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相关概念视频

Inductively Coupled Plasma Atomic Emission Spectroscopy: Instrumentation01:26

Inductively Coupled Plasma Atomic Emission Spectroscopy: Instrumentation

202
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....
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Atomic Emission Spectroscopy: Instrumentation01:22

Atomic Emission Spectroscopy: Instrumentation

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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.
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相关实验视频

Updated: Jun 18, 2025

Quantifying X-Ray Fluorescence Data Using MAPS
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基于机器学习的X射线光谱从过堆光谱仪数据的展开.

M Alvarado Alvarez1, B T Wolfe1, C-S Wong1

  • 1Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA.

The Review of scientific instruments
|August 1, 2024
PubMed
概括
此摘要是机器生成的。

神经网络准确地从波器堆谱仪中展开X射线光谱. 这种方法显示出对错误的稳定性和高重复率应用的潜力.

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ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis
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Elemental-sensitive Detection of the Chemistry in Batteries through Soft X-ray Absorption Spectroscopy and Resonant Inelastic X-ray Scattering
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相关实验视频

Last Updated: Jun 18, 2025

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ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis
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Elemental-sensitive Detection of the Chemistry in Batteries through Soft X-ray Absorption Spectroscopy and Resonant Inelastic X-ray Scattering
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科学领域:

  • 频谱学是一种光谱学.
  • 机器学习 机器学习
  • 在X射线物理中,X射线物理

背景情况:

  • 过堆光谱仪通过光刺激发光 (PSL) 测量X射线能量沉积.
  • 精确的X射线光谱展开对于各种科学和工业应用至关重要.

研究的目的:

  • 应用神经网络用于X射线光谱的展开使用过堆光谱仪数据.
  • 评估神经网络方法的准确性,稳定性和速度.

主要方法:

  • 在合成X射线数据 (<1 MeV) 上训练神经网络,使用马克斯韦尔和高斯分布.
  • 使用来自五种不同的过堆光谱仪设计的PSL测量.
  • 测试网络的性能与地面真相光谱和模拟的实验错误.

主要成果:

  • 神经网络的预测与单个分布的基本真相光谱密切匹配,高能量的差异<20%.
  • 该网络表现出对实验误差 (<5%) 的稳定性,以及对混合分布展开的一些能力.
  • 展开速度超过1Hz,适用于高重复率系统.

结论:

  • 神经网络提供了一个强大而高效的工具,用于用波器堆谱仪展开X射线光谱.
  • 开发的方法是准确的,强大的实验噪声,并足够快的要求应用程序.