Unlocking Comprehensive Two-Dimensional Gas Chromatography: Simplifying Method Development for Users

🎤 Katelynn Perrault Uptmor

Join us on the Concentrating on Chromatography podcast as David Oliva interviews Katelynn Perrault Uptmor, PhD about her recent article, "Detangling the Complex Web of GC x GC Method Development to Support New Users." In this insightful conversation, Katelynn explains the basics of comprehensive two-dimensional gas chromatography (GC x GC), its advantages over traditional one-dimensional GC, and shares practical tips for overcoming common challenges faced by new users.

Discover how GC x GC vastly increases separation capacity by combining two columns with different stationary phases, allowing for more efficient analysis of complex samples. Katelynn discusses the key steps in method development, including establishing a good one-dimensional separation, using chromatogram modelers to predict peak behavior, and fine-tuning modulator parameters for optimal peak shape and resolution.

Learn about choosing the right column combinations for your specific application, understanding the importance of orthogonality between columns, and leveraging resources like literature and expert advice to optimize your setup. Explore real-world applications of GC x GC in fields like forensics, food analysis, environmental science, and petroleum analysis, where this technique is revolutionizing routine analysis.

Whether you're a seasoned chromatographer or just starting out, this interview offers valuable insights into the power and accessibility of GC x GC. Katelynn also shares her recent recognition as an Emerging Leader in chromatography and highlights the exciting work of her undergraduate research group.  Katelynn is currently Assistant Professor of Chemistry at William and Mary.

Video Transcription

Interviewer:

Hi Katelynn, thanks for joining me. Could you briefly explain the concept of comprehensive two-dimensional gas chromatography, otherwise known as GC×GC, and its advantages over traditional one-dimensional GC?

Katelynn Perrault Uptmor:

Absolutely. My group specializes in comprehensive two-dimensional gas chromatography, which we abbreviate as GC×GC. A GC×GC system is very similar to what most people know from one-dimensional gas chromatography. In 1D GC, you inject your sample into a hot inlet where it is vaporized, then it moves through a column coated with a stationary phase. Depending on each analyte’s affinity for that stationary phase, the components separate and ideally reach the detector one by one, so the detector can identify them individually.

In GC×GC, we’re really trying to increase the separation capacity. When you have very complex samples with hundreds or thousands of compounds, getting everything to elute single-file is extremely challenging. With GC×GC, we collect what’s coming off the primary column—essentially the same type of column used in 1D GC—in very short time slices, usually a few seconds. Each slice is then rapidly injected onto a secondary column that is shorter and coated with a different stationary phase. That small “plug” of analytes is then separated again based on a different retention mechanism. By doing this, we greatly increase our ability to separate analytes, because they’re separated first on the primary column and then again in a fast second-dimension separation.

Interviewer:

One thing I enjoyed about your LCGC article was that it focused on new users. It was a great introduction for someone like me who isn’t very familiar with GC×GC. Could you talk about some common challenges new users face when developing GC×GC methods, and how you addressed those challenges in your article?

Katelynn:

When GC×GC was first being developed, many researchers were studying every aspect of this new technology. A well-known figure in the GC×GC community—the “spaghetti diagram”—shows how every system component interacts with all the others. If you change one parameter, several others increase or decrease. It’s useful, but it also intimidated people for a long time; it made GC×GC look overwhelmingly complex. You could do the same thing for a 1D GC system and make it look equally complicated.

Today, the situation is very different. The hardware used in GC×GC systems has been studied extensively, and we know optimal operating ranges and ratios for many parameters. There are now clear recommendations—maximum and minimum limits, suggested ratios—that simply didn’t exist when GC×GC was new.

In our article, we tried to show that there is a logical, stepwise way to approach method development. If you have a new sample you’ve never run on a GC×GC system before, there are sensible steps to follow that minimize subjective decision-making. We want the data to guide users, rather than forcing them to guess. The article is meant to present an approachable workflow and reassure people that much of this has already been figured out—they can be guided through the process in a straightforward way.

Interviewer:

Speaking of workflow, could you talk about the key steps in that decision-making process and why they matter for a new user who’s considering GC×GC for the first time?

Katelynn:

One thing I always emphasize to new GC×GC users is that if you start with a good one-dimensional separation, you’re already far ahead. GC×GC only adds two main hardware components to a standard 1D system: the modulator, which performs the trapping and re-injection after the primary column, and the secondary column. Everything else is largely the same.

Our workflow focuses first on establishing a strong first-dimension separation. Once that’s in place, you’re really just testing and tuning the additional GC×GC components. Often, especially in non-targeted analyses, you have a general idea of the types of compounds you expect—for example, a fragrance sample versus an explosive sample. We recommend compiling a list of representative compounds spanning your expected range (low, mid, high boiling or polarity, etc.) and putting them into a chromatogram modeler.

Rtx has a very useful modeling tool where you can enter compounds and adjust parameters to build theoretical chromatograms. It’s powerful and underused. Step one is to test your expected analytes in the modeler, see what the separation looks like, and identify where you expect co-elution or overlap. This doesn’t require any actual sample runs. Then you try to replicate that model on your instrument and see how similar it looks. That gives you a good idea of where to focus your separation power.

People sometimes feel they must endlessly optimize to squeeze every last bit of performance out of a method. But with the enormous peak capacity that GC×GC already provides, you can concentrate your efforts on the most problematic regions of the chromatogram rather than optimizing everything.

So, step one: get your 1D GC separation working, using a modeler if helpful, and run a test sample. Step two: start optimizing your GC×GC parameters. The first thing we focus on is what I call “defining the box,” which means defining the rectangular separation space. In 1D terms, that’s your start time and initial oven hold, your ramp, and your end time and final hold. In GC×GC, the modulation period defines the upper and lower boundaries by controlling how long you collect before re-injecting into the second dimension. Together, these define the separation space where all your analytes must fit.

Once that box is defined and your analytes fit within it, you move on to fine-tuning—improving peak shape and resolving especially tricky pairs of compounds. At this stage, we adjust individual modulator settings such as hot pulse time and secondary oven offset (for thermal modulators), which help maximize the performance of the second dimension. In the article, we show data from running samples with default settings and then after applying just three targeted optimization steps, highlighting the dramatic improvements in peak shape and resolution. The message is: focus on the parameters that matter most for method development.

Interviewer:

I can see how that would be very useful for anyone just starting out. How do you go about choosing an appropriate column combination, and what factors influence that decision?

Katelynn:

For most non-targeted applications, I don’t think there is a single “correct” column combination. In GC×GC you have two columns: the primary column, which is typically the same one you’d use for your standard 1D GC application, and the secondary column. If you already have a favorite column for fragrance analysis, for example, that’s likely what you should use in the first dimension. There’s no need to reinvent the wheel.

For the second dimension, you generally want something that is orthogonal to the first. If your primary column is very non-polar, you probably want a polar column in the second dimension, and vice versa. But polarity isn’t just “polar” or “non-polar”; it’s a spectrum. Some polar columns are less polar than others. Once you’ve optimized hardware parameters like flow rates and oven ramps and still have analytes that won’t separate, that’s when you need to think more critically about stationary phase selection.

There is a lot of literature to guide these choices, and again, modeling tools can be very helpful. It’s also valuable to talk with column manufacturers—their technical experts can often recommend combinations for difficult separations. A standard GC×GC setup often uses what we call a “5/17 combination”: a non-polar first dimension with a polar second dimension. You can flip that if your sample demands it. Ultimately, the choice depends on your sample and how you want the analytes to spread across the two-dimensional separation space.

Interviewer:

You mentioned fragrances earlier. Could you talk about some other real-world applications where GC×GC is especially powerful?

Katelynn:

GC×GC is gaining momentum in many fields. My research group primarily focuses on forensic and food applications. We analyze fingerprint residue, organic gunshot residue, tissue decomposition over time, and fermented products like beer and kombucha.

More broadly, GC×GC has become a staple in petroleum analysis. There’s been significant work on aviation and jet fuel analysis using GC×GC. On the environmental side, it’s used for source apportionment of pollution, wildfire smoke analysis, and related topics. There are many exciting examples where GC×GC is directly helping real-world decision-making, and I think we’ll see even more as the technique becomes more widely known and adopted. Our article helps by providing solid, practical recommendations so people can implement GC×GC appropriately in routine analysis.

Interviewer:

I saw you last week at Pittcon, and I believe you were there in part because you received an award. Would you like to talk about that?

Katelynn:

Yes, this year at Pittcon I received the LCGC Emerging Leader Award, which was a great honor and made it a very special conference for me. I received it alongside Chris Pohl, who was given LCGC International’s Lifetime Achievement Award in Chromatography. Hearing his stories and seeing his body of work—hundreds of patents—was really inspiring.

I was able to give an award lecture that highlighted the work of my students over the last year. I recently moved to William & Mary, where I now lead a predominantly undergraduate research group (we also have a small master’s program). Many of my students are encountering separation science for the first time and may never have heard of it before joining the lab. Being able to showcase their hard work and accomplishments in that talk was very meaningful.

Interviewer:

When I contacted your colleague Isaiah, he said he was happy to be interviewed but that you were really the person to talk to about separation science. You’re clearly involved in many exciting projects. Is there any ongoing work you’re particularly excited about that you can share?

Katelynn:

I’m always excited about our forensic research because relatively few people are doing chromatographic method development in that space. Forensic science is interesting because practitioners want methods that are tried-and-true, gold-standard, and conventional, while research tends to focus on the new and novel. We often approach problems from a fundamental separation science perspective, but our goal is to help practitioners adopt these techniques in routine casework. That means we have to stay in close contact with them, understand their main challenges, and shape our research so it truly supports their needs.

One area I’m particularly excited about is organic gunshot residue analysis. There’s a challenge in forensic science right now because many modern ammunition types are heavy-metal-free or lead-free and don’t produce the characteristic SEM-EDS signature (lead, barium, antimony) under the microscope. When practitioners search for gunshot residue, they traditionally look for these particles, but with new ammunition, those markers may not be present. To determine whether a firearm might have been discharged, you need additional chemical evidence.

We’re using GC×GC to analyze the organic components of gunshot residue and to separate analytes that are characteristic of a firearm discharge event. We’re combining this with data from scanning electron microscopy, which means venturing into the microscopy lab as well. It’s a new world for us, but it’s a lot of fun to pair high-power microscopes with advanced chromatographic techniques.

This text has been automatically transcribed from a video presentation using AI technology. It may contain inaccuracies and is not guaranteed to be 100% correct.

Concentrating on Chromatography Podcast

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