When high performance liquid chromatography (HPLC) users are confronted with questions about how to improve upon the performance of an existing or recently developed method, there are many technological options to consider. This series offers guidance for a systematic approach to method improvement that can save time and resources and likely produce more robust methods. Part II explains options adjusting selectivity (peak spacing or elution pattern) when dealing with relatively simple mixtures—which are not always easy to separate—with a focus on reversed-phase LC (RPLC) separations.

As explained in a recent article (1), this article series was prompted by a question I was asked during a visit to a local laboratory here in Minnesota. The question was essentially, “What are my options for peak separation that quantitatively takes into account both the widths of peaks and the improving an old high performance liquid distance between them (that is, the distance chromatography (HPLC) method that works, but probably is not ideal?” There are many options, but it can be difficult to know where to start. This series of articles is intended to make it more manageable to think about how to proceed. Not all the options we will discuss will be available to all users, but it is valuable to understand what the full array of options looks like, so that you can feel confident and make an informed decision about how to proceed.

In last month's installment of "LC Troubleshooting," I kicked off this series of articles by reviewing and discussing some foundational concepts that form the basis of a systematic approach to thinking about how to improve the resolution (and indirectly, speed) of a separation. I discussed how different separation challenges (for example, simple versus complex mixtures) can require different approaches to improving resolution, and talked through a menu of options to improve performance, including both kinetic (that is, factors affecting peak width) and thermodynamic (that is, factors that affect peak sspeak facing) factors. I also discussed the importance of resolution as a metric of separation performance because it is a measure of peak separation that quantitatively takes into account both the widths of peaks and the distance between them (that is, the distance between the apices of the peaks). Finally, I discussed the influence of different factors on resolution, including retention (as measured by retention factor, 

), and selectivity (a). The relationship between resolution and these factors is often examined using the Purnell equation, and one of the key takeaways is that the influence of the selectivity of resolution is greater over a large range than are the influences of retention and efficiency. Thus, it is always wise to think about how one might change selectivity as a means of improving resolution.

In the second installment of this series, I focus on options to improve the performance of separations of relatively simple mixtures. In this case, because it is usually possible to fully resolve the components of the mixture, we can also thinking about how to improve the speed (throughput) of the separation; however, our discussion of speed will be addressed in a future installment. Many of the details of this discussion are applicable to the specific case of reversed-phase LC (RPLC) separations.

For the purposes of discussion in this series of articles, I think of a “simple mixture” as one containing a small number of compounds. What constitutes a number that is small enough for the mixture to be considered simple is discussed in the next section. In a very rough sense, I always consider a mixture with fewer than five components to be simple, and a mixture with more than 100 components to be complex. What happens between five and 100 really depends on the chemistry of the analytes and the goals of the separation. Readers interested in this aspect are referred to the discussion of “regular” and “irregular” samples by Snyder and Dolan (2). Although this is also a useful framework for thinking about how to approach method development, we will not discuss this framework here. It is very important to note here that just because I refer to a mixture as “simple” does not mean it will be easy to separate. Indeed, a mixture of just two analytes can be very difficult to separate, as in the case of a pair of enantiomers. In most cases, it will be impossible to resolve such a pair of compounds, unless a chiral stationary phase is involved.

What Are My Chances of Separating the Mixture I Have?

One way to answer this question about the probability of separating a particular mixture is to use the statistical overlap theory (SOT), which was developed mainly by Davis and Giddings starting in 1983, with their seminal paper introducing the idea (3). The SOT is based on a number of assumptions that may not be strictly consistent with the characteristics of a real method development problem; however, I think it is still a very useful means of thinking through the chances of actually separating a particular mixture at hand. If SOT predicts that the probability of separating the mixture is less than 1%, then it would probably be a major waste of time and resources to try to fully resolve the mixture, and we would be better off thinking about alternatives, such as two-dimensional (2D) separations or multiple methods (see below for more on this). One of the major assumptions of SOT is that peaks are randomly distributed throughout the resulting chromatogram when a mixture is separated. For a diverse mixture of analytes, the distribution of peaks is usually close enough to random for SOT to be applicable, but there certainly are exceptions to this. For example, separations of homologous series of analytes (for example, alkanes, and alkylbenzenes) produce chromatograms with highly ordered patterns of peaks. SOT cannot reasonably be applied to separations of samples that are dominated by compounds like this. In our discussion here, we only scratch the surface of SOT; readers interested in learning more are referred to the original paper by Davis and Giddings (3), and the subsequent literature that refers to this early work (4).

When considering simple mixtures, we are usually most interested in fully resolving all the components of the mixture. In SOT, this outcome is given the symbol 

, where m is the number of components in the sample, and 

Conceptually, peak capacity quantifies the number of peaks that can fit perfectly side-by-side in a given separation space without overlapping. Under isocratic conditions, this can be calculated easily from the desired resolution between adjacent peaks (

), and the retention factor of the most retained component in the separation (

Most calculations of peak capacity in the literature involve a resolution of 1, but if we want to baseline resolve them for quantitative purposes, then we need at least 1.5, and I have used that number here. Table I shows the peak capacities calculated using equation 2 for some combinations of 

 that are relevant in everyday practice of isocratic RPLC. The 

 values span a range from short (5) to long (20) separations, and the plate numbers span a range from what can be achieved easily on a short column with almost any instrument (5000), to what can only be achieved with modern columns and high-performing instruments (40,000). We see that the range of peak capacities that results is approximately 20 to 100. Realizing a peak capacity beyond 100 requires gradient elution conditions, 2D separations, or both; these are topics we will address in future installments in this series.

Now that we have a sense for the range in peak capacities that can be expected under a typical range of conditions, we can turn back to the original question: What is the probability of separating a given mixture with these peak capacities? Figure 1 shows this probability for mixtures in the range of 2–50 components, and peak capacities of 20, 100, 300, or 1000.

There are a number of important takeaways we can glean from this result. First, with a peak capacity of 20, the probability of resolving even a relatively simple five-component mixture is just 8%. This low probability is a harsh reality of statistics applied to chromatography. It is absolutely true that we can significantly improve these odds by adjusting the mobile phase chemistry, stationary phase chemistry, or both, to reorganize the peak pattern, but in the absence of this effort, the likelihood of fully resolving the mixture on the first try using a short separation and modest column are disappointingly low. In the process of method development, we should never be surprised if we cannot resolve our mixture on the first go. Second, when using a higher peak capacity separation of a 100, we see that the probability of resolving the five-component mixture increases dramatically to 60%. This is still far from a “sure thing,” but at least now the odds of success on the first try are better than 50%. Now, if we follow the blue line to the right, we see that the probability drops rapidly, reaching 1% for a 16-component mixture. Beyond 16 components, the likelihood of fully resolving the mixture is vanishingly small, and it will take a great deal of effort in method development to find conditions that provide complete resolution of the mixture. Finally, we see that improving peak capacity to 300 (achievable with a single reversed-phase LC column, under gradient conditions) improves things significantly, moving the 1% mark out to a 27-component sample. Giving ourselves a reasonable chance of resolving mixtures more complex than this requires even higher peak capacities, which can only be provided by 2D separations in most cases, at least under practically useful conditions.

An alternative to 2D separation that is sometimes used for samples of intermediate complexity is to use multiple conventional LC methods to target and resolve different subsets of the components of a mixture. For example, a C18 column might be used to resolve components 1–12, and a phenyl column may be used to separate components 13–20. The information provided by the two methods run separately for each sample can then be combined to provide the information needed for all 20 analytes in the mixture, whereas developing a single method to yield the same information may be very difficult and require a great deal of method development time and resources.

Improving Resolution Through Thermodynamic Adjustments: Changing the Selectivity

As discussed in the preceding section, the likelihood of separating even relatively simple mixtures using a single column under isocratic conditions is dismally low. Fortunately, we have at our disposal a number of levers we can use in method development to overcome the harsh reality of the statistics of random distributions and reorganize peaks to make better use of the space in our chromatograms. In LC, the chemistries of both the mobile and stationary phases can have significant impacts on selectivity, and thus the distribution of peaks in a chromatogram. In general, changing the mobile phase is easier experimentally, but has less of an impact on selectivity; changing the stationary phase is more expensive and harder (but not impossible) to automate, but has more potential for providing large changes in selectivity.

When it comes to adjusting the mobile phase, in RPLC, both the solvent type and the aqueous or organic solvent composition influence selectivity. Currently, we do not know how to predict which organic solvent will provide the most useful selectivity for a given mixture of molecules, thus it is most common in practice to simply do a solvent screen early in method development to see which solvent is most likely to provide the needed selectivity. Figure 2 shows that the impact of the solvent type on the peak pattern can be significant. The dashed lines connecting the peaks for specific compounds are drawn in to indicate which compounds are impacted the most, and that elution order reversals can occur.

We also do not know how to predict a priori the amount of organic solvent (that is, the organic:aqueous ratio) in the mobile phase that will yield the selectivity needed for a successful separation. However, the good news is that once a solvent is chosen, the retention behavior for a given molecule is consistent enough that a retention model can be built from a few initial retention measurements. With this model in hand, optimal conditions can be found by simulation and then verified by experiment. The development of this general approach began in the 1980s with the work of Snyder and Dolan and coworkers (5), and the approach has enjoyed great success through the years, with development of several commercially available software packages along the way (such as DryLab and LC Simulator). Figure 3 shows an example of how a retention model (Panel A) can identify mobile phase compositions that yield selectivities that looks promising (see dashed lines C and D) to fully resolve the mixture. Panels B–D show chromatograms simulated using mobile phase compositions highlighted by the dashed lines in the plot. In Panel B we see coelution of mefenamic acid and 4-n-hexylaniline, which is predicted by the crossing of the green and purple lines in Panel A. In Panels C and D, we see that the mixture is fully resolved, but the conditions in Panel D are more attractive because they yield resolution of the mixture in a shorter time.

Using this approach to improve isocratic separations is straightforward in practice and requires a few initial “training experiments” where the mobile phase composition is deliberately varied so that the dependence of retention on mobile phase composition can be established. These data can then be fed into the software that allows identification of candidate conditions that are likely to provide resolution of the sample mixture in the shortest analysis time.

In addition to changing the organic solvent type and amount (as shown in Figures 2 and 3), changing the mobile phase pH can also be a powerful way to adjust selectivity, particularly when the sample mixture contains ionogenic analytes. This is a complex topic that requires too much discussion to address here; however, it has been covered extensively in previous installments of “LC Troubleshooting”; interested readers are referred to those prior installments to learn more (6).

Changing the stationary phase can also be an effective means of adjusting selectivity. As I tell my students often, the good news is that we have approximately 1000 different RPLC columns to choose from the in marketplace. The bad news is that in practical method development, we don’t have the time or resources to try them all, so we have choose a few to try. Figure 4 shows an example of the dramatic impact that changing the stationary phase can have on the elution pattern for a simple mixture. The extent to which the changes are more or less different than the change observed when changing the organic solvent type (as in Figure 2) will depend on the chemistry of the analytes and stationary phases involved. Our ability to predict which stationary phases are the right ones to try is pretty limited, but there are some tools to help with this. The hydrophobic subtraction model (HSM) of reversed-phase selectivity, developed by Snyder and Dolan and coworkers (7), provides a means of identifying column pairs that are either similar or different. In the context of method development where we want to adjust selectivity by changing the stationary phase chemistry, the HSM can be used to suggest candidate columns to try. Currently, data for approximately 750 columns are available and free web-based tools can be used to interrogate the results. Readers interested in learning more about the model and use of the tools are referred to previous articles in 

 (8), and the websites hosting the tools (

https://apps.usp.org/app/USPNF/columnsDB.html

Many HPLC users are confronted with questions about how to improve upon the performance of an existing method. Fortunately, modern HPLC technologies (that is, instrumentation, columns, and software) provide many possible avenues to make improvements. However, the number of possibilities can be intimidating, making it difficult to decide upon a particular path to improvement. In this installment of “LC Troubleshooting,” I have discussed options for adjusting selectivity (that is, peak spacing, or elution pattern) when dealing with relatively simple mixtures, with a focus on reversed-phase LC separations. In LC, both the chemistry of the mobile phase and the stationary phase strongly influence selectivity, thus changing one or both of them can be viable means of bringing about changes in selectivity. In the case of the mobile phase, changing the type and amount of organic solvent, as well as the pH, are all important factors to consider. In many cases, changing the stationary phase chemistry is the most impactful, albeit more expensive, means of changing selectivity. These are all important options to keep in mind when thinking about how to improve a separation.

(1) Stoll, D. R. What Are My Options to Improve My Separation? Part I: Foundational Concepts. 

High-Performance Gradient Elution: The Practical Application of the Linear-Solvent-Strength Model

 (John Wiley & Sons, Hoboken, NJ, 2007) pp. 1–22.

(3) Davis, J. M.; Giddings, J. C. Statistical Theory of Component Overlap in Multicomponent Chromatograms. 

(4) Schure, M. R.; Davis, J. M. The Simple Use of Statistical Overlap Theory in Chromatography, 

LCGC Europe – Recent Developments in UHPLC and HPLC

(5) Snyder L. R.; Dolan J. W.; Lommen D.C. Drylab® Computer Simulation for High-Performance Liquid Cromatographic Method Development. 

(6) Dolan, J. W. Back to Basics: The Role of pH in Retention and Selectivity. 

(7) Dolan, J. W.; Snyder, L. R. The Hydrophobic-Subtraction Model for Reversed-Phase Liquid Chromatography: A Reprise. 

(8) Dolan, J. W.; Stoll, D. R.; Snyder, L. R. How to Take Maximum Advantage of Column-Type Selectivity. 

Articles

Column: LC Troubleshooting

Even relatively simple mixtures are not always easy to separate. What are the options for adjusting selectivity in reversed-phase LC separations?

What are Options to Improve My Separation? Part II: Likelihood of Separation, Adjusting Selectivity for Simple Mixtures

Retention and selectivity in gas chromatography (GC) occur because of the thermodynamics of the phase transition from the vapor phase following sample injection to the solution phase of being dissolved in the stationary phase. Analytes then move along the column because the vapor phase is flowing. In this installment, we quickly review the basic thermodynamics underlying separation in GC, and we see how this impacts selectivity and retention. Last month, we saw how seemingly large differences in column polarity may still generate little, or even the opposite, effect on retention and generate confusion and difficulty in method development. This month, we explore this challenge more in-depth using thermodynamics and a freely available online GC simulator.

Like all separation and extraction techniques, gas chromatography (GC) relies on the equilibrium between two distinct phases. These two phases are a gas phase, which is moving, and a liquid or solid phase, which is stationary. The equilibrium process and its relation to retention times is discussed in the literature (1–3). A quick summary, establishing the relationship between thermodynamic constants and retention times, is provided here. The phase equilibrium and equilibrium constant expression for an analyte partitioning between the mobile and stationary phases is shown in equation 1:

Equation 2 shows the relationship between the partition coefficient, 

, and retention time. The gas hold-up time is 

, is the volume ratio of the mobile phase and stationary phase. As seen in equation 2, there is a linear relationship between retention time and partition coefficient:

Equation 3 is the classical van ’t Hoff equation, which relates the partition coefficient to the standard enthalpy and entropy of a reaction. In our case, this would be Δ

 of the phase transition described in equation 1. Equation 4 is the classical Gibbs’ equation, gas phase, which relates the partition coefficient and the standard free energy change.

As seen from equations 2 and 3, the thermodynamic constants, Δ

, and the temperature determine the partition coefficient, which, along with the gas hold-up time, determines the retention time. For a given stationary phase and analyte combination, these thermodynamic constants are easily measured by plotting 

), with the slope of the resulting line being -Δ

Optimizing selectivity is usually the goal of stationary phase selection. Selectivity, shown in equation 5, is seen as the ratio of adjusted retention time, retention factor, or partition coefficient, which is usually determined for adjacent peaks in a chromatogram. If equation 5 is combined with equation 4, it can be seen that selectivity originates from the difference in the standard free energy change, ΔΔ

, for the phase transition seen in equation 1. A larger ΔΔ

 indicates a greater ratio of partition coefficients and therefore a greater selectivity.

 to describe how thermodynamic constants can be used to predict retention times under any conditions for a given analyte and stationary phase combination. Rijks and others also used this concept for the determination of temperature-programmed retention indexes and Snow extended it further to multi-ramp temperature programs (4–7). Thermodynamic retention indexes provide the basis for today’s online tools for predicting retention times (8).

We can use the online tool ProEZGC, whose web address is provided in reference (8) and which employs thermodynamic retention indexes, to explore some factors that impact selectivity and retention and to follow up on our discussion of stationary phase polarity and retention from last month’s installment (9).

Retention Examples Using Traditional Stationary Phases

Figures 1 and 2 show chromatograms of hydrocarbon mixtures on two classical stationary phases simulated using ProEZGC: 100% polydimethylsiloxane (PDMS) and polyethylene glycol (PEG). The two columns have identical length, inside diameter, and film thickness. The inlet pressure and the flow rates are also identical. The temperature was also identical for both at 40 

C isothermal with a total run time of 30 min for PEG and 60 min for PDMS. Identical column dimensions and separation conditions allow for direct comparison of all chromatographic parameters.

The mixtures consisted of a homologous series of 

-alkanes and three additional compounds often used in stationary phase polarity determinations: benzene, which measures pi–pi interactions; ethanol, which measures hydrogen bonding; and pyridine, which measures acid–base interactions. The two stationary phases are expected to exhibit different intermolecular interactions with PDMS showing almost entirely dispersive interactions and PEG showing mostly hydrogen bonding.

Examining retention of the homologous series of 

-alkanes, several observations related to the fundamental thermodynamics and the differences in intermolecular interactions are seen. First, note that within the 30 min time window for the separation at 40 

C, on PEG, tridecane (C13) is eluted in approximately 26 min, whereas on PDMS undecane (C11) it requires approximately 52.5 min, and it is eluted on PEG in approximately 6.5 min. Alkanes undergo almost entirely dispersive interactions. This demonstrates that the dispersive interactions are much stronger on PDMS than on PEG.

-alkanes on both stationary phases. Adding methylene (-CH

-) units to a straight chain hydrocarbon has a roughly linear effect on the standard free energy of vaporization. This leads to an exponential effect on the partition coefficient, resulting from the classical Gibbs’ equation and a resulting exponential effect on the retention time, as seen in equation 2. We see this in the peak spacing for the 

Finally, examine the three non-alkane compounds. Table I shows retention time data, partition coefficients and partition coefficients, for these three compounds. Using the retention factor 

, we can examine how the change in stationary phase affects the retention of each analyte based on intermolecular interactions.

Ethanol shows the least retention of all compounds in the mixture on PDMS, with a retention factor of approximately 0.1, indicating that approximately 10% of the ethanol molecules are dissolved in the stationary phase at any moment in during the separation. The equilibrium shown in equation 1 lies strongly to the left, favoring the mobile phase. On PEG, ethanol shows a retention factor of approximately 2, indicating that approximately two-thirds, or 67%, of the ethanol molecules are dissolved in the stationary phase at any point in the separation. This equilibrium lies to the right, favoring the stationary phase. Because the PEG stationary phase exhibits primarily hydrogen bonding, it is no surprise that ethanol would be 6–7 times more strongly retained on PEG than PDMS.

Benzene is more strongly retained on PEG than ethanol with a retention factor of 1.2 indicating that slightly more benzene is dissolved in the stationary phase; the equilibrium shown in equation 1 lies slightly to the right. On PDMS, benzene shows a retention factor of 0.7, indicating that more benzene is vaporized in the mobile phase and the equilibrium lies to the left. Benzene is generally considered non-polar or moderately polar, but is polarizable, leading to stronger interactions with the polar PEG stationary phase and greater retention.

Pyridine is most strongly retained of these three analytes on both stationary phases. On PDMS, 

 is 8.3. On PDMS, the equilibrium shown in equation 1 lies slightly to the right and on PEG it lies far to the right, both favoring the stationary phase, but PEG favoring it much more. Being basic, pyridine is expected to strongly bond with the slightly acidic PEG, which is seen in the much higher retention factor.

Figure 3 shows van ’t Hoff plots for benzene and ethanol on PDMS (Figure 3a) and PEG (Figure 3b), generated using simulations of the separations of each in a temperature range from 40–120 

C. Thermodynamic values, determined from the slope and 

-intercept of the lines are included on the plots. These values are the thermodynamic retention indexes described originally by Dose in 1987 and used in ProEZGC and other programs to predict retention times.

In Figure 3a, the two lines are spaced far apart, indicating high selectivity in the temperature range shown on the plot. Note that as the temperature increases (reading the 

-axis on the plot from right to left), the two lines converge slightly, indicating that selectivity decreases with increasing temperature, as expected. The high selectivity for ethanol and benzene, indicated in Figure 3a, is also seen in Table I, with the retention factor of benzine being 0.72 and ethanol 0.098, for a selectivity, as calculated using equation 5, of approximately seven. Therefore, PDMS is highly selective for benzene and ethanol.

In Figure 3b, showing lines for benzene and ethanol on the PEG stationary phase, the lines are much closer together, indicating much lower selectivity. Using the data in Table I, we see that the selectivity for benzene and ethanol on PEG is approximately 1.1, much lower than on PDMS. From Table I, note that the retention times for both are higher, indicating that PEG is more retentive but less selective than PDMS for these two compounds. Finally, note that the position of the lines also predicts the elution order. Reading up from the bottom of the graph, the earlier eluting compound is shown by the line closer to the 

Figure 4a shows a simulated separation of benzene and ethanol on a PEG stationary phase at 40 

C and Figure 4b shows the same separation at 120 

C. First, note the impact of temperature on selectivity as seen by the spacing of the peaks calculated from the adjusted retention times and Equation 5, 1.1 at 40 

C, except with the order of the peaks reversed. There is almost no selectivity at 90 

C, as seen in Figure 3, with the data points for benzene and ethanol almost fully overlapped. Generally, since higher temperatures favor the mobile phase, pushing the equilibrium seen in equation 1 back to the left, selectivity decreases as the temperature increases. See also the change in elution order. At 40 

C ethanol is eluted first. This can be explained by the difference in slope of the two lines in Figure 4b.

The difference between the lines generates selectivity, the larger the spacing between the lines, the greater the selectivity. Retentiveness can also be predicted from this plot or from simply estimating partition coefficients. A larger partition coefficient, 

 indicates stronger retention and longer retention times. For two compounds, a larger difference in 

Retention and selectivity in GC are based on thermodynamic values for the phase transition between the mobile and stationary phases. By examining the basic thermodynamics and equilibrium constants for this process, we can systematically compare retention on the same stationary phase with changing conditions or across stationary phases. We see that strong retention does not always mean high selectivity and that selectivity changes with temperature, sometimes resulting in changes in elution order on the same stationary phase. Larger differences in partition coefficients or in standard free energy changes generate higher selectivity. Larger partition coefficients and more negative standard free energy changes generate longer retention.

(1) Snow, N. H. Determination of Free-Energy Relationships Using Gas Chromatography. 

(2) Vitha, M. F. Short Gas Chromatography Experiment That Richly Connects Thermodynamics to the Intermolecular Interactions That Govern Solute Retention. J. Chem. Educ. 2022, 99 (5), 1923–1930. DOI: 

(3) McNair, H. M.; Miller, J. M.; Snow, N. H. 

 (John Wiley and Sons, Hoboken, NJ, 2019).

(4) Dose, E. Simulation of Gas Chromatographic Retention and Peak Width Using Thermodynamic Retention Indexes. 

(5) Curvers, J.; Rijks, J.; Cramers, C. et al. Temperature Programmed Retention Indices: Calculation from Isothermal Data. Part 1: Theory. 

(6) Curvers, J.; Rijks, J.; Cramers, C. et al. Temperature Programmed Retention Indices: Calculation from Isothermal Data. Part 2: Results with Nonpolar Columns. 

(7) Snow, N. H.; McNair, H. M. A Numerical Simulation of Temperature-Programmed Gas Chromatography, 

(8) Restek, ProEZGC Chromatogram Modeler, 

(9) Snow, N.; Rana, H. Does High Polarity Mean High Retention on Stationary Phases in Gas Chromatography? 

Column: GC Connections

By reviewing the basic thermodynamics underlying GC separations, we see how it impacts retention and method development in GC analysis.

Using Thermodynamics and Simulations to Understand Selectivity and Retention in Gas Chromatography

Organofluorine compounds are potential contaminants in the environment, particularly in natural water sources. Leo W. Y. Yeung, PhD, is a Senior Lecturer in the School of Science and Technology of the Man-Technology-Environment Research Centre (MTM) at Örebro University in Örebro, Sweden. His research has involved the analysis of organofluorine compounds of concern in the natural environment. We recently spoke to him about his work using combustion ion chromatography (CIC) and other methods to analyze organofluorine and specific perfluoroalkyl and polyfluoroalkyl substances (PFAS) compounds in environmental samples.

You have studied unidentified organofluorine compounds (UOF) and extractable organofluorine (EOF) compounds in the environment (1). What concerns you the most about either the type or quantity of such contaminants in the environment, particularly natural water sources?

Both the types and quantities of unidentified organofluorine compounds are of concern. There are fewer naturally occurring organofluorines compared to the vast number of per- and polyfluoroalkyl substances (PFAS) in the global market. The proliferation of organofluorine compounds is believed to be caused by humans. The question is whether these unidentified organofluorine compounds are newly introduced to the market, or if they are the unknown intermediate compounds from degradation of precursor compounds. Precursor compounds can be related to legacy PFAS compounds, such as perfluorooctane sulfonic acid (PFOS) precursors, or pharmaceuticals or pesticides containing fluorine, with the CF3-moiety. At the same time, it is not yet known what the effects of these unknown organofluorine compounds will be to the environment or humans. Studies are being undertaken to characterize these unidentified organofluorine compounds.

What are some of the greatest challenges you have encountered for characterizing and quantifying PFAS in environmental samples?

Depending on the definition of PFAS, the number of compounds may vary from 4700 to 12,000. Regardless of which PFAS definition we use, the class includes compounds having molecular weight ranges from approximately 114 g/mol (trifluoroacetic acid) to 1204 g/mol (bis[2-[ethyl(1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,8- heptadecafluorooctylsulfonyl)amino] ethyl] hydrogen phosphate-diSAmPAP), including side-chain fluorinated polymers, which exhibit quite different physico-chemical properties. As a result, no single extraction method can capture all PFAS and no single instrumental analysis method can analyze all PFAS. In our experience, we may need to extract the same samples using at least two extraction methods to capture polymeric and non-polymeric PFAS and use at least four instrumental methods, such as gas chromatography–mass spectrometry (GC–MS), liquid chromatography–mass spectrometry (LC–MS) (such as tandem mass spectrometry [MS/MS] and quadrupole time-of-flight mass spectrometry [QTOF-MS]), and supercritical fluid chromatography–mass spectrometry (SFC-MS) to discriminate approximately 100 PFAS compounds. It is important to note that analytical standards are only available for some 100 named PFAS compounds; thus, we may not be able to report the concentrations of some identified PFAS quantitatively because authentic reference standards are not available. In addition, as discussed above, not only are limited reference standards available, but even fewer internal standards are available for quantification to correct for recovery loss and ionization suppression in environmental samples.

What are the major theoretical and practical challenges for using combustion ion chromatography (CIC) as an analytical apparatus for EOF analysis (1–3)?

Fluorine analysis using CIC is a non-selective method, which means that you know that the sample contains fluorine, but you do not know which forms of the fluorine (organic bound or inorganic fluoride) are measured. Therefore, sample pretreatment is needed to separate inorganic fluoride from the organic bound fluorine for EOF analysis. However, different sample pretreatment, extraction methods, and washing steps may result in different amounts of EOF. Data compatibility between studies needs to be considered carefully; EOF analysis using method A may not give the same result as EOF analysis using method B. In general, the limit of quantification (LOQ) or detection (LOD) of PFAS requires analysis at the pg to sub-pg levels. Our CIC method achieves a limit of quantification/detection at ng levels. (Note: 1 picogram [pg] = 0.001 nanogram [ng]), which is 1000-fold less sensitive than that of LC–MS/MS). So samples of higher EOF concentrations are needed to achieve detectable signals from CIC.

You have published research on organofluorine analysis of whole blood samples in humans and the relation of these compounds to gender and age (4). Are these materials a concern for humans? Which groups are most affected?

The recent study was a pilot study to evaluate how much or proportion of unknown organofluorine were present in human blood. Samples were collected from different cities in Sweden where people donated their blood. Organofluorine mass balance analysis—the comparison between EOF and target PFAS—revealed that 63 monitored PFAS compounds accounted for 40% of the EOF in female samples; in males, target PFAS accounted for 59% of the EOF. Significant differences in the fraction of UOF were observed for both age groups and sex; the highest fraction of UOF was observed in young females (70% UOF, aged 18–44), which was different from observations found in other research studies for commonly monitored compounds (PFOS and so forth) where females were shown to have lower concentrations than males. However, the high fraction of UOF in human blood is of concern because we do not know the identity or sources of these unknown compounds so their potential health effects cannot be accurately evaluated.

You have investigated the presence of C1−C4 perfluoroalkyl acids (PFAAs), which are highly persistent chemicals, by studying their distribution and seasonal variation in remote Arctic locations (5). What did you learn from this work?

There are several important findings in this work. First, trifluoroacetic acid (TFA), perfluoropropanoic acid (PFPrA), perfluorobutanoic acid (PFBA), and trifluoromethane sulfonic acid (TFMS) were detected in most surface samples, including samples collected at remote locations without any potential for local sources, indicating that atmospheric transport of volatile precursors and their subsequent degradation play an important part in global distribution. Second, our results further support previously models that atmospheric concentrations of perfluorooctanoic acid (PFOA) would be 15–20 times higher in the Arctic in July compared to January, because of seasonal variations in atmospheric hydroxyl radical concentrations, which lead to degradation of 8:2 fluorotelomer alcohol (FTOH). Third, this is the first report of TFMS not only in surface snow but also at remote locations, suggesting this chemical may be globally distributed. However, further investigation is needed to understand the source and transport of TFMS to the Arctic.

What prompted you to work in the field of PFAS detection in the environment? 

We have learned that there are many disturbing effects of chemicals in the environment. For example, we know of the adverse effects of dichlorodiphenyltrichloroethane (DDT), described in the book 

, or polychlorinated biphenyls (PCBs), which caused the Yusho incident. These occurrences were acute and observable. However, in the case of PFAS, their effects are not acute when compared to DDT or PCBs; more recent epidemiological studies have demonstrated a number of adverse effects of PFAS, especially on the most vulnerable groups such as children. But what we know so far is only for the known PFAS compounds. What are the environmental and health affects of unknown PFAS? Even though we may not be able to identify what they are, at least we can to determine if the levels of unknown PFAS are increasing. We are trying our best to develop different analytical methods for detecting and identifying these compounds in order to protect the environment.

What are your greatest analytical challenges for accurate and precise analysis of PFAS materials in environmental samples?

The performance of different analytical laboratories for PFAS analysis has improved a lot when compared to what we observed in 2006 (6). The improvements are attributed to a number of factors, such as availability of reference and mass labeled internal standards, standard extraction procedures, and precise standard reference materials (SRMs). However, current analytical challenges remain the same for the newly discovered PFAS. To ensure good data quality, improvements in instrumentation, data analysis, analytical reference standards, mass-labeled internal standards, extraction methods, and standard reference materials are still needed.

For you, what are the most important questions to answer or an important problem to solve related to PFAS contamination? 

At the moment, several questions have to be addressed, which include: How much of PFAS are being overlooked? What are the unknowns? What is the source of these compounds? What are their effects to the environment including humans? Finally, how can we clean up PFAS contaminated areas in a cost-effective way?

What is your next area of research interest?

When one performs a literature search on PFAS, most of the research has been focusing on condensed phases (water, soil and sediment) or human exposure (internal concentration or food/water); relatively fewer investigations are available in air. It is very difficult to capture volatile PFAS in the air or to measure these aerosol compounds; therefore this area of research should be focused on more, especially when considering the destruction of PFAS by incineration, which, if not properly conducted, might produce other forms of PFAS as well as hazardous greenhouse gases. Therefore, analytical methods for measuring these volatile PFAS as well as new sampling techniques should be further developed.

(1) Aro, R.; Carlsson, P.; Vogelsang, C.; Kärrman, A.; Yeung, L. W. Fluorine Mass Balance Analysis of Selected Environmental Samples from Norway. 

(2) Aro, R.; Eriksson, U.; Kärrman, A.; Jakobsson, K.; Yeung, L.W. Extractable Organofluorine Analysis: A Way to Screen for Elevated Per- and Polyfluoroalkyl Substance Contamination in Humans? 

(3) Aro, R.; Eriksson, U.; Kärrman, A.; Reber, I.; Yeung, L. W. Combustion Ion Chromatography for Extractable Organofluorine Analysis. 

(4) Aro, R.; Eriksson, U.; Karrman, A.; Yeung, L.W. Organofluorine Mass Balance Analysis of Whole Blood Samples in Relation to Gender and Age. 

(5) Björnsdotter, M. K.; Hartz, W. F.; Kallenborn, R.; Ericson Jogsten, I.; Humby, J. D.; Kärrman, A.; Yeung, L. W. Levels and Seasonal Trends of C1–C4 Perfluoroalkyl Acids and the Discovery of Trifluoromethane Sulfonic Acid in Surface Snow in the Arctic. 

(6) van Leeuwen, S. P. J.; Kärrman, A.; van Bavel, B.; de Boer, J.; Lindström, G. Struggle for Quality in Determination of Perfluorinated Contaminants in Environmental and Human Samples. 

Focus On Environmental Analysis

Analysis of unidentified organofluorine compounds and known PFAS in environmental and human samples presents many challenges. Combustion ion chromatography is one technique that can help.

Analyzing Organofluorine Compounds in the Environment Using Combustion Ion Chromatography (CIC) and Other Methods

You probably think you know how to pipette correctly. But do you?

At the heart of most chromatographic analyses are the gravimetric and volumetric operations used to prepare samples, standards, and high-performance liquid chromatography (HPLC) mobile phases. Most of us believe we are proficient in these basic laboratory skills, but from time to time, I like to revisit the skills, theory, and regulatory recommendations for even for the most basic operations. So, let’s take a little deeper dive into the requirements for the use of so called “automatic” pipettes.

Let’s start with choosing the correct pipette for the job and compare and contrast the differences. In short, an air displacement pipette has a layer of air between the head of the piston and the liquid within the pipette tip. Air pressure is used to force liquid from the tip. Positive displacement pipettes have an integral piston within the disposable tip and the piston is in contact with the liquid being dispensed.

The manufacturers recommendations align well when considering which type of pipette to use for which sample type. Use air displacement pipettes for aqueous solutions and biological samples, and positive displacement pipettes for relatively volatile organic solvents, viscous solutions, and corrosive acids or bases (I wonder if, like me, you had been using an air displacement pipette for relatively volatile organic solvents or viscous solutions for years).

One pipette tip installation for air displacement pipettes: When installing a tip onto a air displacement pipette, rather than pressing the pipette shaft firmly and directly down onto the tip (apparently known as “hammering”), it is recommended that a slight twisting motion is used to create a good seal. Tips in racks are convenient (if more costly) for good practice in pipette tip fitting. For positive displacement pipettes, the above guidance should be followed, but once the tip is installed onto the shaft, the tip should be lifted out of the rack and the piston lowered until a “click” is heard as the piston locks into the pipette body. The plunger should then be pressed to the first stop position.

When using a variable volume pipette, some manufacturers recommend the volume is dialed using a clockwise motion as the dispensed volume is controlled by a piston spring. For maximum accuracy, the spring should always be set under tension by turning the thumbscrew clockwise. Operationally, when reducing the volume, slowly move the volume setting dial to the required volume, but when increasing the volume, overshoot the required setting by 1/3 of a turn and then reduce the volume to the desired setting. In this way, the desired volume is always reached using a clockwise motion and the piston spring remains under tension. Always hold the volume setting at eye level to avoid parallax error.

Holding the pipette vertically, smoothly depress the plunger to the first stop position, immerse the tip of the pipette into the liquid to be aspirated, and aspirate the liquid by smoothly and steadily raising the plunger to the rest position. If the tip is immersed too deeply, the increased hydrostatic pressure may cause too much liquid to be aspirated or liquid droplets to adhere to the outside of the pipette tip which may be transferred to the receiving vessel. If the tip is not immersed deeply enough, then vortexing may occur, or the liquid level may fall below the tip before reaching the top of the filling stroke, and too little liquid will be aspirated.

Wait two seconds for the aspirated liquid to move up into the tip and to equilibrate the temperature and pressure of the headspace air above the liquid. This step is particularly important in air displacement pipettes because of the changing compressibility of the headspace air above the aspirated liquid because of temperature and vapor pressure changes.

This first aliquot should then be discarded to waste, or if the liquid is to be retained, returned to the original container. Note that any contaminants present on the inside surface of the pipette tip may also be returned to the sample or reagent.

This tip-wetting step can significantly reduce the error associated with pipetting, and acts to reduce any interfacial effects between the liquid being aspirated and the inside surface of the pipette tip. With certain liquid types, or during pipette calibration or testing, some manufacturers and standards bodies call for up to five pre-equilibration and wetting step repeats prior to the first 'real' aspiration into a new tip or when volume changes are made to adjustable pipettes.

Place the upright pipette tip gently against the wall of the receiving vessel, held at an angle of between 10 and 45 

. Smoothly depress the plunger to the first stop and to the second stop after a slight pause. Slowly draw the pipette tip 8–10 mm up or along the vessel wall prior to removal from the vessel and then slowly allow the plunger to return to the rest position and discard the pipette tip. Draw the pipette tip 8–10 mm along the vessel wall prior to removal.

To quote Henry Ford, "Anyone who stops learning is old, whether at twenty or eighty. Anyone who keeps learning, stays young." I feel the flush of my youth after this salutary learning experience!

Columns

How Well Do You Pipette?

Peter Schoenmakers and Emanuela Gionfriddo are the winners of the 16th annual 

 Lifetime Achievement and Emerging Leader in Chromatography Awards, respectively. The 

 Awards honor the work of leading separation scientists for lifetime achievement and emerging potential. The award winners will be honored in an oral symposium at the Pittcon 2023 conference in March 2023 in Philadelphia, Pennsylvania, USA.

The 2023 Lifetime Achievement in Chromatography Award honors an outstanding seasoned professional for a lifetime of contributions to the advancement of chromatographic techniques and applications (Table I).

Peter Schoenmakers, the 2023 winner, is a professor of Analytical Chemistry and a former Scientific Director of the van ’t Hoff Institute for Molecular Science at the University of Amsterdam. He is best known for his work in liquid chromatography (LC), starting with the theory of gradient elution in reversed-phase liquid chromatography and its optimization, and continuing through his many contributions to the analysis of polymers and his pioneering work in developing two- and three-dimensional LC methods, particularly of polymers. Schoenmakers is also the Education Director of Comprehensive Analytical Science and Technology (COAST), a Netherlands public-private-partnership organization on analytical chemistry. In 2016, he was awarded an ERC Advanced grant for the project Separation Technology for A Million Peaks (STAMP) project.

Schoenmakers obtained his master’s degree in chemical engineering from the Technical University of Delft, in The Netherlands, and performed his PhD research studies with Professor Leo de Galan in Delft and with Professor Barry Karger at Northeastern University in Boston, Massachusetts. After completion of his studies, Schoenmakers worked for Philips Research Laboratories in Eindhoven (The Netherlands), the Shell Research and Technology Centre in Amsterdam, and the Shell Westhollow Technology Center in Houston, Texas. In 1998, while at Shell, Schoenmakers became a part-time professor in polymer analysis at the University of Amsterdam. In 2002, he became a full-time professor of Analytical Chemistry at the University of Amsterdam, specializing in applications related to forensic science.

Over his career, Schoenmakers has received numerous awards, including the Silver Jubilee medal of the Chromatographic Society (1989); the Venema Award for Industrial Separation Science (1994); the Eastern Analytical Award for Excellence in Separation Science (2010); the Martin medal of the Chromatographic Society (2011); the John H. Knox Medal of the Royal Society of Chemistry (2014); the Csaba Horváth Memorial Award (2015); the CASS Award for Distinguished Contributions to Separation Science (2015); a European Research Council (ERC) Advanced Grant for Excellent Research (2016); the Fritz-Pregl Medal (2018), the Dal Nogare Award from the Chromatography Forum of the Delaware Valley (2019); and the American Chemical Society (ACS) Award in Chromatography (2022). In June 2022, Schoenmakers received a royal commendation as a “Knight of the Order of the Netherlands Lion,” one of the oldest and highest civil honors in the Netherlands.

The broad range of areas where Schoenmakers has made important contributions includes:

Pioneering development of multi-dimensional GC (two-dimensions [2D])

Multi-angle laser light scattering (MALLS) 

Theory of gradient elution in reversed-phase LC (RPLC)

Pioneering development of multidimensional LC (two- and three-dimensions)

Pyrolysis GC–mass spectrometry (Py-GC–MS)

Schoenmakers is the author of more than 300 publications, has presented more than 300 talks, and has given approximately 800 poster presentations. Table II lists Schoenmakers’s papers that are highlighted in this article. An assessment of the impact of his career highlights several main areas for his research career.

Schoenmakers’s work in GC before 1997 involves comprehensive 2D-GC (1), and has been cited approximately 250 times. This work described how the compositional analysis of hydrocarbon mixtures using 2D-GC is far superior to methods using 1D-GC.

“Peter’s greatest contribution is without any doubt his early papers on retention time modeling and the influence of the organic phase in RPLC,” said Hans-Gerd Janssen, a prominent analytical scientist at Unilever and Wageningen University who studied with Schoenmakers. Indeed, Schoenmakers’s early work in RPLC received considerable attention, being cited over 300 times (2). This early work focused on generalizing a theory of gradient elution in LC, and provided a strategy for selecting an optimal gradient in RPLC from two viewpoints. First, a general expression was derived for the numerical calculation of retention when the relationship between the capacity factor, 

, is known. Secondly, multicomponent solubility parameter theory was used to predict the relationship between 

. This work also addressed the essential variables to improve the accuracy of the predictions made.

Schoenmakers’s work in polymer characterization also stands out, both in terms of the fundamentals of separation techniques and their applications. His initial work in this area described the use of SEC and RPLC for characterizing polydisperse polymers (3,4). These papers examined how the existing theories of chromatographic behavior with respect to retention times and peak width for isocratic and gradient-elution LC could be expanded to predict the separation behavior of polydisperse macromolecules, such as natural and synthetic repetitive polymers.

He expanded this research to include the use of multiple detector types and detector configurations, including SEC with multi-angle laser light scattering (SEC/MALLS) (5), and online Fourier transform infrared spectroscopy (FT-IR) detection (6). Schoenmakers also expanded his work in complex mixture and polymer characterization by applying multidimensional chromatography (6–10). “I would consider Peter’s most significant technical contributions to be in the area of multidimensional macromolecular separations,” said André M. Striegel of the U.S. National Institute of Standards and Technology (NIST).

For many, however, what stands out the most about Peter Schoenmakers and his career is his pioneering work in multidimensional LC.

“Peter’s most significant contributions to the field of separation sciences relate to his pioneering developments of comprehensive two- and spatial three-dimensional liquid chromatographic concepts and their technical realization,” said Wolfgang Lindner, who is a professor at the University of Vienna in Austria and a lifelong friend and colleague since the time they studied with Barry Karger at Northeastern University.

Karger—who has followed Schoenmakers’s work since the 1970s when Schoenmakers spent time in Karger’s laboratory—agrees. “He is a leader in two- and three-dimensional chromatography,” he said.

Four of his papers (7–10) in particular exemplify his work in 2D-LC. In a 2010 paper (7), Schoenmakers and coworkers developed a method for optimizing total analysis time, total peak capacity, and total dilution for comprehensive 2D-LC. The approach is based on Pareto-optimality for estimating optimal parameters for column particle sizes and column diameters. This paper formally recognized the Pareto optimality approach to improving analysis parameters, and it recognized that dilution is an important variable to consider for analysis optimization.

In 2018, Schoenmakers’s group published a review of the recent progress to optimize analytical parameters for online 2D-LC. The goal of this review was to provide chromatographers with a clear understanding of which analytical parameters, including sampling time, can be optimized using current strategies and the rationale for implementing different optimization approaches (8).

Schoenmakers and his colleagues also developed a program for the systematic optimization of 2D-LC separations, using gradient elution in both dimensions (9). This program was successfully applied to a separation problem consisting of a mixture of 54 aged, synthetic dyestuffs, separated by ion-exchange chromatography and ion pair chromatography. Schoenmakers has continued to explore the rationale and principles of 2D-LC chromatography experiments, expanding the knowledge base of strategies to improve the quality of two-dimensional liquid chromatography separations (10).

In his work at the University of Amsterdam since 1998, Schoenmakers has taught many different chemistry courses and has advised the research of numerous university students—whom he generously encourages—in addition to teaching over 500 students in his short courses. “He has done a great job in training a large number of PhD students,” affirms Karger. “Peter’s career is about good science and educating the next generations of people to continue good science,” Janssen said.

Karger also noted the impact of Schoenmakers’s role in conferences. “He has been a major force on several of the key international meetings on chromatography,” he said.

Striegel of NIST agrees. “Peter’s personal contributions have been in organizing meetings and bringing together scientists from around the world, and in tirelessly promoting his students,” he said.

Indeed, Schoenmakers has been active in conference organization as a member of the Permanent Scientific Committee of the symposium series on High-Performance Liquid-Phase Separations and Related Techniques (HPLC), including chairing HPLC 2013 in Amsterdam, and as chairman of the organizing committee of the series of International Symposia on the Separation and Characterization of Natural and Synthetic Macromolecules (SCM; Amsterdam, 2003, 2005, 2007, 2009, 2011, 2015, and 2017; Dresden, Germany 2013). He has also chaired or helped to organize multiple other conferences and symposia.

Schoenmakers’s impact at conferences is also notable at the podium, as he sprinkles his discussions with scientific jokes, delivered in a quiet, deadpan fashion and supported by funny graphics. “He presents with a humorous and engaging speaking style,” said Peter W. Carr, a professor at the University of Minnesota. Karger agrees. “Everyone always looks forward to Peter’s talks at international meetings,” he said, “He not only presents exciting results, but keeps the audience entertained with humorous ways of discussing the results.”

Schoenmakers’s service to the scientific community also include being an editor of the 

 since 2003, where he has recruited new authors, increased the quality of published papers, and fostered relationships with reviewers. “As editor of the 

, Peter brought a fundamental understanding of both separation and polymer science to that publication’s stewardship,” Striegel said. Schoenmakers has also been a member of the A page panel (1996–2000) and Editorial Advisory Board (2001–2003) of the American Chemical Society journal 

(1) Blomberg, J.; Schoenmakers, P. J.; Beens, J; Tijssen, R. Comprehensive Two‐Dimensional Gas Chromatography (GC×GC) and its Applicability to the Characterization of Complex (Petrochemical) Mixtures. 

(2) Schoenmakers, P. J.; Billiet, H. A. H.; Tussen, R.; De Galan, L. Gradient Selection in Reversed-Phase Liquid Chromatography. 

(3) Schoenmakers, P. J.; Fitzpatrick, F.; Grothey, R. Predicting the Behaviour of Polydisperse Polymers in Liquid Chromatography Under Isocratic and Gradient Conditions. 

(4) Fitzpatrick, F.; Edam, R.; Schoenmakers, P. J. Application of the Reversed-Phase Liquid Chromatographic Model to Describe the Retention Behaviour of Polydisperse Macromolecules in Gradient and Isocratic Liquid Chromatography. 

(5) Grcev, S.; Schoenmakers, P. J.; Iedema, P. Determination of Molecular Weight and Size Distribution and Branching Characteristics of PVAc by Means of Size Exclusion Chromatography/Multi-Angle Laser Light Scattering (SEC/MALLS). 

(6) Kok, S. J.; Hankemeier, T.; Schoenmakers, P. J. Comprehensive Two-Dimensional Liquid Chromatography with On-line Fourier-Transform-Infrared-Spectroscopy Detection for the Characterization of Copolymers.

(7) Vivó-Truyols, G.; Van der Wal, S.; Schoenmakers, P. J. Comprehensive Study on the Optimization of Online Two-Dimensional Liquid Chromatographic Systems Considering Losses in Theoretical Peak Capacity in First-and Second-Dimensions: A Pareto-Optimality Approach. 

(8) Bedani, F.; Schoenmakers, P. J.; Janssen, H. G. Theories to Support Method Development in Comprehensive Two‐Dimensional Liquid Chromatography–A Review. 

(9) Pirok, B. W.; Pous-Torres, S.; Ortiz-Bolsico, C.; Vivó-Truyols, G.; Schoenmakers, P. J. Program for the Interpretive Optimization of Two-Dimensional Resolution. 

(10) Pirok, B. W.; Gargano, A. F.; Schoenmakers, P. J. Optimizing Separations in Online Comprehensive Two‐Dimensional Liquid Chromatography. 

The Emerging Leader in Chromatography Award

The Emerging Leader in Chromatography Award recognizes the achievements and aspirations of a talented young separation scientist who has made strides early in their career toward the advancement of chromatographic techniques and applications (Table I).

Emanuela Gionfriddo, the 2023 winner, received her PhD in chemistry in 2013 at the University of Calabria, in Italy. Following work as a postdoctoral fellow and research associate at the University of Waterloo, in Canada, in 2018 she became an assistant professor of chemistry at the University of Toledo, in Ohio, where she is the founding member of the Dr. Nina McClelland Laboratory for Water Chemistry and Environmental Analysis.

Gionfriddo received her BSc cum laude in 2008, her MSc cum laude in 2010, and her PhD in 2013—all degrees in chemistry from the University of Calabria in Arcavacata di Rende, Italy. From 2014 to 2017, she was a postdoctoral fellow and laboratory manager in the Industrially Focused Analytical Research Laboratory (InFAReL) of Professor Janusz Pawliszyn’s Research Group in the Department of Chemistry at the University of Waterloo in Ontario, Canada. Later in 2017, she became a research associate while maintaining her laboratory manager role at InFAReL. In 2018, she became an assistant professor at the Department of Chemistry and Biochemistry at The University of Toledo in Ohio, USA.

Gionfriddo’s work focuses on research and development of green microseparation methodologies for extraction of small molecules from complex mixtures, including biological and environmental samples. Her research is important for environmental analysis, for understanding the effects of environmental factors on human health (exposomics), and for metabolomics. Her research group is in active collaboration with several leading universities, as well as corporate research teams in the field of chromatography, including PerkinElmer, Gerstel, Supelco-Millipore Sigma, and Restek Corporation.

With more than 50 publications, Gionfriddo has given 60 oral and poster presentations. She has received several other awards, including a 2022 National Science Foundation CAREER Award, the 2022 Satinder Ahuja Award for Young Investigators in Separation Science from the American Chemical Society’s Analytical Chemistry Division, the 2022 University of Toledo President’s Award for Excellence in Creative and Scholarly Activities, and the UTC Excellence in Sample Prep Award at the North American Chemical Residue Workshop (NACRW) in 2018. She has also received the Young Author Prize at the International Symposium on Advances in Extraction Technologies (ExTech 2018), and best poster awards at the ExTech 2018, 2017, and 2016 conferences. In addition, Gionfriddo is part of the executive committee of the American Chemical Society subdivision for Chromatography and Separation Chemistry, as well as active in advising Ohio statewide authorities.

Gionfriddo’s most cited work underscores her keen attention to the development of solid-phase microextraction (SPME) techniques (11–15), As a side note, Gionfriddo’s father is a fragrance chemist, who has used SPME in his own research. “SPME became her passion since her father made her aware of this technique, and it is still driving her research,” Pawliszyn said. Her most highly cited SPME papers, with recent numbers of citations, appear in Table III.

One important area of research for Gionfriddo is the application of advanced extraction methods for environmental analysis. In one recent study, her research group investigated versatile extraction methods for the untargeted analysis of produced water (PW), which is a contaminated waste byproduct of oil and gas extraction (16). Using their specialized sample extraction methods combined with gas chromatography–mass spectrometry (GC–MS) analysis, they measured over 250 different organic compounds, including dioxane, atrazine, pyridine, and polycyclic aromatic hydrocarbons (PAHs). Elemental analysis was demonstrated for PW by applying dispersive solid-phase extraction along with inductively coupled plasma–mass spectrometry (ICP–MS), confirming the presence of multiple elements including rare earths, and hazardous metals such as Cr, Cd, Pb, and U. When these data were analyzed using partial least squares–discriminant analysis (PLS-DA), the geochemical origin of PW was achieved with a prediction accuracy above 90% indicating a potential method for PW origin tracing—an important ability for holding polluters responsible for environmental contamination.

Gionfriddo and colleagues also have described the details of a thin-film SPME method to assist in the analysis of produced water (PW) (17). The thin-film device they have developed enabled the reproducible analysis of PW without sample pretreatment. This approach was able to characterize approximately 200 compounds from PW samples where GC–MS and data deconvolution were used for analysis.

In another study, SPME was assessed by Gionfriddo as a preconcentration technique to aid the quantitation of per- and polyfluoroalkyl substances (PFAS) in drinking water at ppt levels, below the EPA method 533 established advisory limits (18). Four model PFAS compounds with varying physicochemical properties were evaluated for this feasibility study. Ultrahigh-pressure liquid chromatography–laminar flow tandem mass spectrometry (UHPLC–MS/MS) was also used for compound analysis. This method achieved LOQs of 2.5 ng/L for PFOS and 1 ng/L for GenX, PFBS, and PFOA, with acceptable precision and accuracy over a 5-day period.

Emmons and Gionfriddo also recently published a new method of sample introduction using SPME-Arrow-DART-MS (19). For the first time, an effective strategy was described for hyphenating SPME Arrow to mass spectrometry via a thermal desorption unit (TDU) and direct analysis in real time (DART) source. The developed method was optimized for the extraction and analysis of pesticides and pharmaceuticals from surface water. The method overcomes one of the limitations of ambient MS, especially for onsite analysis applications, which is the irreproducibility of the measurements related to the occurrence of transient microenvironments and variable background interferences.

New Tools for Metabolomics and Exposomics

Peter Schoenmakers and Emanuela Gionfriddo are the winners of the 16th
annual LCGC Lifetime Achievement and Emerging Leader in Chromatography
Awards, respectively.

February 2023 | LCGC North America

What are Options to Improve My Separation? Part II: Likelihood of Separation, Adjusting Selectivity for Simple Mixtures

Using Thermodynamics and Simulations to Understand Selectivity and Retention in Gas Chromatography

Analyzing Organofluorine Compounds in the Environment Using Combustion Ion Chromatography (CIC) and Other Methods

How Well Do You Pipette?

The 2023 Lifetime Achievement and Emerging Leader in Chromatography Awards

Analysis of siRNA with Denaturing and Non-Denaturing Ion-Pair Reversed-Phase Liquid Chromatography Methods