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相关概念视频

Infrared (IR) Spectroscopy: Overview01:09

Infrared (IR) Spectroscopy: Overview

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When electromagnetic radiation passes through a material, atoms or molecules transition from a lower to a higher energy state by absorbing radiation corresponding to the energy difference between the two states. The absorption of infrared (IR) radiation causes transitions between vibrational energy levels in a molecule. Therefore, IR spectroscopy is a useful analytical tool for determining the molecular structure of molecules.
Different compounds display unique properties due to their...
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Gas Chromatography: Overview of Detectors01:13

Gas Chromatography: Overview of Detectors

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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...
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Gas Chromatography: Types of Detectors-II01:19

Gas Chromatography: Types of Detectors-II

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In gas chromatography, different detectors are employed to meet specific analytical needs. These detectors are often categorized based on their detection mechanisms and the types of compounds they are best suited to analyze. Thermal Conductivity Detectors (TCD), Flame Ionization Detectors (FID), and Electron Capture Detectors (ECD) represent common categories, each with unique operating principles and applications. However, beyond these, several other detectors are designed for more specialized...
368
IR Spectrometers01:25

IR Spectrometers

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There are two main infrared (IR) spectrophotometers: dispersive IR spectrometers and Fourier transform infrared (FTIR) spectrometers. In a dispersive IR spectrometer, a beam of infrared radiation produced by a hot wire is divided into two parallel equal-intensity beams using mirrors. One beam passes through the sample, while another is a reference beam. The beams then move through the monochromator, which separates the radiations into a continuous spectrum of different frequencies. The...
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Gas Chromatography: Types of Detectors-I01:21

Gas Chromatography: Types of Detectors-I

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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,...
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IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
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Infrared Degenerate Four-wave Mixing with Upconversion Detection for Quantitative Gas Sensing
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深度学习用于通过红外光谱检测的气体传感.

M Arshad Zahangir Chowdhury1, Matthew A Oehlschlaeger1

  • 1Department of Mechanical, Aerospace, and Nuclear Engineering, Rensselaer Polytechnic Institute, 110 Eighth St., Troy, NY 12180, USA.

Sensors (Basel, Switzerland)
|March 28, 2024
PubMed
概括

深度学习使用红外光谱准确地识别混合物中的多种气体. 这种新的卷积神经网络方法在大气和工业气体物种化方面实现了82-97%的准确性.

科学领域:

  • 频谱学是一种光谱学.
  • 人工智能的人工智能
  • 环境监测 环境监测

背景情况:

  • 深度学习 (DL) 越来越多地用于光谱和气体传感.
  • 使用DL从红外 (IR) 光谱中识别多组分混合物中的单个气体是一个尚未探索的领域.
  • 精确的气体物种化对于大气和工业过程监测至关重要.

研究的目的:

  • 开发和评估DL模型,用于识别和量化混合物中的多种气体,使用IR吸收光谱.
  • 为了证明模型在各种大气和工业气体中的有效性.
  • 调查模型的内部运作,以优先考虑光谱特征.

主要方法:

  • 一个单维的深卷积神经网络 (CNN) 模型被设计用于气体分类.
  • 使用HITRAN数据生成了空气中的十个关键分子 (例如,H2O,CO2,O3,NH3) 的红外吸收光谱的模拟数据集.
  • 美国有线电视新闻网的模型是在模拟的光谱上训练的,并用噪音数据和合成实验光谱进行了测试.

主要成果:

  • DL模型在预测混合物中的气体物种化方面实现了高准确度,从82%到97%.
  • 类激活地图可视化了模型对特定光谱区域的重点进行分类.
  • 该模型成功预测了合成实验混合物光谱的物种化,验证了其实际适用性.
关键词:
大气检测检测大气检测这是分类分类的分类.深度学习是一种深度学习.气体传感传感器是指气体传感器.红外吸收光谱学 红外吸收光谱学种类的变化 种类的变化微量气体检测检测仪 微量气体检测仪

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结论:

  • 拟议的CNN模型提供了一种通用且有效的方法,用于使用红外光谱进行多元组分气体物种化.
  • 该方法可适应不同的气体,光谱范围和光谱学类型.
  • 这项工作代表了在HITRAN模拟中训练有素的CNN用于气体混合物的光谱识别的首次应用.