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相关概念视频

X-ray Diffraction of Biological Samples01:10

X-ray Diffraction of Biological Samples

3.8K
X-ray diffraction or XRD is an analytical tool that utilizes X-rays to study ordered structures such as crystalline organic and inorganic samples, polycrystalline materials, proteins, carbohydrates, and drugs.
According to Bragg's law, when X-rays strike the sample positioned on a stage, the rays are  scattered by the electron clouds around the sample atoms. The  X-ray diffraction or scattering is caused by constructive interference of the X-ray waves that reflect off the internal...
3.8K
X-ray Crystallography02:18

X-ray Crystallography

23.8K
The size of the unit cell and the arrangement of atoms in a crystal may be determined from measurements of the diffraction of X-rays by the crystal, termed X-ray crystallography.
Diffraction
Diffraction is the change in the direction of travel experienced by an electromagnetic wave when it encounters a physical barrier whose dimensions are comparable to those of the wavelength of the light. X-rays are electromagnetic radiation with wavelengths about as long as the distance between neighboring...
23.8K

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相关实验视频

Updated: Jun 17, 2025

Synchrotron X-ray Microdiffraction and Fluorescence Imaging of Mineral and Rock Samples
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Synchrotron X-ray Microdiffraction and Fluorescence Imaging of Mineral and Rock Samples

Published on: June 19, 2018

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使用深度神经网络处理XRD模式的相位量化.

Titouan Simonnet1, Sylvain Grangeon2, Francis Claret2

  • 1Institut Denis Poisson, Université d'Orléans, Université de Tours, CNRS, France.

IUCrJ
|August 12, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的神经网络 (NN),用于使用粉末X射线衍射 (XRD) 数据进行自动化矿物相识别和量化. 该方法显著加快了分析速度,特别是在大型数据集中,提高了准确性和效率.

关键词:
石灰岩石的石灰岩石是一种石.复合材料是一种复合材料.计算建模计算建模深度神经网络是一个神经网络.杜洛米特是一种多洛米特.吉布斯网站 吉布斯网站血的血是一种血.粉末X射线衍射粉末的X射线.

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Measurements of Long-range Electronic Correlations During Femtosecond Diffraction Experiments Performed on Nanocrystals of Buckminsterfullerene
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Measurements of Long-range Electronic Correlations During Femtosecond Diffraction Experiments Performed on Nanocrystals of Buckminsterfullerene

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Quantifying X-Ray Fluorescence Data Using MAPS
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Quantifying X-Ray Fluorescence Data Using MAPS

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相关实验视频

Last Updated: Jun 17, 2025

Synchrotron X-ray Microdiffraction and Fluorescence Imaging of Mineral and Rock Samples
10:12

Synchrotron X-ray Microdiffraction and Fluorescence Imaging of Mineral and Rock Samples

Published on: June 19, 2018

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Measurements of Long-range Electronic Correlations During Femtosecond Diffraction Experiments Performed on Nanocrystals of Buckminsterfullerene
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Measurements of Long-range Electronic Correlations During Femtosecond Diffraction Experiments Performed on Nanocrystals of Buckminsterfullerene

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Quantifying X-Ray Fluorescence Data Using MAPS
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科学领域:

  • 材料科学 材料科学 材料科学
  • 地质地质地质地质地质地
  • 计算科学 计算科学

背景情况:

  • 准确的矿物质识别和量化对于预测材料性质至关重要.
  • 粉末X射线衍射 (XRD) 与瑞特维尔德精细化是矿物质定量化的标准,但需要手动相位识别.
  • 手动相位识别对于大型数据集来说是耗时且不切实际的,例如同步射线衍射计算断层扫描中的数据集.

研究的目的:

  • 开发和验证一种新的神经网络 (NN) 方法,用于自动化从XRD数据中识别和量化矿物阶段.
  • 克服XRD分析中手动相位识别的局限性,特别是对于大而复杂的数据集.
  • 为任何数据集提供适用于矿物学分析的免费可用的多功能工具.

主要方法:

  • 使用XRD模式计算代码生成大型合成XRD数据集,用于NN培训.
  • 开发一个专门的损失函数用于比例推断,以提高NN的性能,效率和稳定性.
  • 仅在合成数据上训练神经网络,并在合成和真实实验XRD模式上测试其性能.

主要成果:

  • 训练有素的NN在合成和真实XRD模式中准确地识别和量化矿物阶段.
  • 实现了较低的误差率:合成数据的阶段量化率为0.5%,实验数据的误差率为6% (石,石,多洛石,血石).
  • 证明了NN处理对比晶体结构的能力,以及其适用于各种矿物学数据集的适用性.

结论:

  • 拟议的NN方法在自动化矿物阶段识别和从XRD数据量化方面取得了重大进展.
  • 这种方法使大数据集的有效分析成为可能,克服了与手工方法相关的瓶.
  • 这种免费可用的工具在各种矿物学分析中具有广泛的适用性,无论具体的阶段是什么.