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相关概念视频

Mass Spectrum: Interpretation01:24

Mass Spectrum: Interpretation

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An unknown compound can be established by identifying the molecular ion peak in the mass spectrum. The molecular ion peak is often weak or absent due to the predominance of fragmentation in high-energy electron beams. In such cases, a low-energy electron beam can be used to scan the spectrum to enhance the intensity of the molecular ion peak. Additionally, chemical ionization, field ionization, and desorption ionization spectra are used to obtain a relatively intense molecular ion peak.
To...
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High-Resolution Mass Spectrometry (HRMS)01:15

High-Resolution Mass Spectrometry (HRMS)

1.4K
The resolution of a mass spectrometer depends on the efficiency of separating ions with different ion masses. The mass of an atom is approximated to the sum of the masses of protons and neutrons inside, considering the masses of protons and neutrons as equal. However, the masses of the proton (1.6726 × 10−24 g) and neutron (1.6749 × 10−24 g) are not truly equal. There is a minor error in the expression of atomic masses relative to the simplest atom of hydrogen. For...
1.4K
Chemical Ionization (CI) Mass Spectrometry01:21

Chemical Ionization (CI) Mass Spectrometry

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The molecular ion peak of a molecule in the mass spectrum provides vital information for molecular identification. However, conventional electron impact ionization can lead to the rapid dissociation of some molecular ions before they reach the detector. A milder ionization method is required to increase the lifetime of such ionized analyte molecules. Chemical ionization (CI) is a gas-phase protonation reaction useful for mass-analyzing analyte molecules that are easily protonated to yield the...
799
Electrospray Ionization (ESI) Mass Spectrometry01:12

Electrospray Ionization (ESI) Mass Spectrometry

908
Higher molecular weight biomolecules are nonvolatile compounds that may decompose before ionizing or vaporizing during mass analysis with conventional electron impact ionization methods. Accordingly, electrospray ionization (ESI) is the favored method for vaporizing and ionizing biomolecules as it circumvents rapid fragmentation and enables the recording of mass signals for the entire biomolecule.
ESI utilizes electrical energy to transfer ions from the liquid phase of the sample into the...
908
Capillary Electrophoresis: Applications01:30

Capillary Electrophoresis: Applications

431
Capillary electrophoretic separations offer various modes, each with unique applications. These modes include capillary zone electrophoresis, capillary gel electrophoresis, capillary array electrophoresis, capillary isoelectric focusing, capillary isotachophoresis, micellar electrokinetic chromatography, and capillary electrochromatography.
Capillary zone electrophoresis (CZE) separates ionic components based on their electrophoretic mobility. It has been used to separate proteins, amino acids,...
431
Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

820
Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...
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CEEM:一种化学可解释的深度学习平台,用于识别具有低有效质量的化合物.

Jing Gao1, Zhilong Wang1, Yanqiang Han1

  • 1Key Laboratory of Thin Film and Microfabrication Technology, Ministry of Education, Department of Micro/Nano-electronics, Shanghai Jiao Tong University, Shanghai, 200240, China.

Small (Weinheim an der Bergstrasse, Germany)
|September 13, 2023
PubMed
概括

一个新的深度学习平台预测半导体有效质量 (mE),帮助发现电子和能源材料. 该工具识别了低mE的半导体,这对于高级应用至关重要.

关键词:
深度学习是一种深度学习.有效质量有效质量.可以解释的网络.图形网络是指图形网络.半导体 半导体 半导体

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科学领域:

  • 材料科学 材料科学 材料科学
  • 凝聚物质物理学 凝聚物质物理学
  • 计算化学计算化学

背景情况:

  • 半导体行业依赖于具有特定电子特性的材料.
  • 有效质量 (mE) 是电子行为的一个关键指标,影响设备性能.
  • 对mE的实验测量具有挑战性,限制了材料的表征和设计.

研究的目的:

  • 开发一个深度学习平台,用于预测半导体的有效质量 (mE).
  • 识别表现出低mE的n型和p型半导体.
  • 提供化学洞察力,了解支配ME的因素.

主要方法:

  • 使用深度学习构建一个化学可解释的预测平台 (CEEM).
  • 实现图形神经网络,以实现多功能和可解释的ME预测.
  • 创建迄今为止最大的mE数据库 (126,335条目).

主要成果:

  • CEEM实现了对mE的高预测准确性 (n型的AUC为0.904,p型为0.896).
  • 该平台成功识别了466个低mE半导体.
  • 导出了影响mE的关键化学因素,提供了机理学理解.

结论:

  • CEEM提供了一种高效,准确和可解释的方法来预测半导体mE.
  • 该平台促进了新型半导体材料的发现,用于透明的导电,光伏和水分应用.
  • CEEM为高性能半导体设计和开发开辟了新的途径.