Pittcon is back in-person from 18–22 March 2023, in Philadelphia, Pennsylvania, USA.

The premier conference and exposition on laboratory science, Pittcon provides direct access to the latest research and developments from an international assembly of top scientists and innovators. Advance your knowledge through the technical programme, listen to top researchers by attending award sessions and invited symposia, connect with professionals in networking sessions, and discover new insights on a range of subjects by signing up for in-person short courses.

A sampling of sessions at Pittcon that chromatographers will find interesting includes:

 Lifetime Achievement Award in Chromatography

Green Analytical Liquid Chromatography—“This is the Way”

Application of LC–MS and its Challenges in Drug Discovery and Development

Application of Automation and Machine Learning for Analytical Sciences—Challenges in Pharmaceutical Research and Development

Analytical Challenges and Solutions in the Detection and Quantification of Food Allergens

Advances in Microfluidic Separations for Biological and Pharmaceutical Applications

The Chromatography Forum of the Delaware Valley Dal Nogare Award

Pittcon is honoured to announce Dr. Joanna Aizenberg of Harvard University as the 2023 Wallace H. Coulter Lecturer. A distinct, keynote facet of Pittcon’s annual conference, the 2023 Coulter Lecture is entitled “Venturing into Analytical Chemistry Using Photonic Crystals”. The topic will introduce a powerful colloidal co-assembly approach that results in large-scale, highly ordered porous photonic crystals. This keynote lecture is scheduled to begin at 5:30 PM on Sunday 19 March 2023, in the Michael Nutter Theater at the Pennsylvania Convention Center.

Pittcon’s in-person, instructor-led short courses provide registrants with the opportunity to enhance their professional, technical, and scientific skill sets. Covering an array of relevant topics, these continuing education courses—of which there are over 60—will be offered from 18 March 2023, in full- and half-day formats.

Networking sessions have become an important and valued facet of what Pittcon offers participants year on year, and this year’s sessions promise to be even more inclusive and collaborative than ever before. The 90-min networking sessions provide a unique opportunity for attendees with similar interests to meet other professionals in an informal setting. Here, participants will attend facilitated discussions on scientific topics of interest in a casual, open, and collaborative atmosphere. These sessions facilitate a renewed, more enriched perspective on a variety of scientific disciplines, while also helping many find solutions to laboratory problems they may be facing. Networking sessions are scheduled at Pittcon twice daily—morning and afternoon—20–22 March 2023.

Event location: Pennsylvania Convention Center, Philadelphia, Pennsylvania, USA

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Pittcon is back in-person from 18–22 March 2023, in Philadelphia, Pennsylvania, USA. 

Pittcon 2023

Temperature-responsive liquid chromatography (TRLC) is an emerging green high performance liquid chromatography (HPLC) mode allowing reversed-phase-type separations while necessitating only water as the mobile phase. The columns therein are typically packed with silica particles to which stimuli-responsive polymers are anchored. In hydrophobic interaction TRLC, such polymers depict a loss of water solubility when increasing the temperature above a characteristic conversion temperature, causing large changes in retention over quite narrow and mild temperature ranges (~5–55 

C). TRLC circumvents the concerns about analyte or column degradation that can occur when implementing high temperatures (>80 

C) on conventional reversed-phase columns. It allows for HPLC using only water often spiked with the additives typically used in reversed-phase LC. Therefore, this separation mode allows for greener, cheaper, and isocratic analyses under non-denaturing conditions. The absence of compositional solvent gradients also allows for the exploitation of temperature gradients in combination with refractive index detection. Purely aqueous hydrophobic interaction TRLC is mostly applicable for solutes depicting a 1 < LogP < 5, yet these ranges can be expanded through implementation of combined aqueous or organic mobiles phases, while preserving the temperature-responsive effects. In this first TRLC instalment, our recent developments, new possibilities, and current limitations of the use of 1D TRLC are discussed, while the column performance is described with respect to the fundamentals of HPLC.

Temperature-responsive liquid chromatography (TRLC) is an emerging separation mode in liquid chromatography that makes it possible to obtain reversed‑phase-type separations under purely aqueous conditions. In this mode, retention is controlled through the column temperature while the mobile phase is invariable and composed of only water. This stands in contrast with conventional reversed-phase liquid chromatography (LC) performed on, for example, octadecyl silica (C18), whereby compositional solvent gradients are usually necessary to allow timely elution of disparate molecules in terms of polarity and retention. Therefore, TRLC avoids the need for organic cosolvents in the aqueous mobile phase.

The approach has mainly been based on the exploitation of a particular class of stimuli-responsive (often dubbed “smart”) polymers that lose their water solubility once a certain temperature is exceeded. The temperature at which this transition occurs depends mainly on a lower critical solution temperature (LCST), which is characteristic for each polymer. However, the molecular weight, concentration, and (poly)dispersity also influence the experimental polymer precipitation temperature. In TRLC, the polymer chains are typically anchored to silica particles and packed into stainless steel high performance liquid chromatography (HPLC) columns. The concept was introduced by Kanazawa and associates in the early 1990s, followed by a variety of demonstrations and enhancements, including by our group, in the last two decades (1–8).

Temperature-responsive polymers depict a drastic change in their physical properties with temperature. In general, such behaviour in polymers is not uncommon, providing one can consider any solvent composition. Given that only a limited number of polymers are responsive in water and around physiological temperatures, these polymers have attracted the most attention for applications such as cell culture release and drug delivery, and for their potential in LC (9,10). This phenomenon has mostly been studied in substituted polyacrylamides, with particular emphasis on poly(N-isopropylacrylamide) (PNIPAAm). The utilization of the ensuing stationary phases in LC makes it possible to obtain increased or reduced retention at higher or lower temperatures, respectively, as illustrated in Figure 1. This is caused by the formation of an apolar layer on the surface of the stationary phase owing to the precipitation (demixing) of the polymer from the aqueous mobile phase at higher temperatures. Correspondingly, cooling down the column resolubilizes the polymer chains, concomitantly eliminating their retentive ability. The retention mechanism is based on hydrophobicity and is therefore similar to that of reversed-phase LC. However, it allows operation with only water as the mobile phase, whereby the retention increases drastically with mild increases in temperatures. In this article, the possibilities and limitations of TRLC are discussed with respect to chromatographic fundamentals.

 In reversed-phase LC, one typically observes a decrease in retention when increasing the temperature. Thermodynamically, this means that retention is enthalpically (∆

) unfavourable (11). As chromatographic retention is usually exothermic, the Gibbs free energy equation (∆

) shows that when increasing the temperature, the retention phenomenon becomes less favourable (as ∆

 is becoming less negative at elevated temperatures). This can be represented via a Van ‘t Hoff plot (ln 

 vs 1/T), as shown, for example, for Linuron in Figure 2, whereby it can be seen that indeed the retention is slowly decreasing with increasing temperature on a C8 column (12).

In TRLC, this retention behaviour is drastically altered because the stationary phase itself (and hence mainly ∆

) undergoes a radical, reversible change from a dissolved, highly hydrated random coil to a more condensed globular shape. Thermodynamically, this conversion is entropically driven. The enthalpy contribution of dissolving the polymer in water is exothermic (∆

 <0) because of the formation of favourable hydrogen bonds between the water molecules and the polar groups of the polymer, leading to hydration of the polymer. However, dissolving macromolecules with apolar groups on the backbone and side-chain causes a significant loss in entropy (∆

 <0), as the water molecules are forced into a cage-like orientation around these hydrophobic parts of the polymer (see Figure 1). As the temperature increases, the enthalpy contribution becomes weaker and the entropic loss becomes more pronounced, leading to a positive Gibbs free energy (∆

), which causes the dehydration of the polymer chains. Before the dehydration, the hydrophobic side chains on the polymer are spread over a larger volume providing little retention. After the dehydration, the polymer exposes dense hydrophobic zones, allowing for enlarged retention based on hydrophobicity.

It is interesting to observe that, when the polymer is fully hydrated under cold conditions or fully precipitated in a warm environment, further temperature change beyond these points causes the normal thermodynamic behaviour to take (back) the upper hand. This is also visible through the chromatograms represented in Figure 3 obtained for a number of TRLC columns based on various polymers.

Other Aspects Controlling Retention in TRLC:

 Chromatographically, one aims for retention factors (

) >3 and <10, as this maximizes resolution while avoiding excessive analysis times. As illustrated in Figure 2 and 3, the use of different polymers (on otherwise identical columns) leads to different retention time changes as a function of temperature. When the isopropyl group in the most used PNIPAAm columns is altered for a linear n-propyl chain, the conversion temperature in the resulting PNNPAAm column drops to lower temperatures, while larger retention is obtained for the parabens compared to the first column type. From an HPLC point of view, the ability to perform TRLC at even lower temperatures could become useful because of the related diminished potential issues in terms of analyte stability and silica hydrolysis. When the single propyl groups are replaced by two ethyl groups per monomer, a more strongly retentive column is obtained, which might offer promise for applications where the PNIPAAm columns offer too little retention (such as for biomolecule analyses). A current limitation of TRLC is that, even at the lowest retentive conditions on all TRLC columns, a residual retention is obtained. Research has also been performed based on the implementation of co-polymers, whereby in most cases one of the monomers depicted TRLC behaviour and the other allowed for enhanced hydrophobic or coulombic retention. While copolymers can allow for better biomolecule retention, it also often further increases the residual column retention while reducing the range of retention factors that can be effectively covered through TRLC (1). As the latter is already currently not large in TRLC (see Figure 3[a], where 

 varies from 7 to 23 on a PNIPAAm column for butylparaben), the incorporation of sections in the polymer that do not depict temperature responsive behaviour diminishes the range of retention covered with a column. Conversely, the combination of two or more temperature-responsive polymers in a column might be promising for being able to exploit a broader reachable retention time window. Various studies have also reported the use of cross-linked networks, as compared to linear polymer strands. While promising applications have been shown, this does not seem to offer the prospect of generically applicable columns, as they offer a more limited span of reachable retention factors (9).

Next to temperature and polymer composition, the retention also proves dependent on the grafting density of the polymer brushes immobilized on the silica particles, the molecular weight, the polydispersity, the coupling chemistry, and on surface characteristics of the supporting material. While these aspects can be controlled, an evolution towards narrower polymer dispersity immobilized on a highly controlled surface topology will allow further incremental improvement of TRLC. Most examples of successful TRLC indicate a carbon load on the silica > 15%, whereby polymers depict between 40 and 50 repeating units with as narrow a distribution as possible. While obtaining a shift in retention with temperature is essential in TRLC, the magnitude of this shift can vary from a modest doubling of retention to a sometimes about 10‑fold increase. Ideally, one would strive in TRLC to obtain a similar (extremely broad) ability to tune 

 from 0 to about infinite, as is the case when using different solvent compositions in reversed-phase LC. While this is currently unachievable, the ability to control 

 even over a comparatively narrow order of magnitude already offers significant benefits in terms of gradient peak focusing and analysis time reductions (13,14).

Unsurprisingly, the composition of the mobile phase also plays a strong role in the conversion of the polymer. The addition of salts favours the dehydration of polymers, which lowers the dehydration temperatures, and consequently enhances the column retention at a given temperature (8). A drawback thereof is that in order to obtain a useful influence, high (0.2–2 M) concentrations are required. While volatile salts can also be used, this approach is nevertheless demanding for the instrumentation and columns. Alternatively, organic modifiers also strongly influence the polymer conversion temperature and the overall retention (3,7). While they strongly decrease the retention, the addition of relatively small fractions (5–15%) can be beneficial, as it can remove (or reduce) the residual polymer retention at lower temperatures and offer enhanced efficiencies, as well as elution of highly hydrophobic solutes that cannot be eluted with only water as the mobile phase. Although this approach is contrary to the green incentive to develop TRLC, it might offer promise when combined with biosourced or biodegradable solvents.

In general, polymer-based columns are often perceived to offer only a limited column efficiency as compared to silica-based columns (15). Because, in TRLC, rather short polymer strands are immobilized on silica, similar column efficiencies and plate heights can be expected to be reachable as obtained with reversed-phase LC columns. The best reported performances (for well retained solutes) in TRLC offer column efficiencies that are only 33% below the optimal performance reachable on C18 columns. Correspondingly, the minimal reduced plate heights (h = 

) are close to three (instead of two as obtained on a column packed with fully porous C18 particles). The van Deemter plots obtained for the three TRLC columns are represented (Figure 4[a]) as collected under the most retentive conditions (55 °C), and compared with an earlier TRLC study (described in Figure 4[b]) where a different (free instead of a controlled radical polymerization) synthetic route was followed.

While this illustrates that indeed reasonable and improvable efficiencies can be obtained in TRLC, it can also be seen that H/u plots are characterized by a rather steep C-term regime. This is attributed to the resistance to mass transfer in the stationary phase (Cs), which is inevitably larger in a bulky polymer layer as compared to a thin layer of C18 groups. As a consequence, the optimal linear velocity is found at about 3–4 times slower flow rates compared to conventional reversed-phase LC. Hence, to reach optimal chromatography in TRLC, longer analysis times are still currently a necessity. Without a doubt, TRLC can become more efficient through the use of smaller fully or partially porous particles. The main hurdle therein has been the current practical unviability of sub-2-µm silica-based supporting materials that offer a suitable target functional group to which the polymers can be covalently anchored. In most TRLC work, the polymers are synthesized such as to contain a carboxylic end group, which can then be attached to aminopropyl silica via various amide bond forming chemistries (2,4,6,8).

On the other hand, when TRLC is performed under the poorly retentive low temperature conditions, the polymer layer is extended, and therefore more voluminous. This detrimentally affects the mass transfer (as shown in Figure 4[b] at 25 °C for PNIPAAm) and the corresponding efficiencies obtained under those conditions. However, this is unproblematic in terms of the applications, as in most cases a downwards temperature gradient is used, causing the peaks to refocus (as explained later in the section “Gradient Analyses”).

The selectivity in TRLC is altered through temperature, polymer composition, and additives. While at lower temperatures some enhanced retention of polar solutes can be observed, this has not led to promising applications. This is, in part, related to the aforementioned poor efficiencies obtained at low temperatures. At temperatures above the LCST, the retention is mainly based on hydrophobicity. When altering the polymer composition, different selectivities are observed, even for the three conceptually similar polyacrylamide polymers. This is illustrated in Figure 5, where changes in elution order are apparent for the analysis of steroids and barbiturates. The different selectivities can also be combined via a stationary‑phase optimized selectivity LC-type of approach to deliver an optimized separation (16,17). TRLC also proves able to separate closely related diastereomers, as illustrated for a number of peptides in Figure 5(c) (6).

Hydrophobic interaction TRLC is mainly operated at temperatures above the LCST, allowing exploitation of the then-obtained higher retention, while benefiting of reasonable plate numbers and selectivity reachable under those conditions. The main benefits of TRLC can be reaped when this is combined with a downward temperature gradient imposed on the columns. Although the column temperature can also be programmed in conventional static air ovens, their thermal mass hinders imposing rapid cooling gradients. Programmable water baths are mostly used to control the column temperature in TRLC, as they allow accurate temperature control. Recently, we introduced a column cooling approach, based on the controlled mixing of a hot and cold mobile phase as reachable through any binary HPLC pump, allowing for faster gradients as compared to the use of programmable water baths (13). The ensuing drastic reduction in retention time and obtained gradient peak focusing are illustrated in Figure 6.

The use of gradients also allows for novel possibilities in the field of quantitative analysis. While the combination of compositional solvent gradients with refractive index (RI) detection is prohibitive, the use of the thermal gradients required in TRLC proves not to influence the noise, drift, or response of this detector. A quantitative application is shown for a series of short chain fatty acids determined under the steepest gradient conditions (45 to 5 °C at –1°/min). The overlapping calibration lines illustrate that standard independent quantitation now becomes possible, while the applicability range of the RI detector is extended (Figure 7).

TRLC is an alternative separation mode that makes it possible to obtain separations similar to those of reversed‑phase LC while using only water as the mobile phase. Although poly(N-isopropylacrylamide) (PNIPAAm) is still the most commonly used polymer in TRLC, the possibilities offered by other temperature-responsive acrylamides is appealing, and potentially more interesting, because they offer more retention or dehydrate at lower temperatures. The efficiency of the columns obtained via such polymers immobilized on silica particles appears not fundamentally hindered and an evolution towards more efficient columns appears realistic. Nevertheless, the bulky nature of a polymer layer on silica slows down mass transfer and the resulting optimal velocities. The selectivity of TRLC columns differs between the polymers and at different temperatures. TRLC is most useful when it is used under thermal gradient conditions that allow for peak focusing and reduced analysis times. It could also be shown that the use of temperature gradients in TRLC is compatible with RI detection, allowing for novel applications via this fairly universal detector.

Kanazawa, H.; Yamamoto, K.; Kashiwase, Y.; et al. Analysis of Peptides and Proteins by Temperature-Responsive Chromatographic System Using N-Isopropylacrylamide Polymer-Modified Columns. 

 1545–1550. DOI: 10.1016/S0731-7085(96)02004-3

Kanazawa, H.; Yamamoto, K.; Matsushima, Y.; et al. Temperature-Responsive Chromatography Using Poly(N-Isopropylacrylamide)-Modified Silica. 

Ampe, A.; Wicht, K.; Baert, M.; et al. Investigation of the Potential of Mixed Solvent Mobile Phases in Temperature-Responsive Liquid Chromatography (TRLC). 

Baert, M.; Wicht, K.; Hou, Z. Y.; et al. Exploration of the Selectivity and Retention Behavior of Alternative Polyacrylamides in Temperature Responsive Liquid Chromatography. 

, 9815–9822. DOI: 10.1021/acs.analchem.0c01321

Baert, M.; Martens, S.; Desmet, G.; et al. Enhancing the Possibilities of Comprehensive Two-Dimensional Liquid Chromatography through Hyphenation of Purely Aqueous Temperature-Responsive and Reversed-Phase Liquid Chromatography. 

, 4961–4967. DOI: 10.1021/acs.analchem.7b04914

Satti, A. J.; Espeel, P.; Martens, S.; et al. Tunable Temperature Responsive Liquid Chromatography Through Thiolactone-Based Immobilization of Poly(N-Isopropylacrylamide). 

, 126–132. DOI: 10.1016/j.chroma.2015.11.063

Miserez, B.; Lynen, F.; Wright, A.; Euerby, M.; Sandra, P. Thermoresponsive Poly(N-vinylcaprolactam) as Stationary Phase for Aqueous and Green Liquid Chromatography. 

Lynen, F.; Heijl, J. M. D.; Du Prez, F. E.; et al. Evaluation of the Temperature Responsive Stationary Phase Poly(N-isopropylacrylamide) in Aqueous LC for the Analysis of Small Molecules. 

, 143–150. DOI: 10.1365/s10337-007-0301-z

Lorenzo, R. A.; Carro, A. M.; Concheiro, A.; Alvarez-Lorenzo, C.; Stimuli-Responsive Materials in Analytical Separation. 

, 4927–4948. DOI: 10.1007/s00216-015-8679-1

Aseyev, V.; Tenhu, H.; Winnik, M. Non-Ionic Thermoresponsive Polymers in Water. In 

Self Organized Nanostructures of Amphiphilic Block Copolymers II

, Muller, A. H. E., Borisov, O., Eds.; Springer, 2011; pp. 29–89.

Linford, M.; Jensen, D. S.; Clark, J.; Teutenberg, T. Elevated Temperatures in Liquid Chromatography, Part III: A Closer Look at the van ‘t Hoff Equation. 

Baert, M.; Wicht, K.; Moussa, A.; et al. Implementations of Temperature Gradients in Temperature-Responsive Liquid Chromatography. 

, 1–11. DOI: 10.1016/j.chroma.2021.462425

Bandini, E.; Wicht, K.; Ampe, A.; et al. Hyphenating Temperature Gradient Elution with Refractive Index Detection Through Temperature-Responsive Liquid Chromatography. 

Vanhoenacker, G.; Pereira, A. D.; Kotsuka, T.; et al. Evaluation of a New Polymeric Stationary Phase with Reversed-Phase Properties for High Temperature Liquid Chromatography. 

, 3217–3222. DOI: 10.1016/j.chroma.2009.09.070

De Beer, M.; Lynen, F.; Chen, K.; et al. Stationary-Phase Optimized Selectivity Liquid Chromatography: Development of a Linear Gradient Prediction Algorithm. 

Lynen, F.; Hegade, R.; De Beer, M.; et al. Stationary-Phase Optimized Selectivity in Liquid Chromatography (SOS-LC) for Pharmaceutical Analysis. 

 is a research fellow in Research and Development at Restek.

 is a professor at Ghent University, Ghent, Belgium, and director of the Separation Science Group.

 is a PhD researcher at Ghent University.

completed his PhD research at Ghent University.

 completed her PhD research at Ghent University.

Column Watch

In this first TRLC instalment, our recent developments, new possibilities, and current limitations of the use of 1D TRLC are discussed, while the column performance is described with respect to the fundamentals of HPLC. 

Perspectives in Hydrophobic Interaction Temperature-Responsive Liquid Chromatography

Retention and selectivity in gas chromatography (GC) occur because of the thermodynamics of the phase transition from the vapour phase following sample injection to the solution phase of being dissolved in the stationary phase. Analytes then move along the column because the vapour phase is flowing. In this instalment, 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 

 and the phase ratio, β, 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, 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 indices and Snow extended it further to multi-ramp temperature programs (4–7). Thermodynamic retention indices 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 indices, 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 instalment (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 (C

) is eluted in approximately 26 min, whereas on PDMS undecane (C

) 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 1 shows retention time data 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, favouring 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, favouring 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 nonpolar or moderately polar, but is polarizable, leading to stronger interactions with the polar PEG stationary phase and greater retention.

Pyridine is the 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 favouring the stationary phase, but PEG favouring 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 3[a]) and PEG (Figure 3[b]), 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 indices described originally by Dose in 1987 and used in ProEZGC and other programs to predict retention times.

In Figure 3(a), 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 3(a), is also seen in Table 1, with the retention factor of benzene 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 3(b), 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 1, we see that the selectivity for benzene and ethanol on PEG is approximately 1.1, much lower than on PDMS. From Table 1, 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 4(a) shows a simulated separation of benzene and ethanol on a PEG stationary phase at 40 °C and Figure 4(b) 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 and 1.05 at 120 °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 favour 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, benzene is eluted first and at 120 °C ethanol is eluted first. This can be explained by the difference in slope of the two lines in Figure 4(b).

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.

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

Vitha, M. F. Short Gas Chromatography Experiment That Richly Connects Thermodynamics to the Intermolecular Interactions That Govern Solute Retention. 

 (5), 1923–1930. DOI: 10.1021/acs.jchemed.2c00039.

McNair, H. M.; Miller, J. M.; Snow, N. H. 

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

 (19), 2414–2419. DOI: 10.1021/ac00146a020

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

 (9), 607–610. DOI: 10.1002/jhrc.1240080926

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

 (9), 611–617. DOI: 10.1002/jhrc.1240080927

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

 (7), 271–275. DOI: 10.1093/chromsci/30.7.271

, https://ez.restek.com/proezgc (Accessed January 2023).

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

 is the Founding Endowed Professor in the Department of Chemistry and Biochemistry at Seton Hall University, USA, and an adjunct professor of medical science. Direct correspondence to: amatheson@mjhlifesciences.com

GC Connections

In this instalment, we review the basic thermodynamics underlying separation in GC, and we see how this impacts selectivity and retention.


Using Thermodynamics and Simulations to Understand Selectivity and Retention in GC 

In this latest instalment of our series on recurring challenges in liquid chromatography (LC), we turn our attention to peak widths that are wider than expected. Estimating the expected peak widths can help determine when a column and LC system are not working quite right, and to decide when troubleshooting should be initiated.

Writing this “LC Troubleshooting” column and thinking about topics each month is interesting in the sense that there are some topics that just never get old. Whereas in the chromatography research world certain topics or ideas become obsolete as they are displaced by newer and better ideas, in the troubleshooting world there are certain topics that have remained relevant since the very first troubleshooting article appeared in this magazine (

 at that time) in 1983 (1). Over the last few years I’ve focused several “LC Troubleshooting” instalments on contemporary trends (such as the relatively recent advances in our understanding of the effects of pressure on retention [2]) in liquid chromatography (LC) that are affecting the way we approach our interpretation of LC results, and approach troubleshooting with modern LC instruments. With this month’s instalment, I am continuing a series I started in January 2022 (3) that is focused on some of the “bread and butter” topics of LC troubleshooting—those elements that are essential for any troubleshooter, no matter the vintage of the system we are working with. The topics at the heart of this series are highly related to 

’s well-known LC Troubleshooting wall chart (4) that hangs in many laboratories.

For the sixth instalment in this series, I’ve chosen to continue the discussion I started in September 2022 focused on problems related to peak shape and characteristics (5). Incredibly, the wall chart lists 44 different potential causes of bad peak shapes! In the aforementioned discussion, I dealt with what I would say are gross problems with peak shape—badly fronting or tailing peaks, “fuzzy” peaks, negative peaks, or even no peak at all. In this instalment, I will focus on the more subtle issue of how wide we should expect peaks to be when they appear to be relatively normal in all other respects. I hope LC users young and old will find some useful tips and reminders related to this important topic.

More and more I find myself responding to troubleshooting questions with “everything is possible”. This might seem like an easy out when considering observations that are hard to explain, but I find that this response is appropriate more often than not. While modern LC columns are incredibly powerful, and are the heart of any separation, they are also prone to sudden, unexpected, and sometimes spectacular failure. It may be unfair to the columns to always blame them first when things go wrong, but if they are not at the top of our list troubleshooting targets, they should be near the top of things to consider when the peaks in a chromatogram don’t look quite right.

A critical step in any troubleshooting exercise, but one that I think is underappreciated, is recognizing that there is a problem to be solved. Recognizing that there is a problem usually amounts to recognizing that what is happening with the instrument is different from what is expected to happen, and our expectations are formed from theories, empirical knowledge, and experience (6). When we speak of “peak shape”, we are really referring not only to the shape of the peak (including, but not limited to, symmetric, asymmetric, smooth, shaggy, fronting, and tailing) but also to the width. Our expectations regarding the actual peak shape are straightforward. The textbook expectation, which is well supported by theory (7), is that, under most conditions, chromatographic peaks should be symmetric and consistent with the shape of a Gaussian distribution. Our expectations for the peak width are a more complicated matter, and they are the focus of this instalment. Peak width in any chromatogram is dependent on many factors, including the size and other physicochemical characteristics of the analyte; the mobile phase flow rate, temperature, and viscosity; and the particle size, pore structure, and chemistry associated with the stationary phase, as well as others. Understanding how these factors relate to peak widths is powerful because this allows us to estimate the widths we should observe. If what we observe does not match our expectation, or if we observe changes in peak widths over time, then there is reason to suspect some kind of problem with the column or system, giving us reason to think about starting to troubleshoot.

The following discussion assumes that we are considering reversed-phase LC, and primarily separations of small molecules. Similar relationships exist for other modes of separation (for example, hydrophilic interaction LC, or ion-exchange LC), but we restrict the scope of this particular discussion to reversed‑phase LC to make it manageable for a single instalment. I also point out here that sophisticated simulators for reversed-phase LC are freely available through the internet. One maintained by my research group is available as an application that runs in a web browser (https://www.multidlc.org/hplcsim), and is now available in several languages; one developed by Davy Guillarme’s group is available as a downloadable Excel-based application (https://ispso.unige.ch/labs/fanal/practical_hplc_simulator:en). These tools take care of all the maths for you, and they allow quick and easy manipulation of many of the variables discussed below. The discussion here is intended to help users understand the essential relationships that form the basis of the results we see with these simulators.

 The simplest place to begin a discussion on peak width is to consider isocratic conditions—conditions where the mobile phase composition is not deliberately changed during the analysis. In this case, in approximate terms, the more time the analyte spends in the column, the wider its peak is at the detector. One way of expressing this is shown in equation 1, where 

 is the 4∙σ width of the peak (drawn at about 13.4% of the peak height); 

 is the plate number (chromatographic efficiency); 

 is the isocratic retention factor of the analyte for a given mobile phase composition:

Estimation of the column dead time is straightforward, and is nominally constant for a given column at a given flow rate. What is more interesting is to consider the effects of 

 on the width, and how these relate to the other parameters listed above (for example, analyte size). Estimating the plate number requires some knowledge about the extent to which peaks broaden when the flow rate changes. We start with what we refer to as the reduced form of the van Deemter equation, which relates the plate height to the mobile phase velocity (equation 2).

 is the reduced interstitial mobile phase velocity, and 

 is the reduced plate height. The LC simulators referred to earlier will calculate the reduced velocity when given inputs of column dimensions, flow rate, particle size, and an analyte diffusion coefficient. The important thing to recognize here is that this velocity is dependent on analyte size, as the rate at which the analyte diffuses in the mobile phase affects the reduced velocity; generally speaking, at a given mobile phase flow rate, the reduced velocity increases with the increasing molecular weight of the analyte. One of the advantages of using the reduced form of the van Deemter equation (equation 2) is that the coefficients A, B, and C are nominally independent of factors like particle size. In our work, we typically use values of 1.0, 5.0, and 0.05 for A, B, and C. Of course, these values are not truly constant, but they are good numbers to use to get an approximate value of the plate number under a given set of conditions. Readers interested in learning more about details related to these parameters are referred to several recent articles (8–10). After calculating the reduced plate height, 

, we can calculate the non-reduced plate height 

, and finally the plate number using equations 3 and 4, where 

Figure 1 shows simulated chromatograms calculated using these steps for different combinations of particle size, flow rate, and column length. The main point of this comparison is that the trend in relative peak widths within a single chromatogram is primarily determined by retention—that is, within a given chromatogram, a peak eluting with a retention factor of 10 is approximately 10 times wider (and thus 10 times shorter) than a peak eluting with a retention factor of 0 (that is, at the dead time). This ratio is the same no matter the choices of particle size, column length, and flow rate. When these variables are changed the absolute values of the peak widths change, but the trend in relative widths does not change. This is a hallmark of isocratic elution—that we should expect more retained peaks to be wider than less retained peaks, and the degree of the change in widths is at least approximately predictable using simple tools.

 When working with gradient elution conditions we have an expression for peak width that is similar to equation 1, with two important differences. As shown in equation 5, we replace 

 as the local retention factor of the analyte in the mobile phase it experiences, as it is exiting the column.

, which accounts for the fact that when an analyte is eluted in a gradient, the rear of the peak (pump side) moves at a slightly higher velocity than the front of the peak (detector side), causing the peak to narrow slightly. We are not going to spend time discussing this factor here because under most practical conditions, it does not affect the peak width by more than approximately 10%. Readers interested in learning more about this are referred to Snyder and Dolan’s book on gradient elution LC (11). Relatively speaking, the influence of 

 on peak width in gradient elution separations is much more important than 

 is strongly affected by the gradient slope (

), which quantifies the rate of change of the mobile phase composition during the gradient. This relationship is shown in equation 6, where ∆

 is the change in the volume fraction of organic solvent in the mobile phase (0–1 scale for reversed-phase LC), and 

Generally speaking, larger gradient slopes lead to smaller 

 values, and thus narrower peaks. The gradient slope increases with ∆

 or the dead time, and decreases as the gradient time is increased. The other parameter in equation 6, 

, is related to the sensitivity of the retention of a given analyte to the mobile phase composition. The simplest way to understand this parameter is that it is the negative of the slope of a plot of the natural logarithm of retention factor (

) of the analyte as a function of the mobile phase composition (0–1 scale). This particular relationship is an outcome of what has become known as 

 in equation 6, we see that the gradient slope that impacts the value of 

 can be very different for two analytes with different 

 values, even if all the other parameters (∆

) are exactly the same. I think this is counterintuitive for those new to gradient elution, and can lead to some real surprises when thinking about what to expect for peak widths under different conditions, and for different analytes. If the value of 

 is the retention factor of the analyte in the mobile phase used at the starting point in the gradient elution program.

A representative calculation will illustrate how dramatically the gradient elution conditions can affect the peak width. The values of 

 for the small molecule acetophenone are 3.82 and 6.35, respectively (C18 column, acetonitrile–water mobile phase, at 40 °C; see https://www.multidlc.org/hplcsim for more values like this for other molecules). Figure 2 shows that the magnitude of 

 changes by about a factor of 10 as the gradient time is increased from 1.4 to 18 min. This, in turn, results in an increase in the peak width (as expected from equation 5), but only by about a factor of five. Figure 2 also shows that the simple ratio 1/

 can vary dramatically depending on the gradient conditions and thus result in very different peak widths. However, this is not always the case. Figure 3 shows a simulated chromatogram for a mixture of alkylphenone homologs ranging from acetophenone to octanophenone. In this case, the peak widths are similar for all of the compounds, even though they are eluted at very different points in the gradient. This is because the 

 values are very similar for all of the compounds, covering the narrow range of 3.8 to 5.2 (compared to the range of 0.7 to 7.3 in Figure 2).

The larger size of molecules such as organic polymers and biopolymers (including proteins and oligonucleotides) compared to the small molecules discussed here has two main implications for the estimation of peak width discussed in this instalment. First, they diffuse much slower in solution, which generally leads to lower plate numbers. Second, their retention is generally much more strongly dependent on mobile phase composition (that is, larger 

 values, sometimes exceeding 100). This, in turn, results in low 

 values (<2) for these molecules under a wide range of elution conditions, which provides the potential for narrow peaks. These two effects—slower diffusion leading to low plate numbers, and larger 

 values—offset each other in many situations. We will take up a more detailed discussion of these effects in a future instalment of “LC Troubleshooting”.

When the Instrument Adds to the Peak Width

In 2021, Ken Broeckhoven and I wrote a series of four “LC Troubleshooting” articles focused on providing an update on the factors that affect broadening of peaks outside of the chromatography column—so-called “extracolumn broadening” or “extracolumn dispersion” (ECD) (12–15). Our aim was to provide a means to quantitatively estimate the contributions of different parts of a LC system (that is, injector, connecting tubing, and so on) to the total widths of peaks observed at the detector. Through a series of examples, we showed how the apparent performance of a high quality column can be severely compromised—not due to actual poor performance of the column itself, but due to the broadening of peaks in the instrument components. Readers interested in the details are referred to this series of articles, and a freely available, web‑based calculator that can help with the maths involved (https://www.multidlc.org/dispersion_calculator/). These issues are particularly relevant to the discussion here about estimating peak widths, especially for gradient elution separations or early‑eluted peaks under isocratic conditions, because the low 

 values can lead to very narrow, low volume peaks that are highly susceptible to extracolumn dispersion.

What To Do When the Peak Widths Are Not As Expected?

In my laboratory, we consider the following possibilities when we suspect that a column is producing peaks that are wider than expected.


As shown in equations 1 and 5 above, the peak widths we can expect under both isocratic and gradient elution conditions are dependent on the plate number of the column, which can be measured under isocratic conditions. We do these checks with simple, small, neutral molecules to avoid poor plate numbers that can arise from deleterious interactions between analytes and the stationary phase. The measured isocratic efficiencies should be similar to those reported by the manufacturer on their quality control (QC) sheet. If not, then either something is wrong with the column, or something is wrong with the instrument used to make the measurement.

Consider the possibility that wide peaks are due to chemical problems.

 
Analyte-specific problems, such as slow desorption kinetics and mass overload, can lead to peak tailing and wider-than-expected peaks. If one observes that some peaks in a chromatogram are much wider than others, this can be an important clue that something is not going well in a chemical sense. Readers interested in these issues are referred to a recent “LC Troubleshooting” article on this topic (16).

Check to make sure that the system is not the problem.

 
As discussed above, peak dispersion in LC components other than the column can make the performance of a column look much worse than it actually is. A quick way to check for this possibility is to move the column in question to a different system that is known to have low extracolumn dispersion. If the column produces the expected peak widths in this second system, then this is a clue that the wide peaks observed with the first system are not due to the column, but rather the instrument itself.

In this sixth instalment on essential topics in LC troubleshooting, I have discussed the essential relationships that can be used to estimate expected peak widths for a given set of chromatographic conditions, including both isocratic and gradient elution. By using these estimated peak widths, one can detect when a column or system is not producing peak widths as expected, and initiate the troubleshooting process to determine why the peaks are wider than expected. This discussion has been focused on peak width estimates for small molecules (that is, less than 500 Da); a further discussion of details related to separations of larger molecules including biopolymers will be handled in a future instalment of “LC Troubleshooting”.

Kempen, T.; Stoll, D. R. Retention Factor is Independent of Pressure in Liquid Chromatography, Right? 

Stoll, D. R. Essentials of LC Troubleshooting, Part 1: Pressure Problems. 

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