Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Classification of Titrimetric Analysis Based on Reaction Types01:01

Classification of Titrimetric Analysis Based on Reaction Types

799
Titrimetric analysis in solution chemistry involves measuring the volume of solutions and is often called volumetric analysis. The standard solution of known concentration in the burette is called the titrant, whereas the solution of unknown concentration in the flask is called the analyte, or titrand. Titrimetric analyses can be classified into four types based on the reactions between the titrant and analyte.
Titrations between an acid and a base lead to neutralization reactions that form...
799
Molecular Spectroscopy: Absorption and Emission01:14

Molecular Spectroscopy: Absorption and Emission

2.4K
Molecules possess discrete energy levels called quantum states. Unlike atoms, which have simpler energy levels, molecules possess additional rotational and vibrational energy levels.  Each energy level is separated by an energy gap, with the gaps between adjacent electronic, vibrational, and rotational levels varying significantly. The three types of energy levels in a diatomic molecule are shown in Figure 1.
2.4K
Gas Chromatography: Types of Detectors-II01:19

Gas Chromatography: Types of Detectors-II

462
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...
462
Gas Chromatography: Overview of Detectors01:13

Gas Chromatography: Overview of Detectors

676
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...
676
Gas Chromatography: Types of Detectors-I01:21

Gas Chromatography: Types of Detectors-I

544
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,...
544
UV–Vis Spectroscopy: Molecular Electronic Transitions01:16

UV–Vis Spectroscopy: Molecular Electronic Transitions

1.6K
In Ultraviolet–Visible (UV–Vis) spectroscopy, the absorption of electromagnetic radiation is used to probe the electronic structure of molecules. This technique provides insights into molecular electronic transitions, particularly the movement of electrons between different molecular orbitals. Radiation is absorbed if the energy of the electromagnetic radiation passing through the molecule is precisely equal to the energy difference between the excited and ground states. During this...
1.6K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A machine learning approach for non-invasive PCOS diagnosis from ultrasound and clinical features.

Scientific reports·2025
Same author

Deep Learning for Gas Sensing via Infrared Spectroscopy.

Sensors (Basel, Switzerland)·2024
Same author

Shock tube and chemical kinetic modeling study of the oxidation of 2,5-dimethylfuran.

The journal of physical chemistry. A·2013
Same author

An experimental and kinetic modeling study of the oxidation of the four isomers of butanol.

The journal of physical chemistry. A·2008
Same author

Experimental investigation of toluene + H --> benzyl + H2 at high temperatures.

The journal of physical chemistry. A·2006
Same author

High-temperature thermal decomposition of benzyl radicals.

The journal of physical chemistry. A·2006

Related Experiment Video

Updated: Aug 9, 2025

Infrared Degenerate Four-wave Mixing with Upconversion Detection for Quantitative Gas Sensing
10:42

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

Published on: March 22, 2019

6.3K

TSMC-Net: Deep-Learning Multigas Classification Using THz Absorption Spectra.

M Arshad Zahangir Chowdhury1, Timothy E Rice1, Matthew A Oehlschlaeger1

  • 1Department of Mechanical, Aerospace and Nuclear Engineering, Rensselaer Polytechnic Institute, Troy, New York 12180-3522, United States.

ACS Sensors
|February 23, 2023
PubMed
Summary
This summary is machine-generated.

A deep convolutional neural network, TSMC-Net, accurately identifies eight volatile organic compounds in gas mixtures using terahertz absorption spectra. This machine learning approach enhances gas sensing capabilities for complex multicomponent analysis.

Keywords:
THz spectroscopyclassificationconvolutional neural networkdeep learninggas mixturesspecies identification

More Related Videos

Author Spotlight: Exploring Light-Driven Chemical Reactions and Energy-Harnessing Devices in Photochemical Research
08:12

Author Spotlight: Exploring Light-Driven Chemical Reactions and Energy-Harnessing Devices in Photochemical Research

Published on: February 16, 2024

10.4K
Author Spotlight: Unveiling the Potential of VSFG Microscopy in Studying Mesoscopically Heterogeneous Self-Assembled Structures
08:49

Author Spotlight: Unveiling the Potential of VSFG Microscopy in Studying Mesoscopically Heterogeneous Self-Assembled Structures

Published on: December 1, 2023

1.5K

Related Experiment Videos

Last Updated: Aug 9, 2025

Infrared Degenerate Four-wave Mixing with Upconversion Detection for Quantitative Gas Sensing
10:42

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

Published on: March 22, 2019

6.3K
Author Spotlight: Exploring Light-Driven Chemical Reactions and Energy-Harnessing Devices in Photochemical Research
08:12

Author Spotlight: Exploring Light-Driven Chemical Reactions and Energy-Harnessing Devices in Photochemical Research

Published on: February 16, 2024

10.4K
Author Spotlight: Unveiling the Potential of VSFG Microscopy in Studying Mesoscopically Heterogeneous Self-Assembled Structures
08:49

Author Spotlight: Unveiling the Potential of VSFG Microscopy in Studying Mesoscopically Heterogeneous Self-Assembled Structures

Published on: December 1, 2023

1.5K

Area of Science:

  • Spectroscopy
  • Machine Learning
  • Chemical Sensing

Background:

  • Gas mixture speciation from complex absorption spectra is challenging.
  • Machine learning offers a promising approach to gas sensing problems.

Purpose of the Study:

  • Develop a deep convolutional neural network (TSMC-Net) for multigas classification.
  • Identify eight volatile organic compounds in mixtures using terahertz (THz) absorption spectra.

Main Methods:

  • Utilized a deep convolutional neural network (TSMC-Net) for classification.
  • Converted multilabel classification to multiclass classification via label powerset.
  • Trained and validated the model on simulated THz absorption spectra with and without noise.

Main Results:

  • TSMC-Net achieved high precision, recall, and accuracy for individual compounds.
  • Class activation maps visualized model decision-making and highlighted key spectral regions.
  • The model demonstrated effectiveness on measured THz absorption spectra.

Conclusions:

  • The developed deep convolutional neural network (TSMC-Net) is effective for gas mixture identification.
  • The approach is generalizable to other spectroscopy types, frequency ranges, and sensors.
  • This work advances machine learning applications in terahertz spectroscopy for gas sensing.