The continuous evolution of method development in GC is essential for achieving precision and efficiency in analytical processes. We explored this topic during a discussion with Chris English, Laboratory Manager at Restek Corporation, as he unveiled the benefits, challenges, and future possibilities that characterize the landscape of optimized GC method development.
Benefits of Optimized Method Development
Optimized method development, according to English, involves deploying advanced tools that utilize thermodynamic retention indices. He explains that these tools take into account critical factors, including the choice of stationary phase, column length, analyte retention, and resolution.
The application of method development tools yields numerous advantages. One significant outcome is the creation of an excellent starting point for the overall method development process. "Using those indices, it can come up with the best resolution for a particular stationary phase and give you those conditions," explains English.
Of course, as is the case with most tools, these ones come with limitations. "The power of using these platforms is only as good as the data that was entered in originally," warns English. "It's not operating off of software that's doing actual mathematics that can predict where compounds that have never been run before are going to elute. We simply run them under many different conditions, and then the software is able to come up with optimized conditions based on what was previously entered."
Another primary constraint, as he notes, is the finite number of compounds (some platforms, for example, contain 8,000 compounds). "If the program doesn't have the compound in there, you have no idea where it's going to come out. When we say 8,000, it sounds like a lot, but it's really not, especially for the chemical industry."
Limitations aside, method development tools can help identify a broad range of parameters, including the optimal stationary phase, column film thickness, internal diameter, temperature, and flow. "There are very powerful tools out there to help make method development a lot easier without ever stepping foot in the lab," adds English.
Making Method Development Easier and Accessible
The conversation reveals a paradigm shift in method development approaches, moving away from traditional methodologies. According to English, this shift goes beyond the conventional "like dissolves like" approach. "If we have a lot of aromatic compounds, then we're looking at phenol-containing stationary phases. That's how we would go about looking for a stationary phase. Or if we're looking at Freons (which are fluorinated compounds), we would look at something like a 200-type phase (a trifluoropropyl phase, which is fluorinated). That's a good general way to select a stationary phase."
English goes on to explain that newer tools take things to a new level. "We're able to show where these analytes elute relative to each other and with very specific separations and conditions, and that's a whole step further than just picking an appropriate stationary phase based on solubilities." This departure signifies a substantial leap forward, providing scientists with a nuanced perspective on how various compounds interact with the chosen GC parameters.
The Future of GC Method Development
Envisioning the future of GC method development, English puts forth a forward-looking strategy. "A future idea I would love to see is a crowd-sourced type program, where customers would be able to enter their compounds," reveals English. "Those compounds could be flagged as having been entered by a third party, and whoever is doing the modeling can decide if they want to use that data or not in their method development." This collaborative effort, as English emphasizes, could significantly amplify the applicability of method development tools, fostering a more comprehensive analysis of a broader spectrum of compounds.
With technology advancing rapidly, the integration of AI and machine learning into method development tools emerges as a potential next frontier, although English notes that such developments are still in the conceptual stage. He explains that these have been discussed and could initially be applied to compounds such as ketones, aldehydes, and alcohols. "I don't think it's a huge leap to be able to make a prediction on a branched alcohol that's similar to another alcohol and its solution. I think it's very possible to get there." But he warns of limitations. "Trying to predict what an analyte is going to do on a stationary phase when we've never developed the stationary phase, and we've never run the compound—that's a much bigger leap."
Conclusion
The evolution of GC method development tools signals a significant leap forward in precision and accessibility. These tools offer an excellent starting point by considering crucial factors, though limitations such as reliance on past data and a finite compound library should be considered. Looking ahead, the integration of crowd-sourcing and machine learning holds the promise of a more predictive and adaptive future, overcoming current limitations and propelling GC method development into innovative frontiers.