Category: Publications

These items reflect publication alerts and updates.

Categories
Publications

Analysis of Heavily Glycated Proteins by HILIC and SEC-HRMS

Trypsin digestive enzyme molecule (human). Enzyme that contributes to the digestion of proteins in the digestive system. 3D rendering based on protein data bank entry 1trn. Ball-and-stick model, black background.

The CAST scientist Ziran Zhai recently published a manuscript in which he investigated the usefulness of two novel CAST methods, namely low-flow HILIC [1] and SEC-HRMS [2], to characterize extensively glycated proteins from the intact level. Zhai focuses on four critical aspects: i) using denaturing HILIC-MS to separate glycoconjugates (including, in some cases, the separation of isomers), ii) using native SEC-MS to study the aggregates formed during glycation, iii) identifying the advanced glycation end-products (AGEs), and iv) monitor the dynamic changes of AGEs.

Advanced glycation end products (AGEs) are a family of compounds of diverse chemical nature that are the products of nonenzymatic reactions between reducing sugars (here glucose) and, in the case of our study, proteins. Sugars can attach at different positions in a protein following a Maillard reaction, distributing over several amino acids and in many different chemical species.  Previous studies focused on digesting glycated proteins to identify the AGEs and glycoconjugates from the peptide level. However, these strategies make it difficult to monitor the co-occurrence of multiple glycation events and, therefore, cannot monitor the evolution of the glycation process.

In this study, three model proteins (RNase-A, hemoglobin, and NISTmab) were exposed to conditions that favored extensive glycation and the formation of AGEs. As shown, with HILIC-MS, the glycated forms of the proteins could be resolved based on the number of reducing monosaccharides, and the SEC-MS method under non-denaturing conditions provided insights into glycated aggregates (Figure 1). More than 25 different types of species were observed in both methods, among which 19 of these species have not been previously reported. By tracing the progress of glycation, the dynamic changes of the specific AGEs could be monitored over time.

Figure 1. BPC of non-glycated (A, C) and glycated (20 days, B and D) RNase-A acquired by HILIC-MS and SEC-MS. Deconvolution results of glycated RNase-A (20 days, E and F) obtained by HILIC-MS and SEC-MS methods.

The study is part of the FFF (From Form to Function) project of Zhai, Astefanei,
Corthals, and Gargano and was funded by the Chinese Scholarship Council (CSC) and was recently published in Analytica Chimica Acta and can be accessed freely at the link below.

https://www.sciencedirect.com/science/article/pii/S0003267024003441

References

 [1] https://pubs.acs.org/doi/full/10.1021/acs.analchem.1c03473

[2] https://www.sciencedirect.com/science/article/pii/S0003267023005457

 

Categories
Publications

Optimisation of 2D-LC separations by AutoLC

Optimisation of 2D-LC separation technology
Optimisation of 2D-LC separation technology

In their latest work, the AutoLC team of the CAST group of Pirok at the University of Amsterdam extended their method-development workflow to facilitate optimisation of 2D-LC separations [1].

The demonstrated workflow was capable of unsupervised gradient optimization for comprehensive 2D-LC-MS methods without needing to specify sample information. The required algorithms were designed by CAST researchers Stef Molenaar, Tijmen Bos and Jim Boelrijk, under the supervision of Bob Pirok.

The workflow was inspired by the original theoretical paper by Pirok in 2016 [2], in which retention models are constructed to computationally simulate methods and select the optimal parameters. In a nutshell, the system first utilizes very generic methods to measure the sample. This data is then used to construct retention models that describe the retention behavior of the analytes as a function of the mobile-phase composition. A large number of methods are then simulated computationally, and a computed optimum is submitted to the LC system. The LC system then carries out the proposed method and submits its data back to the system afterwards. Consequently, unsupervised optimisation of 2D-LC separations may be attained.

Unsupervised optimization of comprehensive 2D-LC separations
Figure 1. The AutoLC algorithm was demonstrated on a peptide digest separation using RPLC in both dimensions. Panels A, B, D and E show scanning conditions, with Panels C and F showing a computed method that was proposed unsupervised by the algorithm. Reproduced with permission from [1].

The Pirok group earlier had demonstrated their AutoLC platform on 1D separations [3], but now extended this to the more complicated 2D separation methods [1]. To accomplish this, algorithms were designed to compute and optimize complex second-dimension shifted-gradient assemblies.

The researchers also investigated the robustness of the retention models and the influence of the errors in peak-width predictions.

The study was conducted in a collaboration with the group of Dwight Stoll at Gustavus Adolphus College where the measurements were conducted. The developed tool for optimisation of 2D-LC separations was demonstrated on a complex peptide digest sample with RPLC in both of the dimensions.

The work was a product of Pirok’s UPSTAIRS (Unleashing the Potential of Separation Technology to Achieve Innovation in Research and Society) project, which is financed by the Dutch Research Council (NWO), as well as the synergetic UNMATCHED project, which is supported by BASF, DSM and Nouryon, and also receives funding from NWO.

The work was published open-access in Journal of Chromatography A and can be accessed free of charge here.

References

  1. Computer-driven optimization of complex gradients in comprehensive two-dimensional liquid chromatography, S.R.A. Molenaar, T.S. Bos, J. Boelrijk, T.A. Dahlseid, D.R. Stoll, B.W.J. Pirok, Chromatogr. A, 2023, 1707, 464396, DOI: 10.1016/j.chroma.2023.464306.
  2. Program for the interpretive optimization of two-dimensional resolution, B.W.J. Pirok, S. Pous-Torres, C. Ortiz-Bolsico, G. Vivó-Truyols and P.J. Schoenmakers, J. Chromatogr. A, 2016, 1450, 29–37, DOI: 10.1016/j.chroma.2016.04.061.
  3. Chemometric Strategies for Fully Automated Interpretive Method Development in Liquid Chromatography S. Bos, J. Boelrijk, S.R.A. Molenaar, B. van ‘t Veer, L.E. Niezen, D. van Herwerden, S. Samanipour, D.R. Stoll, P. Forré, B. Ensing, G.W. Somsen, B.W.J. Pirok, Anal. Chem. 2022, 94(46), 16060–16068, DOI: 10.1021/acs.analchem.2c03160.
Categories
Publications

2D-LC in industry: technological innovations reviewed

2D-LC in industry: technological innovations reviewed
2D-LC in industry: technological innovations reviewed

CAST scientist Rick van den Hurk wrote a review on recent developments in 2D-LC and the use of 2D-LC in industry. He did this under the supervision of Bob Pirok and in collaboration with Matthias Pursh (Dow) and Dwight Stoll (Gustavus Adolphus College).

Two-dimensional liquid chromatography (2D-LC) greatly advances the separation powered of analytical separation sciences through a better peak capacity as well as offering more-tailored selectivity combinations.

However, the field of 2D-LC is, in particular in contrast to 2D-GC, still very immature and under significant development. In 2019, Bob Pirok, Dwight Stoll and Peter Schoenmakers published a review in which they examined the latest trends from 2015 until 2019 [1].

In this recent installment [2], Rick van den Hurk reviewed the recent innovations between 2019 and 2023. In addition the review also devotes significant focus to the implementation of the technique in industry. The review was co-written by Bob Pirok, Dwight Stoll (Gustavus Adolphus College) and Matthias Pursch (Dow).

The authors examined over 200 articles and also compared these with the articles published prior to 2019. In their review, the authors concluded that mobile-phase mismatch continues to be an important focus area for the field, and several modulation strategies and new variants were discussed.

Van den Hurk and co-workers also noticed that a third of the publications had at least one author affiliated with industry. Application fields that particularly demonstrated involvement were the polymer characterization, metabolomics, and pharmaceutical and biopharmaceutical analysis. Furthermore, industrial applications favored the use of heart-cut 2D-LC and largely employed on-line hyphenation. The authors did note that the database was likely to be missing out on a number of industrial works that are not published for confidentiality reasons.

Other important developments were the increased popularity of computer-aided strategies, alternative gradient-elution methods to facilitate modulation, as well as multi-stage, multi-dimensional separations, the latter of which were applied to the characterization of protein therapeutics.

2D-LC in industry: technological innovations reviewed
Figure. Number of applications per application area distributed by non-comprehensive (light blue) and comprehensive (dark blue) applications between 2019 and 2023. Reproduced with permission from [2].

Two-dimensional liquid chromatography is of paramount importance to the PARADISE project of CAST scientist Bob Pirok in which multi-dimensional separation technology is used to achieve separation of highly complex samples. In addition, the project aims to characterize the correlation of different sample properties within a single analytical solution. The outcomes of this recent review are thus of value to the ongoing progress in the PARADISE project. Rick van den Hurk is a PhD candidate in the PARADISE project, which stands for Propelling Analysts by Removing Analytical-, Data-, Instrument- and Sample-related Encumbrances and receives funding from the Dutch Research Council (NWO), as well as a number of public and private organisations. Read more about the PARADISE project here.

In addition, the review was part of Pirok’s UPSTAIRS project, which aims to improve the accessibility of advanced separation technology by developing computational methods to leverage chromatographic theory in an unsupervised workflow. This project also receives funding from NWO.

The work was published in TrAC Trends in Analytical Chemistry as open access, and can thus be accessed for free here.

References

  1. Recent Developments in Two-Dimensional Liquid Chromatography: Fundamental Improvements for Practical Applications, B.W.J. Pirok, D.R. Stoll and P.J. Schoenmakers, Anal. Chem., 2019, 91(1), 240-263, DOI: 10.1021/acs.analchem.8b04841
  2. Recent trends in two-dimensional liquid chromatography, R.S. van den Hurk, M. Pursch, D.R. Stoll, B.W.J. Pirok, TrAC Trends in Analytical Chemistry, 2023, 166, 117166, DOI: 1016/j.trac.2023.117166
Categories
Publications

Latest developments in 2D gas chromatography reviewed

In this article, the latest developments in 2D gas chromatography are reviewed by scientists Nino Milani, Peter Schoenmakers and Bob Pirok.
In this article, the latest developments in 2D gas chromatography are reviewed by scientists Nino Milani, Peter Schoenmakers and Bob Pirok.

Comprehensive two-dimensional GC (GC×GC) has developed significantly in the three decades since the technique was first demonstrated experimentally. Consequently, the number of users, published methods, and scientific papers about 2D GC has increased dramatically. In their recent review in Journal of Separation Sciences, CAST member Nino Milani along with Eric van Gilst discuss the latest developments in 2D gas chromatography [1]. The authors reviewed the latest developments in modulation methods and also touched upon detection, retention modelling and data analysis.

Interestingly, Milani et al. found a surprising large number of technology-oriented publications that investigated new modulation technology. Thermal modulators – which are mostly used in application-papers still – yield excellent performance, even without the need for coolant consumables. Novel flow modulators can compete with thermal modulators, yet tend to be simpler and easier to operate.

The authors found a discrepancy between the use of flow modulation in technology-driven publications versus those in application studies. This was considered a testimony to the fact that the field is still highly dynamic, despite the widely-perceived maturity of GC×GC.

Milani also concluded that benchmark datasets were required to properly evaluate the latest developments in the field of signal processing and data analysis.

GC×GC is, along with GC×GC, of high interest to the PARADISE project, in which 2D chromatography is used to establish simultaneous determination and correlation of multiple sample dimensions. The analysis of datasets originating from 2D separations is also of high interest. Therefore, this review on the latest developments in 2D gas chromatography was written in the context of the PARADISE project. The PARADISE project is funded by public and private organisations and also receives funding from the Dutch Research Council (NWO).

The review was published open access in Journal of Separation Science and can be accessed free of charge here.

References

[1] Comprehensive two‐dimensional gas chromatography—A discussion on recent innovations, N.B.L. Milani, E. van Gilst, B.W.J. Pirok, P.J. Schoenmakers, J. Sep. Sci., 2023, DOI: 10.1002/jssc.202300304

Categories
Publications

Peak tracking for larger numbers of 2D chromatography datasets

Abstract digital connection dots. Technology background. Network connection structure. Plexus effect. 3d

In an international collaboration with the University of Stellenbosch, Gustavus Adolphus College and Envalior, a peak-tracking algorithm was developed by CAST scientist Stef Molenaar to compare multiple datasets in one-dimensional (1D) and two-dimensional (2D) chromatography [1]. In his study, Molenaar investigated two application strategies: i) data processing where all chromatograms are produced in one sequence and processed simultaneously, and ii) method optimization where chromatograms are produced and processed cumulatively. The latter strategy is analogue to practice in within the AutoLC framework that is developed by the Pirok group.

Peak tracking for 2D chromatography
Structure of a comprehensive 2D chromatogram. A) raw data, B) folded data matrix, C) interpolated matrix, D) reconstructed first dimension chromatography, E) reconstructed second dimension chromatogram. Reproduced with permission from [2].

One of the difficult operations in using comprehensive 2D chromatography concerns the data processing. With the first dimension not having its own detector, the first-dimension chromatogram must be reconstructed using the information obtained from the second-dimension detector (Figure 1A). This process is also referred to as “folding of the chromatogram”. The result is what is shown in Figure 1B.

As a result, the number of datapoints in the first dimension is equal to the number of modulations. There thus exists a scarcity of data to reconstruct the first dimension (Figure 1D), relative to the abundance of datapoints to describe the second dimension (Figure 1E).

Linking strategies for peak tracking for 2D chromatography
Different clustering strategies. Chromatogram #3 (red) represents a dataset where something went wrong, and thus connections with chromatogram #3 don't provide information about at least one peak. However, following other connections still creates an intact network (green). A) Batch strategy: Each chromatogram is connected to the next, creating a loop indicated with the black dotted line, and then random cross connections are made within the loop. B) Complete strategy: All chromatograms are connected to all others. C) Cumulative strategy: Every new chromatogram is connected to two randomly selected chromatograms. Reproduced with permission from [1].

In his article, Molenaar tested the first strategy on data from comprehensive 2D liquid chromatography (LC×LC) and comprehensive 2D gas chromatography (GC×GC) separations of academic and industrial samples of varying compound classes (monoclonal-antibody digest, wine volatiles, polymer granulate headspace, and mayonnaise). To accomplish this, he collaborated with scientists Dr. John Mommers (Envalior), Prof. Dwight Stoll (Gustavus Adolphus College), Sithandile Ngxangxa (University of Stellenbosch) and Prof. André de Villiers (University of Stellenbosch).

The work is envisaged to be instrumental for successful translation of the AutoLC algorithm [3] to 2D separations. The study was part of the UPSTAIRS project of Pirok and funded by the Dutch Research Council (NWO).

The study was recently published in Journal of Chromatography A and can be accessed freely here.

References

  1. Algorithm for tracking peaks amongst numerous datasets in comprehensive two-dimensional chromatography to enhance data analysis and interpretation, S.R.A. Molenaar, J.H.M. Mommers, D.R. Stoll, S. Ngxangxa, A.J. de Villiers, P.J. Schoenmakers, B.W.J. Pirok, Chromatogr. A, 1705, 2023, 464223: DOI: 10.1016/j.chroma.2023.464223.
  2. Challenges in Obtaining Relevant Information from One- and Two-Dimensional LC Experiments, B.W.J. Pirok & J.A. Westerhuis, LC-GC North America, 6(38), 2020, 8-14, DOI: 10.56530/lcgc.na.jk4782s5.
  3. Chemometric Strategies for Fully Automated Interpretive Method Development in Liquid Chromatography S. Bos, J. Boelrijk, S.R.A. Molenaar, B. van ‘t Veer, L.E. Niezen, D. van Herwerden, S. Samanipour, D.R. Stoll, P. Forré, B. Ensing, G.W. Somsen, B.W.J. Pirok, Anal. Chem. 2022, 94(46), 16060–16068, DOI: 10.1021/acs.analchem.2c03160.
Categories
Publications

Feature article on machine learning and method development 

Feature article on machine learning and method development in liquid chromatography
Feature article on machine learning and method development in liquid chromatography

Liquid chromatography (LC) plays an important role in almost all public and private sectors. It is thus not surprising LC represents of one of the largest analytical fields in terms of resource requirements. For this reason the field is still significantly in development. This has led to the advent of high-resolution separations and multi-dimensional chromatography. However, the analytical separations technology is evolving faster than the ability of humanity to employ it effective.

Indeed, separations are often performed under suboptimal conditions and technological capabilities remain unused. Because expert knowledge and method development time are increasingly scarce, methods are often inefficient. Exploiting the full technological capabilities of liquid-phase separation technology requires deep knowledge and great time investments.

Feature article on machine learning and method development in liquid chromatography

To tackle this, Pirok started the UPSTAIRS (Unleashing the Potential of Separation Technology to Achieve Innovation in Research and Society) project in 2022 using funding from the Dutch Research Council (NWO). The goal of the project is to improve the accessibility of state-of-the-art separation technology by connecting chromatographic theory to practice using computational methods. The group aims to develop the “AutoLC” workflow in which unsupervised method development becomes possible for (2D-)LC-MS methods.

In hisf eature article on machine learning and method development, CAST researcher Gerben van Henten along with Tijmen Bos and Bob Pirok examines the role of computational methods and machine learning to facilitate automated method development. Method optimization strategies that can simultaneously optimize the large number of parameters involved are therefore of great interest to chromatographers [1].

The article focuses particularly the implementation of computer-aided workflows for the optimization of kinetic and thermodynamic parameters in LC, as well as on the possibilities to conduct this in a closed-loop fashion.

Feature article on machine learning and method development
Figure. Example of a method development workflow for unsupervised optimization described by the article [1].

In the feature article on machine learning and method development, Van Henten et al. return to the fundamental equation that quantifies resolution into three components: retention, selectivity and chromatographic efficiency. Different strategies are discussed and place into context of this distinction (Figure).

The article was published in the June edition of LC-GC Europe which was released at the largest conference for LC separations HPLC2023 in Düsseldorf, Germany. The article can be read free of charge here.

References

  1. Approaches to Accelerate Liquid Chromatography Method Development in the Laboratory Using Chemometrics and Machine Learning, G.B. van Henten, T.S. Bos, B.W.J. Pirok, LCGC Europe, 2023, 36(6), 202-209, DOI: 56530/lcgc.eu.rh7676j5.
Categories
Publications

Packed loops may reduce dispersion in 2D-LC

Packed particles may reduce dispersion in 2D-LC
Packed particles may reduce dispersion in 2D-LC

Modulation interfaces employing sample loops are applied in many hyphenated separations such as two-dimensional liquid chromatography (2D-LC). When the first-dimension effluent in 2D-LC is eluted from the modulation loop, dispersion effects occur due to differences in the laminar flow velocity of the filling and emptying flow. These effects were recently studied by Moussa et al. whom recommended the use of coiled loops to promote radial diffusion and reduce this effect. In the 1980s, Coq et al. investigated the use of packed loops, which also promote radial diffusion, in large volume injection 1D-LC. Unfortunately, this concept was never investigated in the context of 2D-LC modulation.

In their study CAST scientist Wouter Knol evaluated use of packed loops in 2D-LC modulation and compares them to unpacked coiled and uncoiled modulation loops [1]. The work was conducted under the supervision of Prof. Ron Peters and Dr. Bob Pirok. The effect of the solvents, loop volume, differences in filling and emptying rates, and loop elution direction on the elution profile were investigated.

Packed loops may reduce dispersion in 2D-LC
Figure 1. Schematic representation of the experiential setup in the loop filling and emptying valve position. Along with the packing of loops, the effect of coiling was also investigated. Reproduced with permission from [1].

To pragmatically quantify elution profile characteristics, Knol employed statistical moments. Decreased dispersion was observed in all cases for the packed loops compared to unpacked loops and unpacked coiled loops. In particular for larger loop volumes the dispersion was reduced significantly. Furthermore, countercurrent elution resulted in narrower elution profiles in all cases compared to concurrent elution.

Knol found that packed modulation loops are of high interested when analytes are not refocussed in the second-dimension separation (e.g. for size-exclusion chromatography). One additional observation was that the work suggested that the use of packed loops may aid in prevention of loop overfilling.

Packed loops may reduce dispersion in 2D-LC
Figure 2. Schematic representation of the experiential setup in the loop filling and emptying valve position. Along with the packing of loops, the effect of coiling was also investigated. Reproduced with permission from [1].

This study was conducted in the context of the UNMATCHED project which received funding from BASF, DSM and Nouryon, as well as the Dutch Research Council (NWO). The work was published in Journal of Chromatography A and can be accessed for free here.

Categories
Publications

Unsupervised LC method development with AutoLC

Unsupervised LC method development with AutoLC
Unsupervised LC method development with AutoLC

In an international and interdisciplinary collaboration, CAST members Tijmen Bos, Stef Molenaar, Jim Boelrijk, Leon Niezen and Bob Pirok have demonstrated unsupervised LC method development with AutoLC. This is the first automated LC-MS method development workflow. It was applied it to a complex antibody digest sample. The work was recently published in Analytical Chemistry as cover article [1].

The majority of liquid chromatography (LC) methods are still developed in a conventional manner, that is, by analysts who rely on their knowledge and experience to make method development decisions. To tackle this problem, several tools utilizing design-of-experiment workflows, retention modeling based on experimental data and/or chemical structure information have been developed and even commercialized.

However, these approaches are generally difficult to scale with sample complexity and require significant user input to operate. Consequently, high-resolution separation technology and multi-dimensional systems have not been economically feasible for routine use. To improve the accessibility of state-of-the-art separation technology, the Pirok group at the University of Amsterdam is developing a workflow capable of unsupervised method development.

This has led to the present demonstration of a novel, open-source algorithm for automated and interpretive method development of LC(−mass spectrometry) separations (“AutoLC”). The scientists constructed a closed-loop workflow that interacted directly with the LC system and ran unsupervised in an automated fashion. 

The first demonstration of AutoLC was published as front cover article in Analytical Chemistry
The study was published as feature article in Analytical Chemistry.
Unsupervised LC method development with AutoLC
Schematic overview of the generic workflow employed by the AutoLC algorithm using retention modeling (top, blue) or BO (bottom, pink).

The team tested the algorithm using two newly designed method development strategies. The first utilized retention modeling, whereas the second used a Bayesian-optimization machine learning approach. In both cases, the algorithm could arrive within 4–10 iterations (i.e., sets of method parameters) at an optimum of the objective function, which included resolution and analysis time as measures of performance.

Retention modeling was found to be more efficient while depending on peak tracking, whereas Bayesian optimization was more flexible but limited in scalability. We have deliberately designed the algorithm to be modular to facilitate compatibility with previous and future work (e.g., previously published data handling algorithms).

AutoLC was tested on a peptide digest mixture.
The AutoLC framework was tested on an antibody digest sample. A) example of a generic scouting measurement, B) proposed optimum at the 4th iteration. Reproduced with permission of [1].

The degree of separation is often quantified as the resolution between chromatographic peaks, which can be written as a product of retention, selectivity and chromatographic efficiency. Currently, the AutoLC framework largely focuses on retention, but contemporary efforts have shifted focus to include selectivity. Support of validation is the logical next step thereafter.

AutoLC leverages earlier studies and interdisciplinary expertise

The AutoLC framework is the product of a several years of scientific studies that were conducted within public-private partnerships by the group of Pirok. These projects focused relevant aspects such as peak tracking [2,3], machine learning [4], and gradient deformation [5]. The AutoLC framework was designed to be modular so as to leverage global initiatives by the scientific community that were published in literature. Currently, the development of the framework is supported by funding from several grants from the Dutch Research Council (NWO). It is the prime topic of the UPSTAIRS project.

The present study was conducted in collaboration with Dr. Bernd Ensing (Computational Chemistry, University of Amsterdam), Dr. Saer Samanipour (Analytical Chemistry, University of Amsterdam), Dr. Patrick Forré (Institute for Informatics, University of Amsterdam), as well as scientists from Gustavus Adolphus College.

Special acknowledgement to Peter Schoenmakers

In the article, the authors acknowledged Prof. Peter Schoenmakers for his founding contributions. In one of his first papers in 1978 on gradient selection for RPLC method development Schoenmakers already envisaged the use of scouting data to facilitate automated method development [6].

Schoenmakers was the promotor of Bob Pirok, who first published about this topic in his 2016 paper in which the theoretical possibility of leveraging these concepts for 2D-LC were investigated [7]. This study was marked the start of this research line that, ultimately, led to the present publication of AutoLC.

References

  1. Chemometric Strategies for Fully Automated Interpretive Method Development in Liquid Chromatography S. Bos, J. Boelrijk, S.R.A. Molenaar, B. van ‘t Veer, L.E. Niezen, D. van Herwerden, S. Samanipour, D.R. Stoll, P. Forré, B. Ensing, G.W. Somsen, B.W.J. Pirok, Anal. Chem. 2022, 94(46), 16060–16068, DOI: 10.1021/acs.analchem.2c03160.
  2. Peak-Tracking Algorithm for Use in Automated Interpretive Method-Development Tools in Liquid Chromatography, B.W.J. Pirok, S.R.A. Molenaar, L.S. Roca and P.J. Schoenmakers, Anal. Chem., 2018, 90(23), 14011-14019, DOI: 10.1021/acs.analchem.8b03929.
  3. Peak-tracking algorithm for use in comprehensive two-dimensional liquid chromatography – application to monoclonal antibody peptides, R.A. Molenaar, T.A. Dahlseid, G. Leme, D.R. Stoll, P.J. Schoenmakers, B.W.J. Pirok, J. Chromatogr. A, 2021, 1639, 461922, DOI: 10.1016/j.chroma.2021.461922.
  4. Bayesian Optimization of Comprehensive Two-dimensional Liquid Chromatography Separations, J. Boelrijk, B.W.J. Pirok, B. Ensing, P. Forré, Chromatogr. A, 1659, 2021, 462628, DOI: 10.1016/j.chroma.2021.462628.
  5. Reducing the influence of geometry-induced gradient deformation in liquid chromatographic retention modellingS. 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.
  6. Gradient selection in reversed-phase liquid chromatography, P.J. Schoenmakers, H.A.H. Billiet, R. Tussen, L. De Galan, J. Chromatogr. A, 1978, 149,  519-537, DOI: 10.1016/S0021-9673(00)81008-0.
  7. Program for the interpretive optimization of two-dimensional resolution, B.W.J. Pirok, S. Pous-Torres, C. Ortiz-Bolsico, G. Vivó-Truyols and P.J. Schoenmakers, J. Chromatogr. A, 2016, 1450, 29–37, DOI: 10.1016/j.chroma.2016.04.061.
Categories
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

Categories
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