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–Mass Spectrometry (GC–MS)01:14

Gas Chromatography–Mass Spectrometry (GC–MS)

7.0K
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....
7.0K
Gas Chromatography: Introduction01:13

Gas Chromatography: Introduction

4.2K
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...
4.2K
Gas Chromatography: Sample Injection Systems01:08

Gas Chromatography: Sample Injection Systems

1.7K
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...
1.7K
Gas Chromatography: Types of Columns and Stationary Phases01:17

Gas Chromatography: Types of Columns and Stationary Phases

2.6K
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...
2.6K
Gas Chromatography: Overview of Detectors01:13

Gas Chromatography: Overview of Detectors

2.1K
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...
2.1K
Optimizing Chromatographic Separations01:15

Optimizing Chromatographic Separations

1.1K
Optimizing chromatographic separations is crucial for obtaining clean separations in a minimum amount of time. Optimization is required for several factors, including kinetic effects related to band broadening, plate height, capacity factor, and separation factor.
Band broadening refers to spreading solute bands as they travel through the column. This broadening can impact resolution. Plate height (H) represents the length required for one theoretical plate. A lower plate height corresponds to...
1.1K

You might also read

Related Articles

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

Sort by
Same author

New equations of column performance in liquid chromatography.

Journal of chromatography. A·2026
Same author

Kinetic properties of open rectangular columns (based on Poppe data).

Journal of chromatography. A·2025
Same author

Relation between dimensionless parameters of LC columns.

Journal of chromatography. A·2025
Same author

Generic gradient elution RPLC analyses.

Journal of chromatography. A·2025
Same author

Knox-Saleem kinetic performance limits in liquid chromatography. Part 2: Alternative interpretation - No upper limit to separation performance.

Journal of chromatography. A·2025
Same author

Chromatographic parameters: Transport efficiency-A meaningful alternative to the plate number parameter.

Journal of chromatography. A·2024

Related Experiment Video

Updated: Feb 23, 2026

Qualitative Characterization of the Aqueous Fraction from Hydrothermal Liquefaction of Algae Using 2D Gas Chromatography with Time-of-flight Mass Spectrometry
11:44

Qualitative Characterization of the Aqueous Fraction from Hydrothermal Liquefaction of Algae Using 2D Gas Chromatography with Time-of-flight Mass Spectrometry

Published on: March 6, 2016

9.8K

Flow optimization in one-dimensional and comprehensive two-dimensional gas chromatography.

Leonid M Blumberg1

  • 1Advachrom, P.O. Box 1243, Wilmington, DE 19801, USA.

Journal of Chromatography. A
|September 4, 2017
PubMed
Summary
This summary is machine-generated.

Optimizing gas chromatography (GC) flow rates is crucial for efficient analysis. Temperature-programmed GC columns require 30% lower optimal flow rates than isothermal ones, with specific recommendations for comprehensive two-dimensional GC (GC×GC) systems.

Keywords:
Default flow rateEfficiency-optimized flow rate (EOF)Flow reconciliation in GC × GCSpeed-optimized flow rate (SOF)

More Related Videos

Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography
10:14

Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography

Published on: September 2, 2020

5.5K
On-line Analysis of Nitrogen Containing Compounds in Complex Hydrocarbon Matrixes
07:49

On-line Analysis of Nitrogen Containing Compounds in Complex Hydrocarbon Matrixes

Published on: August 5, 2016

11.2K

Related Experiment Videos

Last Updated: Feb 23, 2026

Qualitative Characterization of the Aqueous Fraction from Hydrothermal Liquefaction of Algae Using 2D Gas Chromatography with Time-of-flight Mass Spectrometry
11:44

Qualitative Characterization of the Aqueous Fraction from Hydrothermal Liquefaction of Algae Using 2D Gas Chromatography with Time-of-flight Mass Spectrometry

Published on: March 6, 2016

9.8K
Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography
10:14

Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography

Published on: September 2, 2020

5.5K
On-line Analysis of Nitrogen Containing Compounds in Complex Hydrocarbon Matrixes
07:49

On-line Analysis of Nitrogen Containing Compounds in Complex Hydrocarbon Matrixes

Published on: August 5, 2016

11.2K

Area of Science:

  • Analytical Chemistry
  • Chromatography

Background:

  • Optimizing carrier gas flow rate is essential for achieving optimal separation efficiency in gas chromatography (GC).
  • Different GC operational modes, such as isothermal and temperature-programmed, have distinct flow rate requirements.
  • Comprehensive two-dimensional gas chromatography (GC×GC) involves a primary column and secondary columns, each with potentially different flow optimization needs.

Purpose of the Study:

  • To provide theoretical considerations and numerical recommendations for optimal flow rates in GC columns under various conditions.
  • To address the specific flow optimization challenges in comprehensive two-dimensional gas chromatography (GC×GC).
  • To offer solutions for reconciling flow rate differences between columns in GC×GC systems.

Main Methods:

  • Theoretical analysis of optimal flow dynamics in GC columns.
  • Development of a simplified equation for calculating optimal flow rates.
  • Investigation of flow rate relationships between primary and secondary columns in GC×GC.
  • Formulation of boundary conditions for column mismatch scenarios.

Main Results:

  • Optimal flow rate for temperature-programmed GC is approximately 30% lower than for isothermal GC in the same column.
  • A relationship (²d = 0.7¹d) is derived for complementary internal diameters (IDs) of GC×GC columns to achieve equal optimal flow rates.
  • Typical complementary ID pairs are tabulated.
  • Methods for reconciling differing optimal flow rates in mismatched GC×GC columns are considered and evaluated.

Conclusions:

  • Understanding and applying optimal flow rate principles is key to enhancing GC and GC×GC performance.
  • The derived relationships and methods provide practical guidance for optimizing GC×GC system configurations.
  • Addressing column flow mismatches is critical for maximizing the analytical power of GC×GC.