Preview of the 2024 HPLC Keynote Sessions

Colorado State Capitol Building & the City of Denver Colorado at Sunset © nick - stock.adobe.com.

The 52nd International Symposium on High Performance Liquid Phase Separations and Related Techniques (HPLC 2024) will take place from July 20-25, 2024, in Denver, Colorado. The conference will bring together separation scientists from around the world to discuss the latest research and trends in high performance liquid chromatography (HPLC).

The event will feature a robust program with tracks focused on pharmaceutical analysis, predictive modeling, column technology, and more. Below, we highlight several keynote sessions that attendees can add to their list.

Super-Resolution Imaging of Mass Transfer Dynamics

In this presentation, Lydia Kisley from Case Western Reserve University, provides an overview of applying 3D super-resolution fluorescence imaging to investigate analyte behavior in various commercial chromatography materials, revealing that chemical functionalization can block over 50% of the material's porous interior. The current optimization of high performance liquid chromatography (HPLC) relies heavily on trial-and-error methods, encapsulated in the Van Deemter equation's constants A, B, and C, which obscure the molecular dynamics crucial to the separation process.

This inaccessibility, undetected by traditional characterization methods, suggests a significant inefficiency. The study found that selective solvent treatment could remove some polymer and ligand functionalization, restoring access to the porous network without altering mass transfer kinetics. These findings suggest that commercial stationary phases are over-functionalized, and molecular-scale observations can guide the design of more efficient HPLC materials.

Reinforcement Learning for Method Development in Liquid Chromatography

Diedre Cabooter, of the University of Leuven in Belgium, provides an overview of her team’s research on exploring the possibilities of reinforcement learning to automate the optimization step in method development. Cabooter’s keynote will demonstrate the application of reinforcement learning to optimize scouting runs for retention modeling in liquid chromatography, tailored to specific sample compositions. A double deep Q-learning algorithm is employed for isocratic conditions, while a deep deterministic policy gradient algorithm is used for gradient conditions. Further, a proximal policy optimization (PPO) algorithm is trained to directly optimize separations using the outcome of a single generic separation as input. The PPO agents can select both linear and multi-step gradients in a single experiment, leading to improved separations. These preliminary results highlight the potential of reinforcement learning in chromatography, suggesting a promising future for the automated optimization of separations.

Miniaturizing LC Columns and Instruments

In this keynote, Jim Grinias of Rowan University, will discuss the development of methods using a compact capillary LC instrument with integrated components designed to minimize extra-column volumes, thus improving compatibility with capillary columns. Grinias will highlight applications for both small and large molecule separations, particularly therapeutic and illicit drugs. Additionally, the design and use of a new capillary-scale column format that can be integrated into LC instrumentation while maintaining high separation pressures are explored.

Comprehensive Chemical Analysis of Treated Oilfield Wastewater

In this presentation, Kevin Schug, of the University of Texas, Arlington, will discuss the chemical analysis of treated oilfield wastewater featuring targeted and untargeted liquid chromatography, and mass spectrometric analysis. When oil or gas is extracted, it often produces a larger volume of water, known as produced water (PW), which is a highly complex mixture requiring extensive treatment for reuse. Disposing of PW by reinjection into the ground is wasteful and can induce seismic activity. To address this, various treatment options such as distillation, ozonation, and membrane filtration are used to clean PW for different reuse applications. Evaluations of these treatment methods involve characterizing the biogeochemical content of the water post-treatment, including volatile and semi-volatile organic compounds, total carbon content, microbial populations, and perfluoroalkyl substances (PFAS). Some treatments have rendered PW cleaner than standard tap water, while others have concentrated PFAS. In this talk, Schug will provide an overview of the use of liquid chromatography-mass spectrometry (LC-MS) methodologies and the resulting data.

High-Throughput Capillary LC

In this presentation, Robert T. Kennedy of the University of Michigan presents a droplet system that replaces the autosampler with a two-position, 20 nL internal loop valve and a three-axis positioner to sample droplets from a well plate. The cycle time of a liquid chromatography (LC) system is typically constrained by lengthy autosampler injection sequences, limiting throughput despite the potential for fast LC separations. Traditional systems use large bore columns with high flow rates, producing significant mobile phase waste and complicating mass spectrometry interfacing. By alternating sample and immiscible fluid into a capillary interfaced through the injection valve, the system allows high-throughput sequential injections. Using 300 µm inner diameter capillary LC columns, the system reduces mobile phase and sample consumption. It achieved 96 separations of 20 nL droplet samples with three components in 7.8 minutes, maintaining a 4-second cycle time. This system, coupled with a mass spectrometer via an electrospray source, enhances high-throughput chemical reaction screening.