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Related Concept Videos

Gas Chromatography: Types of Detectors-II01:19

Gas Chromatography: Types of Detectors-II

1.1K
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...
1.1K
Gas Chromatography: Overview of Detectors01:13

Gas Chromatography: Overview of Detectors

1.8K
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...
1.8K
Gas Chromatography: Types of Detectors-I01:21

Gas Chromatography: Types of Detectors-I

1.4K
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,...
1.4K
Gas Chromatography–Mass Spectrometry (GC–MS)01:14

Gas Chromatography–Mass Spectrometry (GC–MS)

6.4K
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....
6.4K
Gas Chromatography: Sample Injection Systems01:08

Gas Chromatography: Sample Injection Systems

1.3K
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...
1.3K

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Quantitative Detection of Trace Explosive Vapors by Programmed Temperature Desorption Gas Chromatography-Electron Capture Detector
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Autonomous Hazardous Gas Detection Systems: A Systematic Review.

Boon-Keat Chew1, Azwan Mahmud1, Harjit Singh2

  • 1Faculty of Artificial Intelligence and Engineering, Multimedia University, Cyberjaya 63100, Selangor, Malaysia.

Sensors (Basel, Switzerland)
|November 13, 2025
PubMed
Summary
This summary is machine-generated.

This review explores reducing manual calibration for gas detection systems (GDSs) in semiconductor facilities. Advanced analytics and machine learning can correct sensor drift, improving safety and reliability.

Keywords:
autonomous calibrationautonomous systemscalibration driftcross-sensitivitygas detection algorithmsgas sensorsmachine learningmultivariate analysissensor calibration techniques

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Area of Science:

  • Industrial Safety
  • Sensor Technology
  • Data Analytics

Background:

  • Gas Detection Systems (GDSs) are vital for monitoring hazardous gases in semiconductor fabrication.
  • Consumable sensors (electrochemical, metal oxide semiconductor) in GDSs degrade over time, causing accuracy drift and cross-sensitivity.
  • Manual calibration is essential but resource-intensive, time-consuming, and error-prone.

Purpose of the Study:

  • To systematically review methods for minimizing or eliminating manual calibration dependency in GDSs.
  • To explore the application of data analytics and machine learning for sensor performance correction.
  • To investigate the potential of calibration-free and self-correcting gas sensor systems.

Main Methods:

  • Systematic literature review (PROSPERO Registration number: 1166004).
  • Analysis of techniques including Principal Component Analysis (PCA), Support Vector Machines (SVMs), and multivariate regression.
  • Exploration of calibration transfer and sensor data synchronization.

Main Results:

  • Data analytics and machine learning can correct sensor accuracy drift and enhance gas selectivity.
  • Techniques like PCA and SVMs effectively differentiate target gases and compensate for aging and environmental variability.
  • Progress is being made towards integrating calibration-free or self-correcting sensor systems.

Conclusions:

  • Advanced data analysis offers a viable alternative to manual calibration for GDSs.
  • Key challenges include understanding sensor drift dynamics and synchronizing multi-sensor data.
  • Future research should focus on application-specific datasets, adaptive models, and hybrid validation for intelligent GDSs.