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: Introduction01:13

Gas Chromatography: Introduction

2.7K
Gas chromatography (GC) is a technique for separating and analyzing volatile compounds in a sample. Its primary purpose is to identify and quantify components in complex mixtures, making it essential in fields such as environmental analysis, pharmaceuticals, and petrochemicals. GC is also called vapor-phase chromatography (VPC) or gas-liquid partition chromatography (GLPC).
In GC,  a sample is vaporized and mixed with an inert carrier gas (the mobile phase), which transports it through a...
2.7K
Gas Chromatography–Mass Spectrometry (GC–MS)01:14

Gas Chromatography–Mass Spectrometry (GC–MS)

5.2K
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....
5.2K
Gas Chromatography: Types of Detectors-I01:21

Gas Chromatography: Types of Detectors-I

898
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,...
898
Gas Chromatography: Sample Injection Systems01:08

Gas Chromatography: Sample Injection Systems

899
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...
899
Gas Chromatography: Types of Columns and Stationary Phases01:17

Gas Chromatography: Types of Columns and Stationary Phases

1.4K
Gas chromatography (GC) relies on stationary phases to separate and analyze components in a sample. There are two main types of stationary phases: liquid and solid. Liquid stationary phases are non-volatile, thermally stable, and chemically inert liquids coated onto the column. Solid stationary phases are particles of adsorbent material, such as silica gel or molecular sieves.
For an analyte to remain on the column for a sufficient amount of time, it must exhibit some level of compatibility (or...
1.4K
Diffusion on Chromatography Columns01:07

Diffusion on Chromatography Columns

882
In column chromatography, when an analyte is introduced as a narrow band at the top of the column, the solutes begin to separate and broaden, developing a Gaussian profile. This broadening occurs due to various factors, such as longitudinal diffusion.
Longitudinal diffusion occurs when the solute molecules in the mobile phase diffuse from the more concentrated center of the chromatographic band to the more dilute regions on either side, both towards and against the flow direction. This...
882

You might also read

Related Articles

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

Sort by
Same author

Silicon Dioxide Multi-Mode Interference Spectrometers.

Micromachines·2026
Same author

Review of Recent Optofluidic Devices.

Micromachines·2026
Same author

Single molecule nanopore counting assay targeting small extracellular vesicle cargo for non-invasive monitoring of cerebral organoid development and health.

Scientific reports·2025
Same author

Air Core ARROW Waveguides Fabricated in a Membrane-Covered Trench.

Photonics·2025
Same author

Drop Dynamics during Condensation on Superhydrophobic Surfaces in Vapor Shear Flow.

Langmuir : the ACS journal of surfaces and colloids·2025
Same author

Drop Retention and Departure in Adiabatic Shear Flow on Structured Superhydrophobic Surfaces.

Langmuir : the ACS journal of surfaces and colloids·2024

Related Experiment Video

Updated: Oct 23, 2025

Quantitative Detection of Trace Explosive Vapors by Programmed Temperature Desorption Gas Chromatography-Electron Capture Detector
07:57

Quantitative Detection of Trace Explosive Vapors by Programmed Temperature Desorption Gas Chromatography-Electron Capture Detector

Published on: July 25, 2014

20.2K

Comparison of the Dynamic Thermal Gradient to Temperature-Programmed Conditions in Gas Chromatography Using a

Samuel Avila1, H Dennis Tolley2, Brian D Iverson1

  • 1Department of Mechanical Engineering, Brigham Young University, Provo, Utah 84602, United States.

Analytical Chemistry
|August 18, 2021
PubMed
Summary

Dynamic thermal gradient gas chromatography (GC) offers improved hydrocarbon separation. Optimized dynamic conditions enhance resolution (Rs) by up to 13% over traditional temperature-programmed GC.

More Related Videos

Identification and Quantification of Decomposition Mechanisms in Lithium-Ion Batteries; Input to Heat Flow Simulation for Modeling Thermal Runaway
11:25

Identification and Quantification of Decomposition Mechanisms in Lithium-Ion Batteries; Input to Heat Flow Simulation for Modeling Thermal Runaway

Published on: March 7, 2022

4.8K
Characterization of Thermal Transport in One-dimensional Solid Materials
05:20

Characterization of Thermal Transport in One-dimensional Solid Materials

Published on: January 26, 2014

17.8K

Related Experiment Videos

Last Updated: Oct 23, 2025

Quantitative Detection of Trace Explosive Vapors by Programmed Temperature Desorption Gas Chromatography-Electron Capture Detector
07:57

Quantitative Detection of Trace Explosive Vapors by Programmed Temperature Desorption Gas Chromatography-Electron Capture Detector

Published on: July 25, 2014

20.2K
Identification and Quantification of Decomposition Mechanisms in Lithium-Ion Batteries; Input to Heat Flow Simulation for Modeling Thermal Runaway
11:25

Identification and Quantification of Decomposition Mechanisms in Lithium-Ion Batteries; Input to Heat Flow Simulation for Modeling Thermal Runaway

Published on: March 7, 2022

4.8K
Characterization of Thermal Transport in One-dimensional Solid Materials
05:20

Characterization of Thermal Transport in One-dimensional Solid Materials

Published on: January 26, 2014

17.8K

Area of Science:

  • Analytical Chemistry
  • Chromatography

Background:

  • Traditional temperature-programmed gas chromatography (GC) faces limitations in optimizing separation resolution.
  • Static thermal gradients show uneven improvements for different analytes, necessitating dynamic approaches.

Purpose of the Study:

  • To compare dynamic thermal gradient GC with temperature-programmed GC for hydrocarbon separation.
  • To evaluate the impact of dynamic thermal gradients on chromatographic peak characteristics and resolution.

Main Methods:

  • Utilized a stochastic transport model to simulate peak characteristics for C12-C40 hydrocarbon separation.
  • Compared dynamic thermal gradient GC with temperature-programmed GC under conditions of equal analyte retention times.
  • Investigated optimized dynamic thermal gradient profiles, holding the gradient fixed while increasing overall temperature over time.

Main Results:

  • Optimized dynamic thermal gradient GC improved resolution (Rs) by up to 13% compared to temperature-programmed GC, even with ideal injections.
  • All simulated analytes exhibited enhanced resolution and slightly reduced retention times under dynamic thermal gradient conditions.
  • Dynamic thermal gradients aim for constant analyte velocities during active separation phases.

Conclusions:

  • Dynamic thermal gradient GC provides a significant improvement in chromatographic resolution for hydrocarbon separations.
  • This method offers a valuable alternative to traditional temperature-programmed GC, yielding better separation efficiency and shorter analysis times.