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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).
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Updated: Jul 27, 2025

Construction and Characterization of External Cavity Diode Lasers for Atomic Physics

Construction and Characterization of External Cavity Diode Lasers for Atomic Physics

Published on: April 24, 2014

Laser Linewidth Analysis and Filtering/Fitting Algorithms for Improved TDLAS-Based Optical Gas Sensor.

Chen Tong¹, Chaotan Sima^1,2, Muqi Chen¹

¹Next Generation Internet Access National Engineering Research Center, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China.

Sensors (Basel, Switzerland)

|June 10, 2023

Summary

This summary is machine-generated.

This study introduces an advanced Tunable Diode Laser Absorption Spectroscopy (TDLAS) system. It enhances gas sensing accuracy by analyzing laser linewidth and employing novel filtering and neural network fitting algorithms.

Keywords:

TDLAS VMD-SG algorithm laser linewidth noise floor optical gas sensor

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Infrared Degenerate Four-wave Mixing with Upconversion Detection for Quantitative Gas Sensing

Infrared Degenerate Four-wave Mixing with Upconversion Detection for Quantitative Gas Sensing

Published on: March 22, 2019

Area of Science:

Optical Engineering
Spectroscopy
Analytical Chemistry

Background:

Tunable Diode Laser Absorption Spectroscopy (TDLAS) is crucial for real-time trace gas monitoring.
Existing TDLAS systems face challenges with noise and fitting accuracy.
Optimization of TDLAS performance is essential for reliable in situ measurements.

Purpose of the Study:

To propose and demonstrate an advanced TDLAS-based optical gas sensing system.
To improve the accuracy and reduce noise in TDLAS measurements.
To enhance the fitting accuracy of gas concentration detection.

Main Methods:

Incorporation of laser linewidth analysis into the TDLAS harmonic detection model.
Development of an adaptive Variational Mode Decomposition-Savitzky Golay (VMD-SG) filtering algorithm for noise reduction.
Application of a Radial Basis Function (RBF) neural network for improved data fitting.

Main Results:

The VMD-SG filter reduced background noise variance by approximately 31% and signal jitters by 12.5%.
The RBF neural network achieved enhanced fitting accuracy, with an absolute error below 50 ppmv (0.6%) for methane up to 8000 ppmv.
The system demonstrated superior performance compared to traditional linear fitting and least squares methods.

Conclusions:

The proposed advanced TDLAS system significantly improves gas sensing accuracy and noise reduction.
The integration of laser linewidth analysis, VMD-SG filtering, and RBF neural network offers a robust solution.
This technique is universally compatible with existing TDLAS sensors, enabling hardware-free optimization.