Current Projects and Interests

Dr. Stoll's educational background covers biology and chemistry, with a current (since 2000) focus on separation science. The active projects in his laboratory are all ultimately focused on the development of multi-dimensional liquid chromatography, as described below. Because of the complexity of multi-dimensional methods, and the number of experimental variables involved, some projects (e.g., Selectivity in RPLC, and Optimization of LC Performance) necessarily play a supporting role in achieving the overall goals of the Stoll Group.

Multi-dimensional Liquid Chromatography

Dr. Stoll's interest in multi-dimensional chromatography (MDLC) was initiated when he was a graduate student, working with Professor Peter Carr at the University of Minnesota. From the start he was been intrigued and motivated by: 1) the potential of MDLC to extend the capabilities of HPLC to handle more complicated samples (e.g., blood, urine, plant extracts) than has been possible with conventional one-dimensional methods; and 2) the challenge of building and optimizing MDLC instrumentation and methodologies to extend the state-of-the-art. While working with Prof. Carr he developed and studied Fast, Comprehensive Two-Dimensional HPLC (LC x LC). In this work he implemented the principles of High Temperature Liquid Chromatography, and studied the limitations to fast gradient elution reversed-phase HPLC. These steps enabled dramatic improvements in the speed of second dimension separations, and thus the overall productivity of LC x LC.

More recent work in the Stoll Laboratory at Gustavus Adolphus College has been focused primarily on innovative methodologies that improve the effectiveness and practicality of 2D-LC separations. This work has been highly collaborative with Agilent Technologies, and has resulted in the development of technologies including selective comprehensive 2D separation (sLCxLC), and Active Solvent Modulation (ASM).

Selectivity in Liquid Chromatography

One of the most important variables in 2D-LC is the choice of separation modes in the first and second dimension separations. In 2012 the Stoll Laboratory assumed responsibility for maintenance and further development of the Hydrophobic Substraction Model (HSM) of reversed-phase selectivity originally developed by Lloyd Snyder, John Dolan, and Peter Carr. This project, which started in the early 2000's, and has been very successful due in large part to broad involvement by column manufacturers, has aimed to characterize all RPLC columns using a five-parameter linear model that is based on intuitive solute-stationary phase interactions. As of August, 2019, about 700 columns have been characterized, and the data resulting from these characterizations are freely available at the website of the United States Pharmacopoeia, as well as a site maintained by the Stoll Lab (www.hplccolumns.org). Please contact Dr. Stoll if you are interested in having your columns characterized and listed in the database, or are interested in the project in other ways.

Fundamental Aspects of  LC

Given the continuously changing landscape of HPLC particle and column technology, and the fact that very different conditions are usually used in the two dimensions of 2D-LC systems, the Stoll Laboratory is also inherently interested in the principles of performance optimization in HPLC. In the last decade or so, we have seen a transformation in the HPLC column market, with multiple vendors now offering sub-two-micron fully porous particles, in addition to multiple offerings of superficially porous particles. During the same time period, we have seen a transformation in HPLC instrumentation, exemplified by the departure from the long-standing 6,000 psi pressure limit to instruments with 15 to 20,000 psi limits from several vendors at this time. Along with this change in pressure capability have come improvements in the extra-column dispersion and gradient delay volume characteristics of the instruments, which are particularly important when using many of the new particle technologies. As these technologies change and develop, there are numerous opportunities to gain new insights into how LC works, and potential directions for future developments.