Day: 6 August 2022

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Publications

Characterisation of Smokeless Powders in Explosives by 2D-LC

Analysis of Smokeless Powders by 2D-LC

Smokeless powders are explosives that are typically used as propellant for ammunition but are also encountered in improvised explosive devices such as pipe bomb. This is particularly true for the United States where smokeless powders are readily available [1]. While residue analysis is possible post-explosion, forensic experts often also encounter explosive devices pre-explosion. In such an explosion, investigators must establish a chemical profile of the propellant and compare it to the energetic material found elsewhere such as a suspect’s home.

Scientist Rick van den Hurk from the Chemometrics and Advanced Separations Team (CAST) at the van ‘t Hoff Institute for Molecular Sciences (HIMS) of the University of Amsterdam developed a method to simultaneously characterize smokeless powders both on molecular weight distribution and additive profile. For the method, Van den Hurk employed two-dimensional liquid chromatography (Figure 1). Combining organic size-exclusion chromatography and aqueous reversed-phase liquid chromatography, Van den Hurk designed a two-stage trapping system to improve compatibility and enhance sensitivity.

Figure 1. Schematic overview of the heart-cut SEC-RPLC setup used for characterizing smokeless powders. Reproduced with permission from [2].

The size-exclusion chromatography (SEC) is conducted as first-dimension separation and resolves the smokeless powders based on size. Larger fragments will elute earlier than smaller fragments. An example is shown in Figure 2 on the left-hand side, with a comparison of two samples and their confidence intervals for the entire range.

Next, the additives, which are among the smallest molecules relative to the very large nitrocellulose molecules, are gathered by the trapping interface and injected into a second-dimension reversed-phase LC (RPLC) separation. An example of the additive package for three different smokeless powders is shown in Figure 2.

Figure 2. Examples of feature analysis of smokeless powders. Left: Molecular weight distribution by size-exclusion chromatography of two forensic samples labelled as 315 and 395. Right: Additive analysis of smokeless powders by reversed-phase LC. The different colors indicate different forensic samples. It can be seen that for the additives there is a significant difference between the fingerprint of each sample. Reproduced with permission from [2].

The project included contributions of students Anouk van Beurden and Mabel Dekker who co-developed the method within their internship project. Dekker examined the discrimination power of the SEC method, whereas van Beurden focused on the RPLC separation. Van Beurden also extensively investigated alternative methods to characterize the nitrocellulose in detail including the use of enzymatic degradation.    

The project was conducted in collaboration with Annemieke Hulsbergen-van den Berg from the Dutch Forensics Institute (NFI), and investigated under the lead of Prof. Arian van Asten and Dr. Bob Pirok. Van Asten and Pirok together lead the PARADISE project along with Prof. Govert Somsen at the Vrije Universiteit of Amsterdam. The PARADISE project is a public-private funded collaboration between a consortium of industrial and academic partners. The project was funded by consortium members Covestro, DSM, Genentech, Shell, the Dutch Forensics Institute and the Dutch Science Council (NWO) and aims to advance the analysis of complex mixtures encountered in industry and society.

The work was published open-access in Journal of Chromatography A and can be freely downloaded here.

References

[1] K.D. Smith, B.R. McCord, W.A. MacCrehan, K. Mount, W.F. Rowe, J. Forensic Sci. 44, 1999, 14554J, DOI: 10.1520/jfs14554j

[2] R.S. van den Hurk, N. Abdulhussain, A.S.A. van Beurden, B.E. Dekker, A. Hulsbergen, R.A.H. Peters, B.W.J. Pirok, A.C. van Asten, J. Chromatogr. A, 1672, 2022, 463072, DOI: 10.1016/j.chroma.2022.463072

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Publications

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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Funding

VENI Grant for Bob Pirok

UPSTAIRS

CAST member Bob Pirok of the Van ‘t Hoff Institute for Molecular Sciences at the University of Amsterdam was awarded VENI grant in the TTW domain of the Dutch Research Council (NWO). With his grant, Pirok aims to improve the applicability of modern separation technology to enhance its relevance to society.

The Veni grants are part of the Talent Scheme of NWO and are aimed at excellent researchers who have recently obtained their doctorate. The grants of up to € 280,000 confirm the quality and innovative nature of their research and help to further establish themselves in their field over a three-year period.

The UPSTAIRS project aims to develop technology to reduce the resources required to use powerful separation systems.

The UPSTAIRS project aims to resolve this problem by developing algorithms that allow the automated development of methods using state-of-the-art instrumentation for the complex samples of this era. In UPSTAIRS, innovative peak-tracking algorithms will be developed that yield the capability of automated workflows to interpret the data. By utilizing these data for the construction of fundamental retention models for each analyte in the mixture, the algorithm can simulate numerous potential methods, regardless of the sample complexity. Novel equations will be designed for combinations of chemical method parameters. Candidate methods will be selected using novel chromatographic response functions based on Bayesian statistics and submitted for (automatic) validation by the instrument.

By Unleashing the Potential of Separation Technology for Achieving Innovation in Research and Society, UPSTAIRS will solve pressing problems and allow us to better understand materials, art, pharmaceuticals, environment, and other matrices.

Bob Pirok

Ultimately, the algorithms will enable a control computer to directly interact with the analytical instrumentation and interpret the resulting methods to then propose and evaluate a better method. According to Pirok, ‘a computer can do all this much more effectively than a human being, so that it takes far less time to develop an optimal method. This will bring the full potential of modern separation technology to society.’

Knowledge utilization is a strong focus of this project and will be achieved through a diverse and strong user committee and a demonstration of this workflow on a highly complex sample in the final work package. Moreover, a protocol will be published along with the developed open-access toolbox to allow analysts to use this work.

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Publications

Algorithm for evaluation of background correction algorithms

Leon Niezen developed a tool to aid in the development of background correction tools. The tool is publically available and allows a user to simulate realistic data and use this to evaluate background correction algorithms.

Chromatographic signals comprise three components, (i) low-frequency baseline drift, (ii) high-frequency noise and, for chromatography (iii) the relative mid-frequency peaks. The first two contributions together are the “background” of the signal. Often, there is more background than chromatographic information, as each data point contains a background contribution. In such a case, or if the background is of a frequency very similar to that of the relevant signals, problems may occur with the interpretation of the data. For example, peak detection may be hindered, and errors in classification, discrimination, and, especially, quantification, may occur [1].

We earlier reported how the past decade has seen the development of a plethora of different background correction algorithms [1]. While this is technically a welcome trend, it is currently unclear which tool is useful in what case. There thus is a need for a tool to objectively compare these algorithms. This will allow other users to select the appropriate algorithm for their case.

Often scientists use simulated data for such a comparison, yet such data is often controversial as it is regarded to poorly represent a realistic case. Experimental data solves this issue, but is difficult to generate for each type of chromatographic application. Moreover, it is impossible to determine the statistically true value of, for example, the area of any given peak in the chromatogram.

In this light, scientist Leon Niezen developed a data simulation tool to generate realistic data for use in algorithm comparison studies. The tool was designed to combine experimental baseline drift and noise signals with carefully modeled chromatographic peaks. For the latter, Niezen modelled experimental chromatographic peaks with distribution functions (Figure 1).

Figure 1. A) Fit for Modified Pearson VII and EMG distributions on experimental data, B) AIC values for the five best distribution models for each of the fitted peaks and C) zoomed-in fits and residuals for five individual peaks. Reproduced, with permission, from [2].

Niezen then applied the tool to evaluate a large number of background-correction algorithms that have been developed. By varying signal properties he was able to discern strengths and weaknesses of various algorithms as a function of signal properties. An example is shown in Figure 2.

Figure 2. A) Root mean square error (RMSE) surfaces obtained for the various drift-correction methods in combination with the sparsity-assisted signal (SASS) smoothing algorithm [3]. Methods are indicated by the coloured dots. B) Bottom view (lowest values) resulting from the overlaid RMSE surfaces. Reproduced, with permission, from [2].

The tool was made available to the public and the work can open-access be downloaded. Aside from being useful to scientists, the work also will be of significant importance to the automation (‘AutoLC’) project that is currently commencing in Amsterdam. The work by Niezen was funded by the UNMATCHED project, which is supported by BASF, Covestro, DSM, and Nouryon, and receives funding from the Dutch Research Council (NWO) in the framework of the Innovation Fund for Chemistry and from the Ministry of Economic Affairs in the framework of the “PPS-toeslagregeling.

References

[1] Recent applications of chemometrics in one- and two-dimensional chromatography
T.S. Bos, W.C. Knol, S.R.A. Molenaar, L.E. Niezen, P.J. Schoenmakers, G.W. Somsen, B.W.J. Pirok, J. Sep. Sci. 43(9-10), 2020, 1678-1727, DOI: 10.1002/jssc.202000011 [OPEN ACCESS]

[2] Critical comparison of background correction algorithms used in chromatography, L.E. Niezen, P.J. Schoenmakers, B.W.J. Pirok, Anal. Chim. Acta, 2022, 1201, 339605, DOI: 10.1016/j.aca.2022.339605 [OPEN ACCESS]

[3] Sparsity-assisted signal smoothing (revisited), I. Selesnick, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing – Proceedings, 2017, DOI: 10.1109/ICASSP.2017.7953017