Goali: technologies to support the new ERA of liquid chromatography research

Grant Award Summary

The National Science Foundation's Division of Chemistry, under the Mathematical and Physical Sciences program (CFDA 47.049), awarded $442,515 to Gustavus Adolphus College for a three-year project (August 1, 2025 – July 31, 2028) titled "GOALI: Technologies to Support the New Era of Liquid Chromatography Research." This academic-industry collaboration with Agilent Technologies, led by Professor Dwight Stoll, focuses on developing innovative technologies in two critical areas of liquid chromatography (LC): novel mobile phase delivery systems (pumping mechanisms) and advanced detection methods for separated compounds. These technological innovations will enable faster liquid chromatography method development, more efficient and reliable analyses, and facilitate the integration of machine learning and artificial intelligence into LC techniques—applications relevant across pharmaceuticals, materials science, and biomarker discovery.

The project delivers both research and educational products. On the research side, the team addresses current technology gaps that hinder high-quality retention data acquisition, which is essential for advancing machine learning applications in LC. On the educational side, Professor Stoll engages high school and undergraduate students in cutting-edge research activities and develops simulation tools to enhance liquid chromatography learning and technique optimization across multiple sectors of the U.S. economy. The collaborative effort targets workforce development while advancing the scientific capabilities required for next-generation chemical analyses.

Abstract

With support from the chemical measurement and imaging program in the division of chemistry, and in collaboration with agilent technologies, professor dwight stoll and his group at gustavus adolphus college are working to develop innovative technologies that enable the next generation of cutting-edge chemical analyses involving liquid chromatography (LC). The LC technique is used widely in a variety of fields to discover the makeup of various samples. For example, LC can be used to understand the composition and chemistry of innovative materials (e.g., biodegradable plastics), determine concentrations of biomarkers of disease in fluids such as urine or plasma, and determine impurities or breakdown products in life-saving medicines. This academic/industrial collaboration seeks to develop novel approaches to mobile phase delivery (i.e., pumping systems) and detection of separated compounds ? Two of the key elements of any LC process ? Which will in turn enable new, faster ways to develop LC methods, and more efficient and reliable analyses. The data that these approaches produce will accelerate the widespread integration of machine learning and artificial intelligence into LC techniques. DR. Stoll involves students at the high school and undergraduate levels in this cutting-edge research to prepare them to become the workforce of the future. He also develops simulations to facilitate learning about liquid chromatography and the more effective use of the technique across many sectors of the U.S. Economy. Currently, a major impediment to wide use of machine learning and artificial intelligence in liquid chromatography (LC) analyses is the paucity of high-quality retention data. Recent work in the stoll laboratory has yielded methodology for increasing the rate of acquisition of high-quality retention data, however this work has also identified technology gaps that prevent further development toward simultaneous improvement of retention data quality, and retention data acquisition rate. This collaborative research project will address these gaps through the development of two key technologies: 1) a novel, specialized flow path design for physically registering analytes both entering and exiting the LC column, in order to enhance retention data precision; and 2) mobile phase delivery technology that enables ultra-fast gradient elution separations by application of a constant pressure solvent delivery mode, in order to eliminate the gradient formation errors originating from rapid pressure changes. We anticipate that these technologies will routinely facilitate high-quality LC measurements on the sub-5-second timescale, both for the purpose of building large retention databases, and in applications such as high-throughput screening laboratories. The large datasets yielded by these technologies will in turn be used by stoll?s group to continue development of predictive simulation tools that support increased autonomous operation of LC instruments, and increase the efficiency of research by focusing experimental lab-based LC measurements on conditions most likely to be useful. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria.- subawards are not planned for this award.