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Published on: July 25, 2014
Retention models for programmed gas chromatography
G Castello1, P Moretti, S Vezzani
1University of Genova, Dipartimento di Chimica e Chimica Industriale, Via Dodecaneso 31, Genova I-16146, Italy. castello@chimica.unige.it
This review covers models for predicting gas chromatography retention data under programmed conditions. It details methods correlating retention data with thermodynamics and optimizing programming rates for better separation.
Area of Science:
- Analytical Chemistry
- Separation Science
Background:
- Gas chromatography (GC) is a key separation technique.
- Predicting retention behavior under programmed conditions is crucial for method development.
Purpose of the Study:
- To review existing models for predicting retention times, temperatures, peak widths, and separation numbers in programmed temperature and pressure gas chromatography.
- To summarize correlations between retention data and thermodynamic parameters.
- To outline methods for determining optimal programming rates.
Main Methods:
- Review of empirical, thermodynamic, iterative, and statistical models for GC analysis.
- Analysis of experimental data from columns of varying polarity.
- Compilation of main equations, mathematical models, and calculation procedures.
Main Results:
- Models have evolved from empirical approaches to sophisticated thermodynamic and statistical methods.
- Correlations between retention data and thermodynamic parameters are well-established.
- Optimization of programming rates enhances chromatographic separation.
Conclusions:
- A comprehensive overview of predictive models for programmed GC is provided.
- The evolution of models reflects advancements in computational power and theoretical understanding.
- The reviewed methods support efficient method development and optimization in gas chromatography.
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