Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Size-Exclusion Chromatography

Size-Exclusion Chromatography

In size-exclusion chromatography (SEC), also known as molecular-exclusion or gel-permeation chromatography, molecules are separated based on their sizes. This technique is important for separating large molecules such as polymers and biomolecules. The two classes of micron-sized stationary phases encountered in SEC are silica particles and cross-linked polymer resin beads. Both materials are porous, but their pore sizes vary significantly.
Silica particles offer advantages such as rigidity,...

Analyte Adsorption and Distribution

Analyte Adsorption and Distribution

In certain chromatographic separations, solutes transfer between the mobile phase and the stationary phase via sorption, which typically refers to the process of adsorption. For many chromatographic systems, the sorption process often depends on the polarity of the compounds—an expression of the overall dipole moment within the molecule. During the separation process, there is competition between the solute and solvent for adsorption to the stationary phase. Highly polar compounds and solvents...

Silica Gel Column Chromatography: Overview

Silica Gel Column Chromatography: Overview

Silica gel column chromatography is a technique for separating compounds using a column packed with silica gel as the stationary phase. This method relies on differences in the polarity of compounds. Based on their polarities, compounds move between the stationary phase (silica gel) and the mobile phase (the solvent), forming discrete bands in the column.
Polar components tend to bind strongly to the silica gel, causing them to move slowly through the column. In contrast, nonpolar compounds...

Principles Of Column Chromatography

Principles Of Column Chromatography

The chromatography technique was first invented in 1901 by Michael S. Tswett, a Russian botanist, to separate plant pigments using organic solvents. Further, in 1941, Archer John Porter Martin and R. L. M. Synge modified the technique by packing silica gel into a column. A mixture of amino acids was then separated on the packed column using chloroform and water mixture as the mobile phase. This was the first report on column chromatography. At present, column chromatography is a widely used...

Column Efficiency: Rate Theory

Column Efficiency: Rate Theory

The rate theory of chromatography provides quantitative insight into the shapes and widths of elution bands. These bands are based on the random-walk mechanism governing molecular migration within a column. The Gaussian profile of chromatographic bands arises from the cumulative effect of random molecular motions as they progress through the column.
During elution, a solute molecule experiences numerous transitions between stationary and mobile phases, exhibiting irregular residence times in...

Ion Exchange

Ion Exchange

Ion exchange chromatography separates charged molecules from a solution by reversibly exchanging them with mobile, or 'active', ions associated with the oppositely charged stationary phase. This method can be used to separate ions, soften and deionize water, and purify solutions. The polymers comprising the ion-exchange column are high-molecular-weight and chemically stable polymers, crosslinked to be porous and essentially insoluble. They are also functionalized with either acidic or basic...

You might also read

Related Articles

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

Sort by

Same author

Prevalence of Rh, Kell, Kidd, Duffy, and MNS antigens in the Hispanic donor population of South Texas.

Immunohematology·2022

Same author

Lymphoid susceptibility to the Aggregatibacter actinomycetemcomitans cytolethal distending toxin is dependent upon baseline levels of the signaling lipid, phosphatidylinositol-3,4,5-triphosphate.

Molecular oral microbiology·2015

Same author

Abiotic and biotic dynamics during the initial stages of high solids switchgrass degradation.

Environmental technology·2011

Same author

Gene expression levels as endophenotypes in genome-wide association studies of Alzheimer disease.

Neurology·2010

Same author

Development and analysis of oxygen-sensing probes for in situ monitoring of unsaturated solid-state biodegradation processes.

Journal of the Air & Waste Management Association (1995)·1997

Same author

Engineering cellulase mixtures by varying the mole fraction of Thermomonospora fusca E5 and E3, Trichoderma reesei CBHI, and Caldocellum saccharolyticum beta-glucosidase.

Biotechnology and bioengineering·1993

Search research articles

Related Experiment Video

Updated: Jul 3, 2026

Cellular Membrane Affinity Chromatography Columns to Identify Specialized Plant Metabolites Interacting with Immobilized Tropomyosin Kinase Receptor B

Cellular Membrane Affinity Chromatography Columns to Identify Specialized Plant Metabolites Interacting with Immobilized Tropomyosin Kinase Receptor B

Published on: January 19, 2022

Solute exclusion from cellulose in packed columns: process modeling and analysis.

R P Neuman¹, L P Walker

¹Department of Agricultural and Biological Engineering, Riley-Robb Hall, Cornell University, Ithaca, New York 14853, USA.

Biotechnology and Bioengineering

|June 20, 1992

Summary

Process modeling accurately predicts solute exclusion from cellulose packed columns. The combined mass transfer and pore diffusion model is best for large particles and high velocities, aiding in ethanol plant design.

More Related Videos

Online Size-exclusion and Ion-exchange Chromatography on a SAXS Beamline

Online Size-exclusion and Ion-exchange Chromatography on a SAXS Beamline

Published on: January 5, 2017

OLIgo Mass Profiling (OLIMP) of Extracellular Polysaccharides

OLIgo Mass Profiling (OLIMP) of Extracellular Polysaccharides

Published on: June 20, 2010

Related Experiment Videos

Last Updated: Jul 3, 2026

Cellular Membrane Affinity Chromatography Columns to Identify Specialized Plant Metabolites Interacting with Immobilized Tropomyosin Kinase Receptor B

Cellular Membrane Affinity Chromatography Columns to Identify Specialized Plant Metabolites Interacting with Immobilized Tropomyosin Kinase Receptor B

Published on: January 19, 2022

Online Size-exclusion and Ion-exchange Chromatography on a SAXS Beamline

Online Size-exclusion and Ion-exchange Chromatography on a SAXS Beamline

Published on: January 5, 2017

OLIgo Mass Profiling (OLIMP) of Extracellular Polysaccharides

OLIgo Mass Profiling (OLIMP) of Extracellular Polysaccharides

Published on: June 20, 2010

Area of Science:

Chemical Engineering
Separation Science
Process Modeling

Background:

Solute exclusion from cellulose is critical in biorefining.
Understanding mass transport and pore diffusion is key for efficient separation.

Purpose of the Study:

To model and analyze solute exclusion behavior in cellulose packed columns.
To evaluate the effectiveness of different mathematical models under varying conditions.

Main Methods:

Utilized process modeling and analysis.
Developed and tested three mathematical models: equilibrium, mass transfer, and combined mass transfer/pore diffusion.
Validated models against experimental data with varying cellulose particle sizes and solution velocities.

Main Results:

All models accurately predicted elution curves for small cellulose particles (200-300 mesh).
Mass transfer and combined models were superior for larger particles (45-60 mesh).
The combined model accurately represented column behavior at high velocities with large particles.

Conclusions:

The combined mass transfer and pore diffusion model is most effective for complex column behaviors.
This model can optimize solute exclusion experiments for industrial applications, like cellulose-to-ethanol production.