Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Atomic Emission Spectroscopy: Instrumentation01:22

Atomic Emission Spectroscopy: Instrumentation

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.
Inductively Coupled Plasma Atomic Emission Spectroscopy: Instrumentation01:26

Inductively Coupled Plasma Atomic Emission Spectroscopy: Instrumentation

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.
Atomic Emission Spectroscopy: Lab01:29

Atomic Emission Spectroscopy: Lab

AES is a powerful analytical technique, especially effective when used with plasma sources, producing abundant spectra in characteristic emission lines. The Inductively Coupled Plasma (ICP), in particular, yields superior quantitative analytical data due to its high stability, low noise, low background, and minimal interferences under optimal experimental conditions. However, newer air-operated microwave sources are emerging as promising alternatives that could be more cost-effective than...
Electronic Distance Measuring Instruments01:30

Electronic Distance Measuring Instruments

Electronic Distance Measuring Instruments (EDMs) are essential tools in modern surveying, offering precise distance measurements by emitting electromagnetic signals and calculating the time required for these signals to travel to a target and return. Two primary types of signals are used in EDMs — light waves and microwaves — each suited to specific environmental and distance requirements. Light-wave-based EDMs utilize either infrared or laser light, providing high accuracy over short distances...

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Spatially Controlled Growth of Ultrathin MoO<sub>2</sub> Polymorphs by Physical Vapor Deposition.

Nano letters·2025
Same author

Alkali hydroxide (LiOH, NaOH, KOH) in water: Structural and vibrational properties, including neutron scattering results.

The Journal of chemical physics·2024
Same author

Surface Transfer Doping in MoO<sub>3-</sub>/Hydrogenated Diamond Heterostructure.

The journal of physical chemistry letters·2024
Same author

Inelastic Neutron Scattering Study of Phonon Density of States of Iodine Oxides and First-Principles Calculations.

The journal of physical chemistry letters·2023
Same author

Author Correction: Ultrafast non-radiative dynamics of atomically thin MoSe<sub>2</sub>.

Nature communications·2023
Same author

Induction and Ferroelectric Switching of Flux Closure Domains in Strained PbTiO<sub>3</sub> with Neural Network Quantum Molecular Dynamics.

Nano letters·2023

相关实验视频

Updated: Jun 28, 2026

Biochemical and Structural Characterization of the Carbohydrate Transport Substrate-binding-protein SP0092
08:53

Biochemical and Structural Characterization of the Carbohydrate Transport Substrate-binding-protein SP0092

Published on: October 2, 2017

30.5K

机器学习用于超快电子衍射的实验设计.

Mohammad Shaaban1, Sami El-Borgi2, Aravind Krishnamoorthy3

  • 1Department of Mechanical Engineering, Texas A & M University, College Station, USA.

Scientific reports
|July 2, 2025
PubMed
概括

机器学习可以实时分析超快电子衍射 (UED) 数据,从而更快地了解材料动态和损坏. 这加速了新材料的发现,并优化了科学研究的实验条件.

关键词:
实验设计 实验设计机器学习 机器学习自主监督学习学习超快电子衍射的超快电子衍射是什么?变量自动编码器变量自动编码器

更多相关视频

Measurements of Long-range Electronic Correlations During Femtosecond Diffraction Experiments Performed on Nanocrystals of Buckminsterfullerene
08:44

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

Published on: August 22, 2017

7.8K
Sample Preparation and Experimental Design for In Situ Multi-Beam Transmission Electron Microscopy Irradiation Experiments
08:31

Sample Preparation and Experimental Design for In Situ Multi-Beam Transmission Electron Microscopy Irradiation Experiments

Published on: June 27, 2022

1.8K

相关实验视频

Last Updated: Jun 28, 2026

Biochemical and Structural Characterization of the Carbohydrate Transport Substrate-binding-protein SP0092
08:53

Biochemical and Structural Characterization of the Carbohydrate Transport Substrate-binding-protein SP0092

Published on: October 2, 2017

30.5K
Measurements of Long-range Electronic Correlations During Femtosecond Diffraction Experiments Performed on Nanocrystals of Buckminsterfullerene
08:44

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

Published on: August 22, 2017

7.8K
Sample Preparation and Experimental Design for In Situ Multi-Beam Transmission Electron Microscopy Irradiation Experiments
08:31

Sample Preparation and Experimental Design for In Situ Multi-Beam Transmission Electron Microscopy Irradiation Experiments

Published on: June 27, 2022

1.8K

科学领域:

  • 材料科学 材料科学 材料科学
  • 物理化学 物理化学
  • 数据科学数据科学数据科学

背景情况:

  • 超快电子衍射 (UED) 提供了对超快材料行为的洞察力.
  • 对大型UED数据集的手动分析耗时,并限制实时实验控制.
  • 缺乏实时数据阻碍了实验参数的现场调整和避免样本损坏.

研究的目的:

  • 开发和演示用于实时分析UED数据的机器学习方法.
  • 允许在UED实验期间现场监测和控制材料动态.
  • 用UED加速发现和优化材料属性.

主要方法:

  • 卷积神经网络 (CNN) 在合成和实验衍射模式上受过训练.
  • CNNs被用于实时分析,以解决动态过程并识别物质损失.
  • 卷积变异自编码器 (CVAEs) 开发用于追踪潜伏空间中的结构相变.

主要成果:

  • 使用CNNs实现了UED数据的实时分析.
  • 在代表性材料中确定了动态过程和物质损害.
  • 通过UED图像时间轨迹,CVAE模型成功地跟踪了结构相变.
  • 实验参数被实时引导到所需的相位转换.

结论:

  • 机器学习,特别是CNN和CVAE,可以对UED数据进行实时分析.
  • 这种方法可以实现自我纠正的衍射实验,以优化大规模的用户设施.
  • 实时数据分析有助于对材料动态和实验结果进行现场控制.