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相关概念视频

Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...
Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

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 developed.

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相关实验视频

Updated: Jul 10, 2026

Separating Beads and Cells in Multi-channel Microfluidic Devices Using Dielectrophoresis and Laminar Flow
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微珠的选择性激光清洁使用深度学习.

Yuchen Liu1, James A Grant-Jacob2, Yunhui Xie2

  • 1Optoelectronics Research Centre, University of Southampton, Southampton, UK. yl22u22@soton.ac.uk.

Scientific reports
|April 30, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了使用femtosecond脉冲和神经网络的自适应激光清洁. 这种智能系统精确地以最小的能量去除污染物,保护微妙的表面.

关键词:
五秒激光激光的使用时间为5秒.激光清洗 激光清洗神经网络的神经网络实时控制 实时控制

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Microfluidic Acoustophoresis for Flowthrough Separation of Gram-Negative Bacteria using Aptamer Affinity Beads
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Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment
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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
  • 光学工程是指光学工程.
  • 机器学习 机器学习

背景情况:

  • 传统的激光清洗缺乏实时监控,导致过度加工和基板损坏.
  • 不高效的能源使用和潜在的材料降解是当前工业清洗方法的关键局限性.

研究的目的:

  • 为高精度应用开发一种选择性和自适应性激光清洗方法.
  • 在激光清洗过程中集成神经网络进行实时反控制.
  • 为了证明精确的污染物去除与最小的能源支出.

主要方法:

  • 使用 femtosecond激光脉冲与15μm聚烯微珠一起使用.
  • 实施了一个经过训练的神经网络来预测样本外观后激光脉冲.
  • 将神经网络集成到反循环中,用于自适应清洁控制.

主要成果:

  • 实现精确的污染物去除,根据特定的目标模式量身定制.
  • 在清洗效率和精度方面显著提高.
  • 在清洗过程中尽量减少能源消耗和潜在的基板损坏.

结论:

  • 开发的方法为需要严格的材料控制的应用提供了一个非常有前途的解决方案.
  • 结合了超快的激光技术和机器学习,用于先进的表面处理.
  • 代表了有效和精确的工业清洁技术的重大进步.