Resolution optimisation in micellar electrokinetic chromatography using empirical models

Area of Science:

• Analytical Chemistry
• Separation Science

Background:

• Micellar electrokinetic chromatography (MEKC) is a powerful separation technique.
• Optimizing resolution is crucial for effective MEKC analysis.

Purpose of the Study:

• To compare empirical modeling approaches for optimizing MEKC resolution.
• To identify the most suitable modeling strategy for predicting separation characteristics.

Main Methods:

• Utilized empirical models to predict migration and separation characteristics in MEKC.
• Compared different empirical approaches, focusing on modeling effective mobility and apparent mobility.
• Evaluated prediction errors for models of effective mobility, apparent mobility, resolution, and separation factor.

Main Results:

• Models of effective mobility combined with average plate numbers showed 2-4 times smaller prediction errors than apparent mobility models.
• For closely migrating peaks, effective and apparent mobility models yielded approximately half the prediction error compared to resolution and separation factor models.
• Predictions of the separation factor generally had lower errors than predictions of resolution.

Conclusions:

• Empirical modeling, particularly using effective mobility, is a viable strategy for optimizing MEKC separation buffers.
• Despite large prediction errors for closely migrating compounds, this approach aids in achieving optimal buffer compositions.