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相关概念视频

Gas Chromatography: Introduction01:13

Gas Chromatography: Introduction

2.1K
Gas chromatography (GC) is a technique for separating and analyzing volatile compounds in a sample. Its primary purpose is to identify and quantify components in complex mixtures, making it essential in fields such as environmental analysis, pharmaceuticals, and petrochemicals. GC is also called vapor-phase chromatography (VPC) or gas-liquid partition chromatography (GLPC).
In GC,  a sample is vaporized and mixed with an inert carrier gas (the mobile phase), which transports it through a...
2.1K
Gas Chromatography–Mass Spectrometry (GC–MS)01:14

Gas Chromatography–Mass Spectrometry (GC–MS)

4.4K
Gas chromatography–mass spectrometry (GC–MS) is the combination of analytical techniques of gas chromatography and mass spectrometry in a single instrument for analyzing a mixture of compounds. The gas chromatograph separates the compounds in the mixture, and the mass spectrometer analyzes each compound separately to determine the molecular masses and molecular structures.
A gas chromatograph consists of a long, narrow capillary column with a polysiloxane coating on the inner wall....
4.4K
Gas Chromatography: Sample Injection Systems01:08

Gas Chromatography: Sample Injection Systems

453
In gas chromatography, the sample is introduced as a vapor plug into the carrier gas stream for high efficiency and resolution. A microsyringe injects the sample solution into a heated sample port, vaporizing it and mixing it with the carrier gas. This process is important to ensure the sample is properly prepared for analysis. Thermally sensitive samples can be injected directly into the column and volatilized by slowly increasing the column temperature.
Two primary injection methods are used...
453
Gas Chromatography: Overview of Detectors01:13

Gas Chromatography: Overview of Detectors

619
Detectors in gas chromatography (GC) help identify and quantify the components of a mixture by translating chemical properties into measurable signals, which are displayed on a chromatogram. Detectors can be categorized into two main types: destructive and non-destructive.
A non-destructive detector allows a sample to be analyzed without altering or consuming it, meaning the sample can be collected after detection for further analysis. Examples include thermal conductivity detectors and...
619
Gas Chromatography: Types of Detectors-I01:21

Gas Chromatography: Types of Detectors-I

478
There are different types of detectors used in gas chromatography, each with its own specific properties that make it suitable for detecting certain types of analytes. The most commonly used detectors in GC are thermal conductivity detector (TCD), flame ionization detector (FID), and electron capture detector (ECD).
TCD is the earliest and most widely used detector that operates by measuring the changes in the thermal conductivity of the carrier gas. When a sample compound enters the detector,...
478
Gas Chromatography: Types of Columns and Stationary Phases01:17

Gas Chromatography: Types of Columns and Stationary Phases

724
Gas chromatography (GC) relies on stationary phases to separate and analyze components in a sample. There are two main types of stationary phases: liquid and solid. Liquid stationary phases are non-volatile, thermally stable, and chemically inert liquids coated onto the column. Solid stationary phases are particles of adsorbent material, such as silica gel or molecular sieves.
For an analyte to remain on the column for a sufficient amount of time, it must exhibit some level of compatibility (or...
724

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相关实验视频

Updated: Jul 20, 2025

Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography
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Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography

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一个基于深度学习的模拟器,用于全面的二维GC应用程序.

Lucas Almir Cavalcante Minho1, Zenilda de Lourdes Cardeal1, Helvécio Costa Menezes1

  • 1Departamento de Química, ICEx, Universidade Federal de Minas Gerais, Avenida Antônio Carlos, Belo Horizonte, Minas Gerais, Brazil.

Journal of separation science
|July 31, 2023
PubMed
概括

深度学习模型在全面的二维气相色谱中准确预测保留时间,从而使方法优化和模拟成为可能. 建议建立一个协作数据库,以更好地预测不太常见的化合物.

科学领域:

  • 分析化学 分析化学
  • 计算化学的计算化学

背景情况:

  • 在全面的二维气相色谱 (GC×GC) 中预测化物位置对于方法优化至关重要.
  • 深度学习 (DL) 模型为复杂的染色学数据分析提供了强大的和可适应的解决方案.

研究的目的:

  • 为优化GC×GC方法开发一个开源的深度神经网络 (DNN) 程序.
  • 为了实现操作条件的模拟和在实验室外预测保留时间.

主要方法:

  • 使用实验预测器开发基于DNN的程序.
  • 培训和验证DL模型用于预测第一维和第二维保留时间.

主要成果:

  • 在第一维和第二维保留时间预测中,分别实现了0.006和0.014的缩放损失 (MSE).
  • 对包括环境污染物,生物分子和制药在内的各种化学品类的预测准确度 (R2 > 0.8) 很好.
  • 确定需要持续更新数据库以准确预测不太常见的化合物.

结论:

  • DNN模型为GC×GC中的保留时间预测提供了可靠的方法.
  • 开发的开源程序促进了方法优化和模拟.
  • 建议建立一个协作数据库,以增强对更广泛化合物的预测能力.
关键词:
人工智能的人工智能是人工智能.协作科学 协作科学数据科学数据科学机器学习是机器学习.公共数据库是公共数据库.

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