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相关概念视频

UV–Vis Spectrometers01:14

UV–Vis Spectrometers

1.2K
The absorbance of UV and visible (UV–visible) radiations is measured using a UV–visible spectrophotometer. Deuterium lamps, which emit UV radiation, and tungsten lamps, which produce radiation in the visible region, are used as light sources in UV–visible spectrophotometers. A monochromator or prism is used for diffraction grating, i.e., to split the incoming radiation into different wavelengths. A system of slits is used to focus the desired wavelength on the sample cell.
1.2K
Atomic Emission Spectroscopy: Instrumentation01:22

Atomic Emission Spectroscopy: Instrumentation

282
The instrumentation of atomic emission spectrometry (AES) involves various components, including atomization devices that convert samples into gas-phase atoms and ions. There are two main types of atomization devices: continuous and discrete atomizers.  Continuous atomizers, like plasmas and flames, introduce samples in a constant stream, while discrete atomizers inject individual samples using syringes or autosamplers. The most common discrete atomizer is the electrothermal atomizer.
282
Inductively Coupled Plasma Atomic Emission Spectroscopy: Instrumentation01:26

Inductively Coupled Plasma Atomic Emission Spectroscopy: Instrumentation

160
Inductively coupled plasma (ICP) is the common plasma source used in atomic emission spectroscopy (AES), a technique that detects and analyzes various elements in a sample. This method is often called inductively coupled plasma atomic emission spectroscopy (ICP-AES).
There are three main types of inductively coupled plasma atomic emission spectroscopy  (ICP-AES) instruments: sequential, simultaneous multichannel, and Fourier transform instruments, with the latter being less commonly used....
160

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相关实验视频

Updated: May 7, 2026

Observation and Analysis of Blinking Surface-enhanced Raman Scattering
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Observation and Analysis of Blinking Surface-enhanced Raman Scattering

Published on: January 11, 2018

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SlitNET:一个深度学习支持的光谱仪裂.

Youxi Zhang1, Ciaran Bench2, Preveen Surendranathan1

  • 1Centre for Craniofacial and Regenerative Biology, King's College London, London SE1 9RT, U.K.

Analytical chemistry
|April 29, 2025
PubMed
概括
此摘要是机器生成的。

研究人员开发了一个深度学习模型SlitNET,以提高光谱仪的分辨率,而不会牺牲吞吐量. 这种由人工智能驱动的光谱仪切口可以提高光学光谱学中的材料识别和分析灵敏度.

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Diffuse Reflectance Spectroscopy: Getting the Capillary Refill Test Under One's Thumb
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Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions
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相关实验视频

Last Updated: May 7, 2026

Observation and Analysis of Blinking Surface-enhanced Raman Scattering
05:52

Observation and Analysis of Blinking Surface-enhanced Raman Scattering

Published on: January 11, 2018

6.9K
Diffuse Reflectance Spectroscopy: Getting the Capillary Refill Test Under One's Thumb
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Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions
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科学领域:

  • 频谱学是一种光谱学.
  • 人工智能的人工智能
  • 材料科学 材料科学 材料科学

背景情况:

  • 光谱仪的效率和分辨率对于光学光谱学至关重要.
  • 优化性能涉及到光谱分辨率 (狭窄隙) 和吞吐量 (宽隙) 之间的权衡.

研究的目的:

  • 引入SlitNET,这是一个用于增强光谱仪分辨率的深度学习模型.
  • 为了在光学光谱学中同时实现高吞吐量和高分辨率.

主要方法:

  • 从低分辨率输入中训练了一个神经网络 (SlitNET) 来重建高分辨率拉曼光谱.
  • 利用转移学习从合成数据到实验拉曼数据进行模型微调.
  • 将模型应用于材料的实验拉曼光谱数据.

主要成果:

  • 通过使用物理100微米隙实现了相当于10微米隙的分辨率增强.
  • 成功地区分了以前无法区分的材料与宽的隙.
  • 证明了分析灵敏度和特异性的提高.

结论:

  • SlitNET 能够同时实现高吞吐量和高分辨率,克服了光学光谱学的关键限制.
  • 深度学习与光子仪器仪表的整合提高了测量精度.
  • 这种方法为各种光学光谱学应用提供了巨大的潜力.