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

Gas Chromatography: Types of Detectors-II01:19

Gas Chromatography: Types of Detectors-II

442
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...
442
Gas Chromatography: Types of Detectors-I01:21

Gas Chromatography: Types of Detectors-I

507
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,...
507
Gas Chromatography: Overview of Detectors01:13

Gas Chromatography: Overview of Detectors

658
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...
658

You might also read

Related Articles

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

Sort by
Same author

Effects of External Load and Holding Duration on PAPE and Muscle Activation During Isometric Split Squat Conditioning Activity.

Medicina (Kaunas, Lithuania)·2026
Same author

Comparative effectiveness of dexmedetomidine and meperidine for the treatment of postanesthetic shivering: a propensity score-matched retrospective cohort study.

BMC anesthesiology·2026
Same author

Scalable multiplexed machine learning gas sensor chips for food classification.

Science advances·2026
Same author

Wireless, battery-free wearable optoelectronic-colorimetric microfluidic sensor for multiplex sweat analysis.

Device·2026
Same author

New generic classification for Korean Peucedanum L. (Apiaceae) species based on deep phylogenomic study.

BMC plant biology·2026
Same author

Genetic dissection and transcriptomic analysis of a novel high-tillering phenotype in rice derived from weedy rice (Hapcheonaengmi3) and Tongil-type Rice (Milyang23).

The plant genome·2026

Related Experiment Video

Updated: Aug 2, 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

Deep-learning-based gas identification by time-variant illumination of a single micro-LED-embedded gas sensor.

Incheol Cho1, Kichul Lee1, Young Chul Sim2

  • 1Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea.

Light, Science & Applications
|April 18, 2023
PubMed
Summary
This summary is machine-generated.

A novel photoactivated gas sensor uses time-variant illumination and deep neural networks to overcome electronic nose cross-reactivity. This single sensor accurately identifies and quantifies toxic gases with low power consumption.

More Related Videos

Flexible Measurement of Bioluminescent Reporters Using an Automated Longitudinal Luciferase Imaging Gas- and Temperature-optimized Recorder ALLIGATOR
10:33

Flexible Measurement of Bioluminescent Reporters Using an Automated Longitudinal Luciferase Imaging Gas- and Temperature-optimized Recorder ALLIGATOR

Published on: December 13, 2017

8.0K
Luminescence Lifetime Imaging of O2 with a Frequency-Domain-Based Camera System
08:35

Luminescence Lifetime Imaging of O2 with a Frequency-Domain-Based Camera System

Published on: December 16, 2019

9.3K

Related Experiment Videos

Last Updated: Aug 2, 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
Flexible Measurement of Bioluminescent Reporters Using an Automated Longitudinal Luciferase Imaging Gas- and Temperature-optimized Recorder ALLIGATOR
10:33

Flexible Measurement of Bioluminescent Reporters Using an Automated Longitudinal Luciferase Imaging Gas- and Temperature-optimized Recorder ALLIGATOR

Published on: December 13, 2017

8.0K
Luminescence Lifetime Imaging of O2 with a Frequency-Domain-Based Camera System
08:35

Luminescence Lifetime Imaging of O2 with a Frequency-Domain-Based Camera System

Published on: December 16, 2019

9.3K

Area of Science:

  • Chemical Sensors
  • Artificial Intelligence
  • Optoelectronics

Background:

  • Electronic nose (e-nose) technology faces challenges with chemoresistive sensor cross-reactivity.
  • Existing e-nose systems require complex sensor arrays for gas identification.
  • Smart factories and personal health monitoring demand efficient gas sensing solutions.

Purpose of the Study:

  • To develop a novel sensing strategy for selective gas identification and quantification.
  • To overcome the cross-reactivity limitations of traditional chemoresistive sensors.
  • To enhance the efficiency of e-nose technology in terms of cost, space, and power.

Main Methods:

  • A micro-LED (μLED)-embedded photoactivated (μLP) gas sensor was designed.
  • Time-variant illumination from the μLED generated forced transient sensor responses.
  • A deep neural network analyzed transient signals for gas detection and concentration estimation.

Main Results:

  • The single μLP gas sensor achieved high classification accuracy (~96.99%) for toxic gases.
  • Quantification accuracy (mean absolute percentage error ~ 31.99%) was demonstrated for various gases.
  • The sensor system operated with a low power consumption of 0.53 mW.

Conclusions:

  • The proposed μLP gas sensor with time-variant illumination offers a novel solution for selective gas sensing.
  • Deep neural network analysis enables accurate gas identification and concentration estimation from transient signals.
  • This approach significantly improves e-nose efficiency, reducing cost, space, and power requirements.