sharp contrast images. Unlike optical microscopes, confocal microscopes use a focused beam of light laser to scan the entire sample surface at different z-planes. These microscopes are, therefore,...
20.0K

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相关实验视频

Updated: Jan 14, 2026

Nano-fEM: Protein Localization Using Photo-activated Localization Microscopy and Electron Microscopy
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Nano-fEM: Protein Localization Using Photo-activated Localization Microscopy and Electron Microscopy

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一个视觉语言模型,使智能纳米材料扫描电子显微镜注释成为可能.

Yongzhu Cai1,2,3, Hong Wang1,2,3

  • 1School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China. hongwang2@sjtu.edu.cn.

Nanoscale
|October 22, 2025
PubMed
概括
此摘要是机器生成的。

一个新的扫描电子显微镜视觉语言模型 (SEM-VLM) 使用人工智能进行纳米材料图像分析,而不需要广泛的标记数据. 这种方法显著减少了手工注释的需要,使材料科学研究更快,更准确.

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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:

  • 在材料科学中,数据驱动的方法很强大,但由于需要大型标记数据集而受到阻碍.
  • 对纳米材料的扫描电子显微镜 (SEM) 图像进行手动注释是复杂且耗时的.
  • 由于标记数据的稀缺性,SEM图像的自动模式识别至关重要.

研究的目的:

  • 为纳米材料的SEM图像开发一种不依赖标记数据的自动模式识别技术.
  • 将现有的视觉语言模型 (VLMs) 适应纳米材料科学的特定领域.
  • 在人工智能驱动的材料研究中减少对大型标记数据集的依赖.

主要方法:

  • 通过调整一般视觉语言模型,开发了扫描电子显微镜视觉语言模型 (SEM-VLM).
  • 从科学文献中提取的SEM图像-文本对上使用对比学习来训练SEM-VLM.
  • 采用集体视觉语言对齐用于零射击分类和激活映射以实现可解释性.

主要成果:

  • 在跨模式检索中,SEM-VLM的表现优于CLIP和随机基线等一般域模型 (Recall@10,Recall@50).
  • 在零射击分类中实现了高精度,超过了CLIP.
  • 与完全监督模型相比,在少数镜头设置中表现优异,仅使用2.1%的培训标签.
  • 激活映射提供了纳米尺度特征的可解释本地化.

结论:

  • SEM-VLM为纳米材料 SEM 图像的自动分析提供了强大的和可解释的解决方案.
  • 该模型显著减少了对标记数据集的依赖,使得高精度分类能够在最低限度的监督下进行.
  • 这种多式模式框架促进了材料科学中的AI应用,特别是复杂的纳米材料表征.