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Related Concept Videos

Gas Chromatography: Overview of Detectors01:13

Gas Chromatography: Overview of Detectors

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

Gas Chromatography: Types of Detectors-I

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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Gas Chromatography: Types of Detectors-II01:19

Gas Chromatography: Types of Detectors-II

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...

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Engineering Molecular Recognition with Bio-mimetic Polymers on Single Walled Carbon Nanotubes
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Predictive mixed-gas detection using rGO/In2O3 nanocomposite sensors assisted by machine learning.

Tanya Sood1, Saikat Chattopadhyay2, P Poornesh1

  • 1Manipal Institute of Technology, Manipal Academy of Higher Education Manipal India poornesh.p@manipal.edu poorneshp@gmail.com.

Nanoscale Advances
|February 20, 2026
PubMed
Summary

This study developed a hybrid reduced graphene oxide/indium oxide sensor for detecting gases at ultra-low levels. A machine learning framework enabled accurate identification and concentration prediction of multiple gases simultaneously, even in complex mixtures.

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Area of Science:

  • Materials Science
  • Nanotechnology
  • Sensor Technology

Background:

  • Chemiresistive gas sensors face challenges in analyte selectivity and sub-ppm detection limits.
  • Hybrid materials combining reduced graphene oxide (rGO) and metal oxides offer enhanced sensitivity at ultralow concentrations.

Purpose of the Study:

  • To develop a highly sensitive and selective gas sensor using rGO/In2O3 nanocomposites.
  • To implement a machine-intelligent framework for simultaneous gas identification and concentration prediction in mixed environments.

Main Methods:

  • Synthesized rGO via a modified Hummers' method and incorporated it into nanocrystalline In2O3.
  • Fabricated rGO/In2O3 thin films using spin coating and post-deposition annealing.
  • Employed a machine-intelligent framework analyzing dynamic response curves for gas analysis.

Main Results:

  • Optimized rGO/In2O3 sensor demonstrated stability and a 100 ppb detection limit for H2S.
  • Machine learning framework achieved 99.7% accuracy in distinguishing gas clusters.
  • Accurate prediction of H2S, NH3, and CO concentrations in mixed environments was achieved.

Conclusions:

  • The rGO/In2O3 hybrid material enhances gas sensing performance.
  • The machine-intelligent framework enables robust, simultaneous multi-gas detection and quantification.
  • This integrated platform advances smart, ultra-low-level gas sensing for complex real-world applications.