Tag: Leon Niezen

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Recycling Liquid Chromatography for Polymer Analysis

Recycling LC for Analysis of Chemical Composition Distribution

Synthetic polymers play an important role in our current society and see use in an incredibly diverse number of materials. Examples include polyurethane foam cushions or the use of aramid in optical fiber cables and jet engine enclosures. To develop better materials without resorting to a “trial-and-error” approach of synthesizing new materials, we must know how a polymer’s molecular structures influence the properties of the material.

The first step in this process is the ability to analyze these molecular structures. We are not usually able to separate all of the individual structures, since polymers typically show dispersity in molecular weight and potentially in chemical composition. These are referred to as the molecular weight distribution (MWD) and chemical composition distribution (CCD), respectively.

Figure 1. A) Schematic illustration of the recycling-gradient set-up, B) Trace from the in-line DAD resulting from the recycling gradient with the switching moments of the valve indicated by the dotted lines, C) Data folded and aligned, displayed as stacked individual cycles (left) or as a surface plot (right). Reproduced with permission from [1].

When a synthetic polymer features both a broad MWD and a low average molecular weight (a type of polymer that is extremely common) it can be challenging to analyze the CCD since the most common method to do so is gradient-elution LC. Using this method the individual analytes elute based on their polarity, which is affected by both their composition and their molecular weight. As a result we typically measure a convoluted distribution.

Applying the theory of retention modelling to standards that feature a narrow MWD and a known molecular weight allows us to determine, approximately, what conditions are required to remove or mitigate the effect of one distribution. As it turns out effectively steep gradients are required to enhance the separation on chemical composition.

Figure 2. LCΠLC of copolymer MB4 using non-porous C18 particles with a 3-min 0-60% THF gradient in ACN. A) Front (blue) and tail (red) peak widths (in mL) as function of cycle number. B and C) Peak profiles after 1st and 20th cycle, respectively, with fractions taken indicated; dashed line under the peak indicates the background signal of the gradient. D and E) SEC chromatograms of the fractions indicated in B and C. Reproduced with permission from [1].

This has led to the development of a technique called recycling liquid chromatography, where the gradient is continuously recycled between two columns. Such a recycling allows analytes a longer time to travel through the gradient. Each successive cycle therefore increases the amount of the gradient the analytes get to experience before eluting from the column; i.e. the effective gradient steepness increases every cycle.  

With the addition of an in-line detector this allows us to assess directly how analytes travel through the gradient within a single experiment. The technique was shown to allow for a significantly better assessment of the CCD, unhampered by the underlying MWD, for several different types of styrene and acrylate-based copolymers.    

All of the conclusions are detailed in the open-access article that was recently published in Journal of Chromatography A. This means you can download and read it for free.

References

[1] Recycling gradient-elution liquid chromatography for the analysis of chemical-composition distributions of polymers, L.E. Niezen, B.B.P. Staal, C. Lang, H.J.A. Philipsen, B.W.J. Pirok, G.W. Somsen, P.J. Schoenmakers, J. Chromatogr. A, 2022, 463386, DOI: 10.1016/j.chroma.2022.463386

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Reducing Effect of Gradient Deformation For LC Retention Modelling

Retention modelling is a useful technique which can be used to substantially reduce the method-development process for LC separations. One approach utilizes so-called scanning (or ‘scouting’) experiments using isocratic or gradient elution [1]. Here, a number of pre-defined methods are employed to record retention times to which empirical models are fitted. 

Isocratic experiments will generally yield reliable solid datasets that are very suitable for retention modelling. Using isocratic elution is, however, not always very practical. Indeed, scouting experiments can take rather long for the slower experiments. Moreover, some manual fine-tuning and experience with the analytes in question are needed to identify the appropriate modifier concentrations.

In contrast, gradient elution allows rather quick and easy scanning experiments at the significant cost of the usefulness of the resulting data. Where isocratic experiments directly measure the retention factor at a certain modifier (φ) fraction, the retention time in gradient elution depends on the gradient experienced by the analyte.

image_2021-01-02_083104

Figure 1. Schematic illustrating a programmed linear gradient and the experienced gradients for two different systems.

However, as the programmed change in composition produced by the pump migrates through the chromatographic system, its shape is altered. In Figure 1, above, we can see how this leads to the familiar difference between the programmed (dark blue) and effective (purple, light blue) gradients for two different systems.

This deviation is the product of an array of effects, such as the morphology and inefficiencies in the pump components, chromatographic system volumes and the accuracy of pumped mobile-phase composition (A vs. B). The latter can rather easy deviate if the pump does not take into account the change in density as φ increases.

Figure 2. Response functions of systems 1 and 2. The shape essentially represents the differences between the programmed and effective gradients shown in Figure 1.

The overall effect can be represented by response functions. These functions essentially describe the difference between the programmed and measured gradient. Two examples for two different systems are shown above in Figure 2. Indeed, depending on the pump characteristics, dramatic changes can be observed.

 

The problem is only complicated further as the recorded dwell curve may also in itself represent an inaccurate depiction. Depending on the detector, solvatochromic effects and  the presence of other mobile-phase components can severely convolute the true depicted of the experienced gradient.

For modelling, deformation is a problem because

  • Published retention parameters include the system effects, rendering the values useless to other users without detailed knowledge of their chromatographic system.
  • Optimized methods cannot be directly translated to other systems.
  • The usefulness of gradients to obtain scanning experiments is severely weakened.

As part of a larger collaboration with Agilent Technologies in the “DAS PRETSEL” project, Tijmen Bos, with assistance of other CAST members Mimi den Uijl, Leon Niezen and Stef Molenaar, developed an algorithm to reduce partially the effects of gradient deformation.

In their work, Bos et al. showed that the impact of the gradient deformation significantly impacts retention parameters. By modelling so-called Stable distribution functions to the measured dwell curves, the authors were able to significantly reduce the prediction errors for water-water systems (Figure 3). Conveniently, the Stable parameters turned out to be related to physical parameters of the chromatographic system.

Figure 3. Relative errors (%) in the predicted retention times of the test compounds on Instruments 2 (top) and 3 (bottom) obtained when using retention parameters determined for the test compounds on Instrument 1 at different flow rates. Please see the publication for details about the instruments. Reproduced with permission from [2].

This work is part of a larger project. In this first stage, we mainly targeted the geometric-influences. Now, we shift our focus to more complicated solvent systems and also the effect on larger molecular systems.

The work was recently published open-access in Journal of Chromatography A and can be downloaded for free here. An accompanying video pitch can be viewed below. Readers interested in learning more about retention modelling and its application areas are referred elsewhere. 

References

[1] Recent applications of retention modelling in liquid chromatography, M.J. den Uijl,  P.J. Schoenmakers,  B.W.J. Pirok, and  M.R. van Bommel, J. Sep. Sci., 2020, DOI: 10.1002/jssc.202000905.

[2] Reducing the influence of geometry-induced gradient deformation in liquid chromatographic retention modelling, T.S. Bos, L.E. Niezen, M.J. den Uijl, S.R.A. Molenaar, S. Lege, P.J. Schoenmakers, G.W. Somsen, B.W.J. Pirok, J. Chromatogr. A, 2021, 1635, 461714, DOI: 10.1016/j.chroma.2020.461714.

The Authors

Tijmen Bos

Mimi den Uijl

Leon Niezen

Stef Molenaar

Researchers Bos, Niezen and Molenaar were part of the UNMATCHED project, which was supported by BASF, DSM and Nouryon, and received funding from the Dutch Research Council (NWO). Den Uijl was part of the TooCOLD project, which was supported by Unilever and NWO. You can read more about them and find their contact info on the Team page.