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Related Concept Videos

Principles Of Column Chromatography01:13

Principles Of Column Chromatography

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

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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...
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Gas Chromatography: Sample Injection Systems01:08

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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.
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Column Efficiency: Rate Theory01:12

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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.
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Optimizing Chromatographic Separations01:15

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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...
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High-Performance Liquid Chromatography: Elution Process01:05

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In High-Performance Liquid Chromatography (HPLC), the elution process is critical to the separation of analytes and the quality of chromatographic results. Elution describes how compounds move through the column and separate based on their interactions with the mobile and stationary phases. This process determines the resolution, peak shape, and retention times in the chromatogram, which are essential for identifying and quantifying components in complex mixtures. Understanding the elution...
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Automated Hydrophobic Interaction Chromatography Column Selection for Use in Protein Purification
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Understanding operational system differences for transfer of miniaturized chromatography column data using

William R Keller1, Steven T Evans2, Gisela Ferreira2

  • 1Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY 12180, United States.

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Summary

Miniature chromatography columns (MiniColumns) can accurately model larger systems when simulation tools account for system differences. This allows for reliable scale-down predictions in bioprocess development.

Keywords:
Column modelingHigh throughput screeningMiniColumnsScale-down models

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Area of Science:

  • Chromatography
  • Bioprocess Engineering
  • Chemical Engineering

Background:

  • Miniature chromatography columns (MiniColumns) are increasingly used for bioprocess development.
  • Differences between MiniColumns and traditional benchtop systems can impact experimental outcomes.
  • Accurate scale-down modeling requires understanding and incorporating these system variations.

Purpose of the Study:

  • To identify and simulate differences between MiniColumns and benchtop chromatography systems.
  • To assess the comparability of MiniColumns to benchtop systems for protein separation.
  • To develop a predictive model for benchtop performance using MiniColumn data.

Main Methods:

  • Utilized a simulation tool to model chromatography experiments.
  • Incorporated multi-step gradients and fraction collection into simulations.
  • Performed step elution experiments with binary mixtures (monoclonal antibody and model proteins).
  • Analyzed linear gradient profiles, first moments, and peak elution characteristics.

Main Results:

  • Simulations with multi-step gradients improved agreement with experimental data for MiniColumns.
  • MiniColumns showed qualitatively similar peak shapes but earlier elution compared to benchtop systems.
  • Benchtop systems demonstrated improved separation for overlapping peaks.
  • Simulations identified increased eluent breakthrough dispersion in benchtop systems as a key difference.
  • Benchtop performance was accurately predicted using MiniColumn-derived parameters within the simulation.

Conclusions:

  • Accounting for system differences in simulations is crucial for accurate MiniColumn scale-down modeling.
  • MiniColumns can serve as effective scale-down models when simulation parameters are appropriately adjusted.
  • This approach facilitates the implementation of MiniColumns in bioprocess development and optimization.