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相关概念视频

Deconvolution01:20

Deconvolution

260
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
260
Molecular Spectroscopy: Absorption and Emission01:14

Molecular Spectroscopy: Absorption and Emission

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Molecules possess discrete energy levels called quantum states. Unlike atoms, which have simpler energy levels, molecules possess additional rotational and vibrational energy levels.  Each energy level is separated by an energy gap, with the gaps between adjacent electronic, vibrational, and rotational levels varying significantly. The three types of energy levels in a diatomic molecule are shown in Figure 1.
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Atomic Absorption Spectroscopy: Radiation and Light Sources01:13

Atomic Absorption Spectroscopy: Radiation and Light Sources

548
Atomic absorption spectroscopy (AAS) relies on the Beer-Lambert law, which requires that the radiation source emits a narrow range of wavelengths to match the absorption characteristics of the analyte atom. The primary criteria for choosing an appropriate radiation source in AAS is to provide a precise and intense emission at specific wavelengths that will allow accurate detection of the analyte.
Two common narrow-range 'line' sources used in AAS are hollow-cathode lamps (HCLs) and...
548
Atomic Absorption Spectroscopy: Overview01:27

Atomic Absorption Spectroscopy: Overview

2.4K
Atomic absorption spectroscopy (AAS) is a technique used to analyze elements by measuring electromagnetic radiation (EMR) absorbed by atoms, which causes them to transition to a higher-energy orbit. The most crucial step in AAS is atomization, where the analyte is converted into gas-phase atoms, typically through a flame or furnace. Some of these atoms become thermally excited in the flame, while most remain in the ground state.
When irradiated by EMR of a particular wavelength, these...
2.4K
UV–Vis Spectroscopy of Conjugated Systems01:32

UV–Vis Spectroscopy of Conjugated Systems

7.3K
Organic compounds with conjugated double bonds show strong absorption features in the UV–visible region of the electromagnetic spectrum attributed to π → π* electronic excitations. Generally, a UV–vis absorption spectrum is recorded as a plot of absorbance vs wavelength. The wavelength of maximum absorbance, which manifests as a peak in the absorption spectrum, is denoted as λmax.
One of the factors influencing λmax is the extent...
7.3K
Atomic Absorption Spectroscopy: Atomization Methods01:25

Atomic Absorption Spectroscopy: Atomization Methods

669
Atomic Absorption Spectroscopy (AAS) atomizes samples through flame atomization or electrothermal atomization. Flame atomization typically involves a nebulizer and spray chamber assembly to combine the sample with a fuel–oxidant mixture, creating a fine aerosol mist that enters a burner. Typically, the fuel and oxidant are combined in an approximately stoichiometric ratio. However, for atoms that are easily oxidized, a fuel-rich mixture may be more advantageous. Only about 5% of the...
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相关实验视频

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Analysis of SEC-SAXS data via EFA deconvolution and Scatter
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机器学习驱动的混合X射线吸收光谱的解卷.

Kexin Wang1, Haishan Yu1, Xiangwei Lu1

  • 1National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei 230026, China.

The journal of physical chemistry. A
|July 30, 2025
PubMed
概括

机器学习 (ML) 对材料分析复杂的X射线吸收光谱 (XAS). 这种方法可以准确地识别氧化状态和成分,提高光谱分析效率.

科学领域:

  • 材料科学 材料科学 材料科学
  • 频谱学是一种光谱学.
  • 计算化学的计算化学

背景情况:

  • 射线吸收光谱 (XAS) 对于材料的表征,确定氧化状态,原子结构和电子配置至关重要.
  • 从混合离子站点和各种化学环境中解释复杂的XAS光谱具有挑战性,需要大量的专业知识和时间.
  • 分析混合XAS数据的现有方法通常是劳动密集型的,可能缺乏效率.

研究的目的:

  • 开发和验证一种机器学习 (ML) 方法,用于高效地分析混合X射线吸收光谱 (XAS) 数据.
  • 专注于 (Co) L-边缘光谱,展示ML模型在分类光谱组件和提取关键参数方面的能力.
  • 为了提高从复杂的XAS数据中确定材料性能的速度和准确性.

主要方法:

  • 使用多重计算生成了L边缘XAS光谱的模拟数据集,包括组件组合和能量转移的变化.
  • 尺寸缩小技术和各种机器学习 (ML) 算法被系统地评估,以获得最佳性能.
  • 开发了一个自动拟合算法来隔离光谱元件,确定它们的比例,并提取像高斯扩展这样的参数.

主要成果:

  • 确定了缩小维度和ML方法的特定组合,在分类混合XAS光谱方面实现了高精度和效率.
  • 自动拟合算法成功提取了关键参数,拟合结果与模拟的输入光谱密切一致.

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Synchrotron X-ray Microdiffraction and Fluorescence Imaging of Mineral and Rock Samples
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  • 经过验证的ML方法证明了其在分析复杂光谱数据方面的有效性,克服了传统解释方法的局限性.
  • 结论:

    • 开发的基于ML的方法显著提高了分析混合X射线吸收光谱 (XAS) 的效率和准确性.
    • 这种方法可以可靠地确定价值态,晶体场参数和化合物等材料中的组成比率.
    • 该研究验证了ML的使用,通过先进的光谱分析加速材料表征.