Advancing HIC method development: Retention-time modeling and tuning selectivity with ternary mobile-phase systems.

The use of a ternary mobile-phase system comprising ammonium sulphate, sodium chloride, and phosphate buffer was explored to tune retention and enhance selectivity in hydrophobic interaction chromatography. The accuracy of the linear solvent-strength model to predict protein retention with the ternary mobile-phase system based on isocratic scouting runs is limited, as the extrapolated retention factor at aqueous buffer conditions (k) cannot be reliably established. The Jandera retention model utilizing a salt concentration averaged retention factor (k¯) in aqueous buffer for ternary systems overcomes this bottleneck.

Enhancing LC×LC separations through multi-task Bayesian optimization.

Method development in comprehensive two-dimensional liquid chromatography (LC×LC) is a challenging process. The interdependencies between the two dimensions and the possibility of incorporating complex gradient profiles, such as multi-segmented gradients or shifting gradients, make trial-and-error method development time-consuming and highly dependent on user experience. Retention modeling and Bayesian optimization (BO) have been proposed as solutions to mitigate these issues.

Computer-driven optimization of complex gradients in comprehensive two-dimensional liquid chromatography.

Method development in comprehensive two-dimensional liquid chromatography (LC × LC) is a complicated endeavor. The dependency between the two dimensions and the possibility of incorporating complex gradient profiles, such as multi-segmented gradients or shifting gradients, renders method development by "trial-and-error" time-consuming and highly dependent on user experience. In this work, an open-source algorithm for the automated and interpretive method development of complex gradients in LC × LC-mass spectrometry (MS) was developed.

Algorithm for tracking peaks amongst numerous datasets in comprehensive two-dimensional chromatography to enhance data analysis and interpretation.

Analytical data processing often requires the comparison of data, i.e. finding similarities and differences within separations.