Predicting retention in reverse-phase liquid chromatography at different mobile phase compositions and temperatures...

Area of Science:

• Analytical Chemistry
• Chromatography
• Physical Chemistry

Background:

• Reverse-phase liquid chromatography (RPLC) is a vital separation technique.
• Predicting solute retention under varying conditions (mobile phase composition, temperature) is challenging but essential for method optimization.
• Existing models often require extensive re-optimization for new conditions.

Purpose of the Study:

• To investigate the predictive power of the solvation parameter model in RPLC.
• To develop a general model for predicting solute retention factor (logk) on an octadecylsilica stationary phase.
• To evaluate the model's accuracy across diverse mobile phase compositions and temperatures.

Main Methods:

• Established linear relationships between logk and solute parameters using a training set of solutes.
• Developed a general retention model incorporating mobile phase composition and temperature.
• Validated the model using a separate test set of 30 diverse solutes and compared predicted vs. experimental logk values.

Main Results:

• The solvation parameter model demonstrated strong predictive capability for solute retention.
• Two distinct prediction procedures yielded accurate logk values.
• Excellent agreement between predicted and experimental logk was achieved for a diverse range of solutes.

Conclusions:

• The developed general retention model effectively predicts solute retention in RPLC.
• The model offers a robust tool for optimizing chromatographic methods by forecasting retention across different conditions.
• This approach simplifies method development and reduces experimental effort in RPLC.