The best option for the gas chromatographic (GC) analysis of highly volatile analytes without cryogenic cooling is to use solid adsorbents. Open tubular GC columns with a deposited layer of solid adsorbent on the inside are known as porous layer open tubular (PLOT) columns. Adsorbents like molecular sieves, alumina, porous polymers, and even carbon materials are essential when analyzing light gases. Most gas samples contain water; although we are not interested in the amount of moisture that is present in the sample, water is injected onto the column unless a drying step of the sample is utilized. This article reviews the effects of water on retention and selectivity in gas–solid chromatography of various adsorbent PLOT columns.

Activated alumina has been studied as a chromatographic material for over 70 years. The high surface area of this porous material showed promise in separating C1–C5 hydrocarbons. Because of the extreme activity, tailing, and inconsistent responses of alumina, the material was at first considered almost unusable. In the mid-1950s, alumina oxides were deactivated with water vapor. This new approach drastically improved peak shape and provided consistent compound responses. However, water-deactivated alumina columns could be used only under isothermal conditions. The next significant improvement for porous alumina columns occurred in the 1970s when more stable deactivations with inorganic salts, such as potassium chloride (KCl), sodium sulfate (Na

), and various proprietary salts, were implemented (1–3).

Even though the industry moved away from alumina-packed columns in favor of alumina-based porous layer open tubular (PLOT) columns, alumina columns remained a reliable choice for analyzing C1–C5 hydrocarbons. One thing has not changed since the 1950s—we still have problems with chromatography in the presence of moisture. To better understand the problematic effects of water on alumina columns, we studied the chromatographic behavior of C1–C5 hydrocarbons. Various selectivities of alumina deactivated PLOT columns were tested under the same isothermal chromatographic conditions. The analysis was performed again after injecting 0.1 μL of water onto the column, and the procedure was repeated five times.

Regardless of the column deactivation, the introduction of water onto the alumina PLOT column changes the retention time by accelerating the elution of the analytes. Water will act as a deactivating agent, meaning that the adsorption sites where water molecules are adsorbed will not be available for hydrocarbon retention. This process results in a decrease in retention time. Figures 1 and 2 show examples of the analysis of those two analytes, alumina MAPD (Figure 1) and alumina potassium chloride (Figure 2), as well as the analysis of the columns after exposing them to 0.5 μL of water in increments of 0.1 μL. Not only do we notice shifting retention times, but we also see changes in resolution, indicating that the polarity of the column has changed.

FIGURE 1: Comparison of the analysis of C1–C5 hydrocarbons on alumina MAPD deactivated column, 30 m x 0.32 mm x 5 μm, (a) chromatogram is the original analysis and (b) chromatogram after treating the column with 0.5 μL of water. Analysis conditions: carrier gas: helium at 5 mL/min, oven: 110 °C isothermal (

FIGURE 2: Comparison of analysis of C1–C5 hydrocarbons on a nonpolar alumina potassium chloride deactivated column, 30 m x 0.32 mm x 5 μm, (a) chromatogram is the original analysis and (b) chromatogram is after treating the column with 0.5 μL of water. Analysis conditions: carrier gas: helium at 5 mL/min, oven: 110 °C isothermal. (

-axis is time in minutes, and y-axis is the detector response).

Regardless of whether the column was deactivated with proprietary deactivation, MAPD, or potassium chloride, exposure to water decreases retention time and impacts the resolution of the compounds. The introduction of any moisture to the column would further deactivate aluminum oxide, making the column less polar, which changes the column selectivity. The plotted deviation of retention indices of the analytes, which is based on the retention of adjacent 

-hydrocarbons, confirms our observation. The analytes most affected by the moisture treatment over time are polar analytes—acetylene, propadiene, and methyl acetylene—that show more significant shifts in retention than the butanes and butenes (Figure 3). Similarly, the impact of moisture is more dramatic on a polar column, such as a sodium sulfate alumina column. Figure 4 illustrates changes in relative retention of acetylene on different types of deactivated alumina columns. The polarity of a sodium sulfate-deactivated alumina column declines considerably faster than a potassium chloride-deactivated alumina column. The potassium chloride-deactivated alumina has the lowest polarity, and it is the least sensitive to water.

FIGURE 3: Relative behavior of analytes with the addition of water by monitoring deviation of retention indices of individual analyte on methyl acetylene and propadiene (MAPD) deactivated column.

FIGURE 4: Behavior of acetylene. Comparison of changes of normalized retention after water treatment for all three deactivated columns. Water has the largest effect on a polar sodium sulfate deactivated column, followed by the methyl acetylene and propadiene (MAPD) deactivated column. The least affected is retention of acetylene on a potassium chloride deactivated column.

Water will not damage the alumina column. The column can always be regenerated by conditioning it at the maximum temperature of the column.

Molecular sieves 5A (MSieve 5A) are zeolites, which are part of the family of aluminosilicate minerals. They are strong adsorbents with an unique, tunnel-like crystalline structure and a well-defined pore size. These strong adsorption properties make them ideal for analyzing permanent gases. The analytes separate on molecular sieves based on two mechanisms: First, how well the molecules fit into the material’s pores, a separation based on the size of the molecules; and second, the physical interactions between the molecules and the MSieve 5A crystal, which is a separation that takes place based on the polarity. For example, nitrogen and oxygen are both small enough to fit in the pores of the 5A mineral, but oxygen, a smaller molecule than nitrogen, will navigate through the “tunnels” faster because it has less interactions with the pore surface and elutes from the column before nitrogen. In the case of carbon monoxide, dipole interactions play a more important role, and the MSieve 5A strongly retains these molecules. A bigger molecule, like sulfur hexafluoride (SF6), will not be able to enter the pores of the zeolite. It will only be retained by the outside, free-accessible surface area. The same phenomena is observed with isoalkanes. Isobutane, for example, would be eluted around nitrogen at 40 °C, while 

-butane (which can fit in the pores because of the linear structure), needs a very high temperature to be eluted (4,5).

Adsorption on molecular sieves is reversible, and the adsorption–desorption process is easily regulated with temperature. Molecular sieves are generally used as drying agents, and we know that we cannot analyze water using the MSieve 5A columns because of the high temperature required to desorb water from the molecular sieve pores. Our study on the effects of water on molecular sieves showed that the MSieve 5A has a relatively high tolerance to moisture in comparison to salt-deactivated alumina columns. A MSieve 5A PLOT column, with the dimensions of 30 m x 0.53 mm x 50 μm, was tested using a mixture of permanent gases at 40 °C. Afterward, water was injected onto the column at 100 °C isothermal. Column behavior was monitored by repeating the analysis of permanent gases at 40 °C. Figure 5 demonstrates a chromatogram of the initial analysis of permanent gases, black overlay, and blue overlay is a chromatogram after exposing the molecular sieves to water. The column had to be treated with 1 μL of water to see a notable shift in the retention time of the polar compound, which in this case is carbon monoxide.

FIGURE 5: Chromatogram overlay of the analysis of permanent gases at 40 °C isothermal. The black overlay is the starting analysis, and the blue overlay is the analysis after exposing the MSieve 5A (30 m x 0.53 m x 50 μm) column to 1 μL of water. Peak identification is in order of elution: argon, oxygen, nitrogen, methane, and carbon monoxide. The retention time of carbon monoxide shifts after the column is treated with water. Analysis conditions are as follows: carrier gas, helium at 5 mL/min; and oven, 40 °C isothermal. (

Although 1 μL of water may seem like a small amount, the quantity is more significant when we look at it from a different angle. For example, if the gas sample has 50% relative humidity, with every 1 mL injection of gas, we are injecting ~0.01 μL of water onto the column. To see a slight shift of the carbon monoxide peak, we would need to make hundreds of injections.

Continued exposure of water to a MSieves 5A column will pack the adsorption sites with water. Figure 6 illustrates the changes in chromatography when the molecular sieves are treated with 50 μL of water at 100 °C. Retention times for all the analytes decrease, and some will be coeluted. When the peaks start to tail (Figure 6, blue overlay), that is an indicator that the column has reached its loading capacity partially because of the adsorbent sites being occupied with water.

FIGURE 6: The overlay of the initial chromatogram of the analysis of permanent gases is in black, and the chromatogram after 50 μL of water was injected onto the column is in blue. Continued exposure of the MSieves 5A column to water will over time load the adsorption sites with water. Analysis conditions: carrier gas, helium at 5 mL/min; oven, 40 °C isothermal. (

The temperature necessary to desorb the water from the 5A molecular sieves is lower than the maximum temperature of the PLOT column. Therefore, water can be removed from the column. By conditioning the column at the maximum temperature of 300 °C, moisture is removed and initial performance is restored (Figure 7).

FIGURE 7: Graph of desorption process relative to the retention time (

) of the carbon monoxide (CO). The blue line is the original retention time, and the black line is the retention factor of CO over the conditioning time. Conditioning temperature is 300 °C.

Porous polymer PLOT columns are based on styrene-divinylbenzene (DVB-styrene) cross-linked polymers. They come in a range of polarities. For example, Q PLOT columns are made with 100% DVB-styrene polymers and are nonpolar. On the other hand, DVB-ethylene glycol- dimethyl acrylate PLOT columns, also referred to as U PLOTs, are highly polar (2,4).

Porous polymer PLOT columns are extremely versatile. They can separate a wide polarity range of molecules from gases to semi-volatiles. The main advantage of porous polymers is their hydrophobicity. Treating a column with large, continuous water injections does not have any effect on the adsorbent. Water elutes from the column very fast as a chromatographic peak and does not change the selectivity of the column because of adsorption (Figure 8). This means there is no need to condition heat porous polymer columns to drive the water off, which reduces the downtime between injections.

FIGURE 8: (a) U BOND: Overlay of analysis of permanent and hydrocarbon gases on a 30 m x 0.53 mm x 20 μm U BOND column before (black overlay) and after treating the column with 20 μL of water (blue overlay). Water elutes from the column as a chromatographic peak and does not change the column polarity. (b) Q BOND: Chromatogram of analysis of hydrocarbon gases with water using 30 m x 0.53 mm x 20 μm Q BOND. Depending on the polarity of the polymer water will be more or less adsorbed and elution time will change accordingly. Analysis conditions: Carrier gas, helium at 5mL/min; and oven, 40 °C (3 min) then 10 °C/min to 190 °C. (

Although we deliberately exposed our columns to aliquots of water to illustrate the effect of water on adsorbent PLOT columns, systems with alumina and MSieves 5A PLOTs should be protected to keep water from causing slow but continuous deactivation and changes in column selectivity. To conclude this column, here are a few tips to keep in mind to prevent water from further affecting your chromatography or to regenerate your column if it has already been exposed to moisture:

1. If the moisture has already accumulated in the column, sodium sulfate-, potassium chloride-, or MAPD- deactivated alumina PLOT columns as well as MSieves 5A columns can be regenerated by conditioning the column at their maximum temperature.

2. Increasing the final temperature of the analysis to the maximum temperature of the column for 5–10 min will desorb the water during each analysis cycle.

3. However, it is always better to avoid water getting into the alumina column. By using a thick film, polar precolumn with a column-switching device, water and other impurities in the sample are backflushed and never enter the analytical column.

4. Gas samples can be dried using Nafion dryers, salt filters, cryogenic trapping, and preconcentrators. Water and other impurities in the sample are backflushed and never enter the column.

Capillary gas adsorption chromatography (Chromatographic methods)

 is an application chemist at Restek Corporation in Bellefonte, Pennsylvania.

 is the International Specialist Gas Chromatography at Restek Corporation in the Netherlands.

 is a director of Research and Development at Restek. He also serves on the Editorial Advisory Board for 

 and is the Editor for “Column Watch.” Over the past 20 years, he has worked directly in the chromatography industry, focusing his efforts on the design, development, and application of chromatographic stationary phases to advance gas chromatography, liquid chromatography, and related hyphenated techniques. His main objectives have been to create and promote novel separation technologies and to conduct research on molecular interactions that contribute to retention and selectivity in an array of chromatographic processes. His research results have been presented in symposia worldwide, and have resulted in numerous peer-reviewed journal and trade magazine articles. Direct correspondence to: 

Articles

Solid adsorbent gas chromatography (GC) columns, such as porous layer open tubular (PLOT) columns, are the best option for GC analysis of C1–C5 hydrocarbons, but water can affect retention and selectivity. We review the effects of water for different types of PLOT columns , and explain how to prevent or remediate the problem.

Effects of Water on Adsorbents in Porous Layer Open Tubular (PLOT) Column Gas Chromatography (GC)

This article addresses the latest developments in microextraction devices for gas chromatography (GC) analysis of complex samples with emphasis on biocompatible extraction phases. The role of microextraction in preventing “garbage in–garbage out” effects in one-dimensional (1D) and two-dimensional (2D) GC separation is described through various applications for food, environmental, and biological samples.

Since the first developments in the 1940s, gas chromatography (GC) has transformed separation science, providing robust and efficient separation platforms extensively used worldwide. Improvements, particularly in column geometries and stationary phase chemistry, have progressively facilitated the analysis of a variety of matrices. Furthermore, the development of multidimensional GC techniques has provided an ideal solution for the analysis of complex mixtures by expanding the chromatographic space available for separation and enabling peak focusing, especially via thermal modulation that permits analyte enrichment (1). Extraction and sample introduction methodologies for GC, however, did not follow the same evolution path, and for decades, these important aspects of the analytical workflow have been overlooked and underdeveloped. Headspace (HS) sampling, for example, has been the technique of choice for introducing volatiles into the gas chromatograph, preventing nonvolatile contaminants from entering the GC inlet. Often, however, the technique does not provide enough analyte preconcentration with low recoveries for semivolatiles, and requires a more sophisticated instrumental setup. Solvent injection in GC after liquid–liquid extraction, sorbent-based extraction techniques such as solid-phase extraction (SPE), or even sample dilute-and-shoot overcomes some of the shortcomings of HS sampling and provides a better option for analyzing semivolatiles. However, solvent injection is more prone to instrument contamination, especially when complex samples are analyzed. The invention of solid-phase microextraction (SPME) in the early 1990s provided a convenient solution for extraction and sample introduction into GC, considering its syringe-like geometry (more commonly known as fiber SPME) (2). SPME offered the ability for simultaneous extraction and preconcentration of the analytes of interest in a single and solvent-free step. The combination of HS sampling and SPME subsequently provided the perfect fit with one-dimensional (1D) and two-dimensional (2D) GC and expanded the analytical tools available for untargeted screening or quantitative target analysis. As a result, HS-SPME has been widely employed in the past three decades. This chapter of “microextraction history” is already well known and acknowledged by the scientific community. More recent developments in the configuration and chemistry of microextraction devices have significantly improved the role of this technique in facilitating GC separation, as described below.

Thin-Film SPME: An Alternative SPME Geometry for GC

Thin-film SPME (TF-SPME) is a microextraction device that allows for depositing more extraction phases, thus having enhanced capacity compared to fiber SPME (3). The surface-area-to-volume ratio also allows for fast extraction kinetics, making TF-SPME devices ideal for ultratrace analysis. When hyphenated to GC, these devices must be desorbed in thermal desorption units (TDU) that are able to accommodate their size more conveniently compared to the conventional split–splitless injectors used to desorb SPME fibers. An advantage of using TDU units for the desorption of TF-SPME devices is the opportunity for cryofocusing the desorbed molecules when a programmable temperature vaporized injector is hyphenated. This dual-stage desorption and injection enables (Figure 1a) trapping and focusing of all the extracted analytes on the column head, greatly improving the efficiency of the separation and the sensitivity of the method. TF-SPME devices for GC analysis are available in various formats and chemistries (polydimethylsiloxane (PDMS), divinylbenzene/polydimethylsiloxane (DVB/PDMS), carboxen/polydimethylsiloxane (CAR/PDMS), and hydrophilic-lipophilic balanced/polydimethylsiloxane (HLB/PDMS)) for multiple headspace applications and direct immersion extraction of complex samples. Compared to conventional extraction techniques and fiber SPME, TF-SPME has shown enhanced extraction kinetics and has enabled the achievement of lower limits of quantitation for analysis of pollutants in environmental samples (4,5). The potential of this technique to facilitate GC separation of complex mixtures has been shown with its application to the characterization of produced water samples, a byproduct of fracking methods used for extraction of oil and natural gas (6). Produced water is an extremely complex matrix that includes dissolved solids, high salinity, and emulsion with petroleum products. The extraction of soluble organics via TF-SPME was performed via both headspace and direct immersion mode, guaranteeing comprehensive untargeted characterization (6). Moreover, it has been demonstrated that this approach can avoid the need for any sample pretreatment such as filtration (always needed for other extraction approaches to prevent contamination of the GC injector and column saturation by nonvolatile constituents), which enables recovery of a broader range of soluble organics than what can be achieved with other methods proposed in the literature (Figure 1b). This method, which is able to identify ~200 soluble organics present in produced water by 1D-GC, can be improved by using 2D-GC coupled to TF-SPME extraction to provide a better separation platform for the characterization of these complex samples. TF-SPME also has been hyphenated to portable GC–mass spectrometry (GC–MS) systems, providing a convenient solution for on-site sampling and guaranteeing enough preconcentration to allow detection of environmental pollutants at low parts per billion levels (7).

FIGURE 1: (a) Schematics of the two-stage thermal desorption unit for thin-film solid-phase microextraction (TF-SPME) (photo courtesy of Gerstel Inc.). CIS: cooled injection system; TDU: thermal desorption unit; GC: gas chromatography. Reproduced with permission from the literature (3). (b) Representative chromatograms obtained by TF-SPME-GC–MS analysis of untreated and filtered produced water samples.

Biocompatible Extraction Phases for Direct Immersion Extraction of Complex Samples 

The development of biocompatible extraction phases for fiber SPME represented a turning point in the analysis of complex samples by SPME-GC. Although HS-SPME has been broadly applied over the years, limitations of the technique arose when analysis of organic molecules with a low Henry’s law constant (which do not partition efficiently into the sample headspace), was needed at parts per billion (ppb) or parts per trillion (ppt) levels. Although conventional extraction phases could be used for both HS or direct immersion SPME, the issues in the latter case consisted of the short lifetime of the extraction device because of fouling and introduction of contaminants or production of artifacts into the GC system. To overcome these limitations, a new extraction phase, PDMS/DVB/PDMS, was developed (8) and tested for a variety of food, environmental, and biological samples (9). These biocompatible extraction phases were found to be an excellent solution to prevent the production of artifacts (such as those generated in the GC injector at high temperatures) and thus, demonstrated their usefulness both for 1D- and 2D-GC separations (Figure 2) (10,11). For the analysis of fruits, Maillard reactions can take place in the GC injector when residues of carbohydrates and amines are present in extracts or on the surface of a nonbiocompatible SPME extraction phase. This phenomenon has been demonstrated for in vivo analysis of fruits, showing that even when 2D-GC is performed, the resolving power of the technique is still not enough to prevent these artifacts from masking the elution of minor metabolites peaks (12). The solution that biocompatible SPME phases provided was to prevent the attachment of carbohydrates on the extraction phase surface, thus eliminating the production of artifacts during their desorption in the GC injector and guaranteeing a “cleaner” chromatographic space where even trace metabolites could be detected. Another application, direct immersion extraction of a fatty sample such as an avocado, showed that biocompatible SPME devices are indispensable to eliminating the accumulation of nonvolatile fatty residues in the GC injector liner, which they are able to do because their inert surface prevents fouling of matrix constituents (Figure 3) (13), unlike with conventional SPME fibers.

FIGURE 2: Elution windows of GCxGC extracted ion chromatograms of furfural, a Maillard reaction byproduct, from in vivo metabolic profiles obtained by (a) DVB/CAR/PDMS and (b) PDMS-overcoated DVB/CAR/PDMS, a biocompatible extraction phase.

FIGURE 3: (a) Nonbiocompatible PDMS/DVB coating after 50 extractions in avocado samples (200x magnification); (b) magnification (1000x) of the PDMS/DVB coating surface; (c) biocompatible PDMS/DVB/PDMS coating after 100 consecutive extractions in avocado samples; and (d) magnification (2000x) of the PDMS/DVB/PDMS coating surface.

The role of microextraction in facilitating separation of complex samples via GC is obvious and has facilitated the development of important applications. Although technical developments in GC–such as sophisticated multidimensional GC systems and their hyphenation to high-resolution MS—that simplify the analysis of complex mixtures are also on the rise, their availability may not be widespread. For this reason, microextraction provides an affordable solution for preventing “garbage in–garbage out” effects in 1D- and 2D-GC separation. The ability of microextraction approaches to provide analyte preconcentration, interference removal, tuning of extraction coverage, an easy coupling to GC systems, and an opportunity for automation constitute the “most powerful analytical alliance” (14) with gas chromatography for targeted and untargeted analysis of complex samples.

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 is with the Department of Chemistry and Biochemistry, the Dr. Nina McClelland Laboratories for Water Chemistry and Environmental Analysis, and the School of Green Chemistry and Engineering at The University of Toledo in Toledo, Ohio. Direct correspondence to: 

Microextraction is an affordable solution for preventing “garbage in–garbage out” effects in one-dimensional (1D) and two-dimensional (2D) GC separations, by providing analyte preconcentration, interference removal, tuning of extraction coverage, and easy coupling to GC systems.

The Role of Microextraction Techniques in Facilitating Gas Chromatography Separation of Complex Mixtures

I like both chromatography and Marvel movies, and an idea came to me as a result: Why not examine a chromatographic separation process from an Ant-Man perspective? I used the stochastic theory of chromatography to walk through this exercise. I am motivated to connect mathematics to separation science in an intelligible form, and convince every other chromatographer that deep down we all love mathematics. Please take a “random walk” with me through the column where chromatography is mathematics and mathematics is chromatography!

Because all chromatographers can operate a chromatograph, it is not obligatory to know how it really works inside. Nevertheless, I wanted to know what happens inside a column, and how and to what extent the analyte molecules interact with the stationary-phase surface.

There are several mathematical models used in chromatography. One of them is the stochastic theory that helps us understand the separation process at the molecular level. At first, when I was introduced to the stochastic theory, I disliked it because it seemed rather complex. But now, I am the one who wants to convince everyone that mathematics gives a great tool for us to be an “Ant-Man” and see what happens at the single molecule level. The stochastic theory is a sophisticated microscopic model that works as a magnifier; it gives us the number of the adsorption-desorption steps (

), and the average time that a molecule spends bound to the stationary phase (

The stochastic theory was first described by Giddings and Eyring more than 65 years ago (1). They realized that the analyte molecules wander randomly along the column. The stochastic theory uses probabilistic terms to represent the adsorption–desorption process. However, at the time, the computation methods were laborious, and the stochastic method could not be further developed. Afterward came the characteristic function (CF) method that simplified the representation of chromatograms (2,3). Shortly following the discovery of the CF method came the best realization of the chromatographic model—it does not depend on the separation procedure; thus, the CF method can be used for any type of chromatography. Regardless of whether it is reversed-phase liquid chromatography (RPLC), size-exclusion chromatography (SEC), ion-exchange chromatography (IEC), chiral chromatography, or another type of chromatography, we only need to con- sider the variables accordingly. This way, we can get information about the number of interactions between the stationary phase and the analyte molecule (

), and about the strength of this interaction (

). Because these terms are easy to understand and work with, the stochastic theory should be preferred to macroscopic models. For example, band broadening phenomena of small molecules in high performance liquid chromatography (HPLC) columns are usually described by the general plate height model where a number of variables are used simultaneously.

Once we know in detail how a given separation process works, the characteristic function helps in the opposite way as well: The chromatogram can be depicted by inverse Fourier transform of the CF and the moments of the peak can be calculated. Thus, fundamental chromatographic characteristics can be tested. The first absolute moment is the retention time (

), the second central moment is the variance (or the width) of the peak, and the third central moment holds information about the peak symmetry. In the simplest case, each moment can be calculated by the above-mentioned two variables. When a novel model is developed, the characteristic function may become intricate, but the moments can still be calculated, and the chromatogram can be represented.

Usually, people are frightened because of the complex mathematics needed when using the stochastic theory, but nowadays we have powerful software packages, such as Wolfram Mathematica, and one can overcome the obstacles of performing Fourier transforms and inverse Fourier transforms. Starting now, our journey begins, and every chromatographic situation can be described.

In this article, I do not want to go into the fine details. This article provides a brief overview of our derived models. Thus, for details and more references, the reader is referred to our published studies in the literature (4–8). People usually dislike reading articles where a lot of equations are present; therefore, to accommodate reader preference, I have focused only the concepts and ideas of the models without using equations.

Over the years, I have developed, with my coworkers, three models for different chromatographic situations. These concern pore-size distribution in SEC (4), polydispersity in SEC (analyte molecule size distribution) (5), and interconversion during the separation process in chiral chromatography (8). Each model contains distributions that had to be introduced into the characteristic function to see what happens at the Ant-Man level of the molecules.

Similar to Emmentaler (Swiss) cheese, which contains many different holes, stationary phase particles also contain a lot of pores. However, the actual size and shape of these pores are unknown. Inverse SEC (ISEC) can help to analyze the pore structure, but a proper model is needed to evaluate the data. Thus, we integrated pore-size distribution into the stochastic theory of SEC to get a universal and nondestructive method to determine the pore structure of any type of stationary phase by a single fitting procedure.

From destructive methods such as mercury intrusion or low-temperature nitrogen absorption, it is already known that the pore size is governed by a lognormal distribution. The lognormal distribution has two parameters: the expected value (or mean) of the pore size and the variance of the pore size. Because the characteristic function is in the Fourier domain, the mathematics degrade to multiplication, whereas in time domain we would need a convolution of the distribution and the inverse Fourier transformed CF, which would be cumbersome (convolution is a mathematical operation that is poorly known for many people). The moments of the peaks were calculated at several parameter combinations, and we realized that compared to the moments when no pore-size distribution is present, the new moments are multiplied by a factor that is actually the novel partition coefficient (

Regarding pore shape, we distinguished four different geometries: slit shaped, conical, spherical, and when no geometry is assumed (that actually gives the pure partition coefficient). By plotting 

 at several different parameter combinations, the model predicts how the molecules behave in either size-exclusion or when macro- molecules are separated by any other type of chromatography. Figure 1 shows the partition coefficient when lognormal distributions of different pore sizes are assumed. This way, we obtained the relative pore accessibility. In an ideal case, when SEC is used, there are molecules that can enter the pores, and the fraction that is larger than the pore size is excluded. This is illustrated with σ = 0, when there is no distribution present. The other lines, where σ is increased, show that there is no sharp line between the molecules that can visit the pores and the ones that are excluded. There are pores that are big enough for larger molecules and that will cause wider peaks on the chromatogram.

FIGURE 1: Dependence of the partition coefficient on the pore size distribution. ρ is the ratio of the gyration radius of the analyte and the pore radius, σ is the variance of the pore size distributions (PSD). The broader the PSD, the more blurred the exclusion limit is.

Our model determined the pore structure of different commercially available stationary phases, both core–shell particles and monoliths (6,7). The novel stochastic model was able to determine that the pore geometry is rather spherical than conical for each investigated column. This geometric assumption gave better results near the exclusion limit than the previously used Knox model (9). There was an interesting discovery when the first- and second-generation monoliths (Chromolith Performance and the Chromolith High Resolution) were investigated: There is no difference in the real mean radii of the mesopore size, but the pore-size distribution is narrower in the latter case, and as a result, the pore size distribution is more homogeneous.

The application of the model is quite simple. The derived equations should be fitted by a proper software on the SEC calibration curve (the gyration radius against the retention volume) of the polymer standard molecules. We used a free, command-driven, function and data plotting program (Gnu-plot) for this purpose, but any other similar software will do the job.

The polydispersity of the analyte is important mainly in ISEC or during the calibration step of SEC because it is important to know the size of the molecules injected onto the column to assess the pore size. If the standard molecule does not have a narrow molecular weight distribution (because of degradation, polymerization or simply by error in production), the size of the molecules can differ. The best way to illustrate the difference between a monodisperse and a polydisperse sample is to play with coins (Figure 2). Will these coins fit identically in the same hole?

FIGURE 2: The best way to depict what polydispersity means, showing the (a) monodisperse sample, and the (b) polydisperse sample.

The molecule size of the standards used in SEC and ISEC is governed by a normal distribution. The stochastic theory of SEC is adequate enough to integrate this information as well. Thus, the molecular size distribution was incorporated into the characteristic function, and a novel model was derived to describe this phenomenon. Chromatograms and their statistical moments were calculated for several pore geometries to investigate the effect of this distribution on the retention properties. It was concluded that the increase in polydispersity leads to a shift of retention time and causes a similar band-broadening to that of pore size distribution. The application of this model is, however, not feasible, since it would need a stationary phase having uniform pore size. Though the insights can be drawn by investigating the properties of the virtual chromatograms.

Our results verified previous observations and experiences: Both the pore-size distribution and the analyte polydispersity have a strong influence on the retention properties (retention time, peak width, and peak shape) of macromolecules. Because the wide pore size distribution will increase retention and efficiency for macromolecules, all modes of liquid chromatography that result in the efficient separation of macromolecules calls for a broad pore-size distribution.

Although the Batman movies are not part of the “Marvel Universe,” we have to talk about Batman peaks. Everyone knows Batman and his mask, but it is not common to know what we should think about when talking about Batman peaks in chromatography. If Batman partially hides below the baseline, one can see two ears and a headboard in between. The chromatogram of two enantiomers that interconvert into each other during the separation looks like what we see illustrated in Figure 3.

FIGURE 3: Batman-like figure superimposed on batman peaks.

The pure enantiomers are separated, but in the plateau region, we observe molecules that interconverted a few times during the separation. The probabilities for the six different fractions (two fractions for the pure enantiomers that do not change during the separation, two fractions for the molecules with the same initial and eventual form and two fractions for molecules with different initial and eventual form) were already derived by Keller and Giddings in 1960 (10). The novel stochastic theory uses convolution of the proper distribution functions and the characteristic function. I would not say that the equations are open-and-shut, but Wolfram Mathematica can handle them.

Our novel model made it possible to examine the kinetics of the enantiomerization process from a series of chromatograms since the peak shape depends on the rate constant of this dynamic reaction. Applying our model is a true random walk along the column, because the different fractions need to be fitted separately. At first, the model was tested in silico, a number of chromatograms were simulated, and classical chromatographic properties were observed. Then, we investigated a commercially available drug molecule, quetiapine, that features a hindered ring-flip atropisomerism. The rate constant of the enantiomerization process was determined at a number of flow rates and temperatures. The interconversion rate constant can be determined from the slope of the fitted line when plotting the number of interconversions against time. Simultaneously, the rate constants were also determined at a number of temperatures from the optical activity of the two respective enantiomer fractions, and with the DCXplorer software derived by Trapp (11). Determined by the three different methods that were analyzed, a good agreement was found between the results.

This novel stochastic model is thus suitable to estimate the rate constant of interconversion from experimentally recorded data and gives better understanding to molecular interactions during the chromatographic separation. Moderate conditions are required because the separation circumstances (such as higher flow-rate, which causes temperature increase in the column) can influence the enantiomerization kinetics. Unfortunately, applying our model seems to be cumbersome because it requires the understanding of Fourier transform and inverse Fourier transform. To get through this obstacle, a software is in the process of being written by us that will allow the fitting procedure to be done by the computer automatically. This way, we would obtain a gentle and convenient method to determine rate constants from chromatograms if direct chiroptical measurement is difficult or unavailable. The application is, however, restricted to lower flow rates and temperatures where the enantiomerization process is limited to only a few interconversions. In the case of fast interconversion, the enantiomers cannot be separated and only one broad peak can be observed.

Although other types of interconversions can also result in Batman peaks, it is important to note that our model has even broader applicability. There is only one main restriction, which is that the molecule should have at most two forms that interconvert to each other. It is also possible that the transformation is limited to one of the directions.

The stochastic theory helps us to become an Ant-Man in the world of chromatography and envision the separation process at the molecular level using variables that are easy to understand. By considering different types of distributions, we have developed three individual novel models. Using them, the pore-size distribution can easily be ascertained in a nondestructive way from a number of chromatograms and when Batman peaks are present in chiral chromatography, the rate constant of the interconversion process can also be obtained.

So far, we have determined the pore geometry and the pore-size distribution of seven commercially available C18 stationary phases (both fully porous and core–shell) and the mesopore structure of monolithic columns of the first and second generation, respectively. ISEC combined with the novel stochastic model containing the pore-size distribution is a great nondestructive alternative to traditional methods such as mercury intrusion or low-temperature nitrogen adsorption. It is also possible to use this method to obtain the pore structure of any material.

The rate constant of the enantiomerization of quetiapine was determined by our novel stochastic model. Good correlation was found between these results and those obtained from optical activities and by using DCXplorer, respectively. Moreover, our model can help to understand the molecular processes emerging in chromatographic enantiomer separations. This stochastic theory can be used for other types of interconversion occurring during the separation process (tautomers and rotamers).

However, the stochastic theory gives us an infinite universe whose door we have just opened. Let’s be Ant-Men (and love math)!

I would like to thank the contribution of my colleagues: Attila Felinger, Ivett Bacskay, Gábor Németh, and Dóra Németh. Their help was essential to derive the models and apply them to real samples.

(2) F. Dondi, A. Cavazzini, M. Remelli, A. Felinger, and M. Martin, 

(3) F. Dondi, G. Blo, M. Remelli, and P. Reschiglian, In 

Theoretical Advancement in Chromatography and Related Separation Techniques

, F. Dondi and G. Guiochon, Eds. (Springer Netherlands, Dordrecht, The Netherlands, 1992), pp. 173–210.

(4) A. Sepsey, I. Bacskay, and A. Felinger, 

(5) A. Sepsey, I. Bacskay, and A. Felinger, 

(6) I. Bacskay, A. Sepsey, and A. Felinger, 

(7) I. Bacskay, A. Sepsey, and A. Felinger, 

(8) A. Sepsey, D.R. Németh, G. Németh, and A. Felinger, 

 is with the Department of Analytical and Environmental Chemistry at the University of Pécs in Pécs, Hungary. Direct correspondence to: 

The stochastic theory of chromatography allows one to connect mathematics to separation science in an intelligible form. We take a “walk” through the column at the level of an Ant-Man, where we can see that chromatography is mathematics and mathematics is chromatography!

An Ant-Man Perspective for Chromatography: My Stochastic World

Dispersion (broadening, or spreading) of analyte zones (peaks) outside of chromatography columns can seriously erode the resolution that good columns provide. In this installment, we focus on the contributions of dispersion in connecting tubing and detectors to the total level of extracolumn dispersion in a LC system.

In the installments of “LC Troubleshooting” from the last two months, we reviewed the basic concepts of extracolumn dispersion (ECD) (1), mentioned how the level of ECD associated with a particular instrument can impact the apparent quality of a separation (for example, as measured by resolution), and discussed dispersion associated with the injection step in some detail (2).

In this month’s installment, we continue our discussion of details associated with the contributions of specific system components. Figure 1 illustrates the contributions of different system components to the total observed variance, starting with the injection step and ending with the detection step. In principle, the total observed peak variance is simply the sum of the variances contributed by each system component. This is only rigorously correct if the dispersion in each element of the system is independent of the others (3), but under most conditions, equation 1 is accurate enough to guide method development and system optimization (4). In this installment of “LC Troubleshooting,” we continue by discussing dispersion in connecting tubing and detectors.

FIGURE 1: Illustration of the contributions of several LC system components to the total variance of peaks observed at the detector, σ

. We note that equation 1 is valid using either time- or volume-based variances.

It is a well known fact that increasing the length and inner diameter of connecting capillaries in a chromatographic instrument increases dispersion. This dispersion is the result of the parabolic flow profile that is established in an open capillary under laminar flow conditions. Under these conditions, the fluid in the center of the tube moves at twice the average velocity, whereas the fluid near the wall is stagnant (that is, stuck to the wall). Estimating the contribution of this dispersion that occurs in connecting capillaries to the total extracolumn band broadening in a liquid chromatography (LC) system is not straightforward. The two limiting conditions for which analytical solutions exist are as follows: 1) dispersion in a short, straight capillary at high-flow rates (with negligible radial equilibration between velocity streams); and 2) dispersion in very long capillaries at low-flow rates (that is, where full radial equilibration between velocity streams occurs by diffusion). In the first case, the dispersion contribution of the capillary is given by the Atwood-Golay equation below (5):

 are the length and diameter of the capillary, respectively. In the second case, the dispersion is given by the Taylor-Aris equation, where 

 is the flow rate through the capillary and 

 is diffusion coefficient of the analyte in the mobile phase (6).

Most conditions used for practical LC methods lie between the extreme conditions where equations 2 and 3 are valid, and thus require that we consider a partial radial equilibration of the analyte across the tubing diameter (that is, across different mobile phase velocity streams). For this purpose, we can use either a Giddings-style coupling of the previous expressions (equation 4) (7), or an exponential model based on numerical simulations (equation 5) (8):

. In practice, however, equations 4 and 5 both seem to overestimate the degree of dispersion due to these capillaries in comparison to experimental observations, especially at high flow rates (7). One possible explanation for this observation comes from the fact that these equations are only valid for a straight tube, which is seldom used in real instruments where the capillaries are coiled and bent to fit in different instrument components, such as the injection valve, mobile phase preheaters, the column, and the detector inlet. As a result of this coiling, centripetal forces acting on the liquid cause secondary radial flow effects in the liquid. Beyond a critical flow rate, these radial flow effects are pronounced enough to enhance radial mixing and thus the equilibration of the analyte across the diameter of the tube. This in turn relaxes the axial dispersion that normally results from the parabolic flow profile. Nevertheless, equations 2–5 make it clear that using shorter and narrower capillaries reduces contribution of these tubes to the total extracolumn dispersion. This is especially true when using gradient elution, where the volumes of peaks exiting the column are typically much smaller than they are when using isocratic elution. Figure 2 shows that even for a relatively short (140 mm) piece of tubing between the column outlet and the detector inlet, the diameter of this tube can have a dramatic effect on peak width observed at the detector (3). When considering the use of narrow capillaries, however, one must be cautious about the pressure required to push the mobile phase through these tubes at the desired flow rate (Δ

), because this increases along with the inverse fourth power of the column diameter as shown in equation 6 below (where 

 is the dynamic viscosity of the mobile phase).

FIGURE 2: Example of the effect of post-column tubing diameter (

 = 140 mm in each case) on peak width and height in the case where gradient elution is used. The 

). The column used was 50 mm x 2.1 mm i.d. (1.3 μm particles), the analyte was benzophenone, and the flow rate was 0.6 mL/min. Adapted from reference (4).

For example, changing from a commonly used 120 μm i.d. stainless steel capillary to a 75 μm i.d. capillary comes at the cost of a 6.5-fold higher pressure drop.

Because of the widespread use of UV absorbance detectors in LC, most investigations into the contributions of detectors to extracolumn dispersion have been focused on UV detectors, although most fundamental aspects can also be applied to other types of detectors with flow-through detection cells. In a first, rough approximation, these flow cells behave as open-tubular flow paths that are either circular or rectangular in cross-section. As a result, the equations that govern dispersion in circular tubes can be used to estimate their contributions to extracolumn dispersion. However, the entire fluidic path in a detector module is more than the detection cell itself, and often involves additional in- and outlet tubing, sharp turns, and changes in the cross-sectional area or shape of channels. Nevertheless, in the literature, the volumetric variance of dispersion attributed to the detector has often been related to the geometrical detection cell volume (

) using an empirical relationship similar to that of the injection volume contribution (2).

The proportionality between cell volume and dispersion is not surprising because equations 2–5 show that a longer or broader flow path results in more dispersion. Thus, there is a tradeoff between dispersion and sensitivity during flow cell design because longer flow cells result in higher sensitivity (9), and broader cell design reduces signal noise. This is illustrated in Figure 3, which shows how narrower peaks are obtained using the flow cell with the smaller volume; however, this comes at the cost of reduced sensitivity (that is, lower peak height) and a much higher baseline noise level. Innovations in the fluidic and optical characteristics of these cells over the past decades have enabled improvements in sensitivity (higher) and noise (lower), while maintaining or decreasing flow cell volume.

FIGURE 3: Comparison of peak width and baseline noise for UV flow cells with different volumes. (a) Inset shows the pre-peak region where the baseline noise is obviously much worse for the smaller flow cell. Note that the peak heights before normalization were about 360 and 560 mAU for the 80 and 500 nL flow cells, respectively. Adapted from reference (3).

Recent studies have shown that equation 7 provides poor estimates of the actual contributions of flow-through cells to the total extracolumn dispersion (10,11). As was discussed for the injection step (2), if a rectangular plug of analyte would move through the detector flow cell in a perfect plug flow without mixing, 

 would be 12. On the other hand, if the flow cell would behave as a “perfect mixer”, 

 would be 1. However, if there are zones in the flow cell that are poorly swept because of flaws in the cell design, then values of 

In addition, another consequence of the similarities between dispersion in a flow-through cell and dispersion in a capillary is that dispersion in the flow cell is expected to be flow rate dependent. Using a custom detector that allowed continuous variation of the cell pathlength, Dasgupta and others also showed that for short cell path lengths, the in- and outlet connections to the illuminated part of the flow cell make up the majority of the measured dispersion (10). To a first approximation, the dispersion at higher flow rates, typically above 0.5 mL/min, from modern low volume flow cells can be estimated using equation 7 with 

 = 0.5–0.8 (11). Given the fact that the dispersion of a flow cell is significantly affected by its internal design, some instrument vendors no longer report the geometrical cell volume, and instead report the expected contribution of the flow cell to peak widths (11).

In discussions on dispersion associated with detectors it is important to note that settings associated with the electronic components of the detector (for example, sampling frequency, and detector rise time or time constant) can also affect peak width and shape. Although strictly speaking these do not contribute to extracolumn dispersion in the same way as the other factors we have discussed (for example, detector cell volume), the effects of these settings can influence the observed peak variance and be confused with other contributions. Readers interested in learning more about this topic are referred to prior “LC Troubleshooting” articles (12), and some recent journal articles on the topic (13,14).

Finally, even though mass spectrometric (MS) detection is being used in more and more laboratories, there are far fewer studies of the contributions of MS detectors to extracolumn dispersion than there are for UV detectors (15,16). In the studies that have been done, however, it was found that when optimized MS settings are used, the ionization source and MS detector itself had little impact on observed peak widths. In fact, it was found that the tubing connecting the LC instrument to the mass spectrometer (MS) was the most critical contributor to the extracolumn dispersion for these hyphenated systems.

In this installment of “LC Troubleshooting,” we have continued our discussion of details associated with the contributions of specific LC system components to extracolumn dispersion, this time focusing on connecting tubing and detectors. In the next installment in this series, we discuss how the impact of extracolumn dispersion can be different under isocratic and gradient elution conditions, and discuss the impact of post-column flow splitting on the total dispersion observed at the detector.

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(4) K. Vanderlinden, K. Broeckhoven, Y. Vanderheyden, and G. Desmet, 

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(10) P.K. Dasgupta, C.P. Shelor, A.F. Kadjo, and K.G. Kraiczek, 

, 2063–2069 (2018). https://doi. org/10.1021/acs.analchem.7b04248

(11) K. Vanderlinden, G. Desmet, and K. Broeckhoven, 

, 489–498 (2019). https://doi.org/10.1007/s10337-018-3622-1

(13) M.F. Wahab, P.K. Dasgupta, A.F. Kadjo, and D.W. Armstrong, 

, 31–44 (2016). https://doi.org/10.1016/j.aca.2015.11.043.

(14) A. Felinger, A. Kilár, and B. Boros, 

, 178–182 (2015). https://doi.org/10.1016/j.aca.2014.11.014.

(15) D. Spaggiari, S. Fekete, P.J. Eugster, J.-L. Veuthey, L. Geiser, S. Rudaz, and D. Guillarme, 

, 45–55 (2013). https://doi.org/10.1016/j.chroma.2013.08.001.

(16) S. Buckenmaier, C.A. Miller, T. van de Goor, and M.M. Dittmann, 

, 64–74 (2015). https://doi.org/10.1016/j.chroma.2014.11.086.

 received his PhD in 2010 from the Vrije Universiteit Brussel (VUB), in Brussels, Belgium. Following post-doctoral research at VUB and work as a visiting researcher in the separation processes laboratory at ETH Zurich, in Switzerland, he became a research professor at VUB in 2012. He was subsequently promoted to Assistant Professor and then to his current position as an Associate Professor in 2017.

 is the editor of “LC Troubleshooting.” Stoll is a professor and the co-chair of chemistry at Gustavus Adolphus College in St. Peter, Minnesota. His primary research focus is on the development of 2D-LC for both targeted and untargeted analyses. He has authored or coauthored more than 75 peer-reviewed publications and four book chapters in separation science and more than 100 conference presentations. He is also a member of LCGC’s editorial advisory board. Direct correspondence to: 

In recent articles, we reviewed the basic concepts of extracolumn dispersion and how this phenomenon can impact the quality of an LC separation. We now specifically discuss the effects of dispersion that can occur due to tubing
and detectors.

Where Has My Efficiency Gone? Impacts of Extracolumn Peak Broadening on Performance, Part III: Tubing and Detectors

This blog is a collaboration between LCGC and the American Chemical Society Analytical Division Subdivision on Chromatography and Separations Chemistry.

Separation science is an intriguing and challenging (yes, let’s admit it) interdisciplinary field. Many of our daily rituals depend on effective chemical separations. This includes our much-needed morning coffee or tea!

Among the separation science subdisciplines, chromatography has surely played a dominant role in enabling separations of complex mixtures by exquisitely coupling fundamental knowledge with technological advancement. In my opinion, one of the major strengths of chromatography is the solid, fundamental understanding that many research groups in the world have tirelessly worked on building for the scientific community.

In the realm of separation methods, analytical extraction and sample preparation can be considered the “Cinderella” subfield—it is often overlooked and its attributes and value are unrecognized. Everyone needs to prepare and extract their samples prior to introduction into separation or detection systems, however, a lesser number of users fully understands the fundamental concepts and the fascinating interplay of chemistry, physics, engineering, and material science that govern analytical extraction processes.

After starting my independent academic career and being responsible for mentoring graduate and undergraduate students, the words of my post-doctoral advisor, Prof. Janusz Pawliszyn, still resonate loudly in my mind: “You need to understand the fundamentals!” This was the common refrain we heard at every group meeting at the University of Waterloo. I must admit that now, the same words often come up during my group meetings at the University of Toledo.

Understanding the fundamentals of extraction methodologies is in fact critical for research and development in academia as well as in industrial settings, and can enable chromatographers to find smart solutions to tackle the complex composition of different types of samples.

To achieve this understanding, it is important to focus not only on the number of applications that a certain extraction methodology can accomplish but also on what drives the performance of that methodology in terms of extraction efficiency, selectivity, and compatibility to separation and detection platforms. Concepts of mass transfer, analyte and extraction phase interactions, adsorption isotherms, matrix effects, and matrix compatibility must be very well studied and understood, to properly tune extraction methods to diverse samples and analytical needs.

In this context, educators play a critical role in raising awareness about the science behind analytical extraction methodologies. More efforts should be made to include this topic in analytical chemistry curricula both at the graduate and undergraduate levels. On the other hand, a challenge for educators is to find textbooks that specifically focus on analytical sample preparation, rather than relying on more generic textbooks that only describe a few of these techniques in the last few paragraphs of chapters on chromatographic techniques. To the best of my knowledge, only a few textbooks provide a comprehensive overview of extraction techniques from an analytical perspective(1–3). As my contribution to the cause, I was able to redesign my “CHEM 8310 Advanced Analytical Chemistry” course to provide a detailed description of analytical sample preparation and extraction methods suitable to small molecules, macromolecules, and inorganic species. It was rewarding to notice how the attendance for this course expanded from chemistry and biochemistry students to pharmacy and chemical engineering students as well.

The lack of understanding of the role of sample preparation goes beyond the educational aspect, however. When I interact with peers, a question that often comes up is, Why would I spend time on sample prep when I can just use an advanced separation system or the powerful high-res mass spectrometer? That is a legit question for sure, and different points of view make science interesting.

My take is this: Why wouldn’t you spend some time on your sample prep to avoid garbage-in–garbage-out effects and repetitive maintenance on your system after injecting dirty samples? Why wouldn’t you want to get a clean chromatogram when you can? Lucky you, if you have access to very sophisticated instrumentation or to a very talented instrument repairperson, but not every lab can.

We all too often see analytical sample preparation tools sold as a “kit” to extract specific analytes from defined samples and aimed to simplify the analyst’s work. While this approach works very conveniently for routine analysis, it has given the wrong impression over the years that all sample prep researchers do are easy and repetitive protocols such as passing a sample through a solid-phase extraction (SPE) cartridge or immersing a solid-phase microextraction (SPME) fiber in a liquid.

The potential of extraction methodologies goes beyond the traditional misconceptions. Simply optimizing and validating analytical methods is not all that scientists devoted to sample prep should and can do.

Today’s scientific community has access to a very diverse “sample prep toolbox” that includes exhaustive and non-exhaustive techniques, micro-scale approaches, and more. Let’s make the effort not to limit sample prep research to the analysis of 

 samples, and utilize what every sample prep technique has to offer to study more in-depth biological phenomena, chemical and physical transformations, and phase distribution, to mention few applications.

I also want to emphasize here that strategizing the proper extraction approach can facilitate complex chromatographic separations as well. In fact, I strongly agree with my colleague Flavio A. Frachina when he mentions that coupling the proper extraction method with chromatographic separation platforms is “the most powerful analytical alliance” in modern separation science (4).

I would like to conclude by saying that it is important to acknowledge that no sample preparation or extraction technique is a panacea. Therefore, to know what’s best for your specific analytical needs,“You need to understand the fundamentals!”

Sample Preparation Techniques in Analytical Chemistry

 (John Wiley & Sons, Inc., 2003). doi:10.1002/0471457817

2.J.B. Pawliszyn, J.M. Bayona, P. Dugo, X.C. Le, H.K. Lee, X.-F. Li, and H L. Lord, 

Comprehensive Sampling and Sample Preparation - 1st Edition

 (John Wiley and Sons, 2011). doi:10.1002/9780813823621

4.F.A. Franchina, D. Zanella, L.M. Dubois, and J.F. Focant, 

(1), 188–210 (2021).doi:10.1002/jssc.202000855

 is an Assistant Professor of Chemistry at the Department of Chemistry and Biochemistry of The University of Toledo (Ohio). Research work in Gionfriddo’s laboratory focuses on the development of advanced analytical separation tools for the analysis of complex biological and environmental matrices, with emphasis on alternative green sample preparation methodologies. She received her BSc (2008) and MSc (2010) in Chemistry and her PhD in Analytical Chemistry (2013) at the University of Calabria (Italy). She joined Prof. Pawliszyn’s group at the University of Waterloo (Ontario, Canada) in 2014 as Post-Doctoral Fellow and manager of the Gas-Chromatography section of the Industrially Focused Analytical Research Laboratory (InFAReL), and within three years became a Research Associate. Gionfriddo has currently authored more than 50 peer-reviewed contributions including a patent on PTFE-based SPME coatings. Gionfriddo is one of the founding members of the Dr. Nina McClelland Laboratory for Water Chemistry and Environmental Analysis at The University of Toledo and she is on the Ohio Attorney General Yost’s Environmental Council of Advisors. She also serves on the executive committee of the ACS Subdivision on Chromatography and Separations Chemistry. Her research program is currently funded by the National Oceanic and Atmospheric Administration and several industrial partnerships.

 and the American Chemical Society Analytical Division Subdivision on Chromatography and Separations Chemistry (ACS AD SCSC). The goals of the subdivision include

promoting chromatography and separations chemistry

organizing and sponsoring symposia on topics of interest to separations chemists

developing activities to promote the growth of separations science

Separation science is an intriguing and challenging (yes, let’s admit it) interdisciplinary field. Many of our daily rituals depend on effective chemical separations. 