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Updated: Jul 14, 2025

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基于深度神经网络的电子显微镜图像识别用于源区分人为和自然磁性粒子

Lin Liu1,2, Tianyou Chen3, Qinghua Zhang1,4

  • 1State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.

Environmental science & technology
|October 6, 2023
PubMed
概括

本研究介绍了CoMask,这是一种新的深度学习模型,用于使用传输电子显微镜图像识别纳米环境颗粒的来源. CoMask有效地区分大气中的磁粒子,在环境科学中推进机器学习.

关键词:
深化学习深化学习电子显微镜的电子显微镜图像识别功能 图像识别功能颗粒物质颗粒物质颗粒物质源区分区分的来源.

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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
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