The characterization of synthetic and bio-based polymers, particularly in their intact or fragmented forms, remains one of the most complex analytical challenges in modern materials science. Unlike small molecules, polymers exhibit broad distributions in molecular weight, composition, architecture, and microstructure, all of which critically influence their macroscopic properties and functional behavior. Accurately resolving these structural features, such as block-length distributions, chemical composition, and surface-charge profiles, requires the integration of high-resolution separation techniques with sophisticated data analysis strategies.
Within the CAST group, research efforts are focused on developing and applying advanced computational and chromatographic methodologies to enable detailed, distribution-level polymer analysis. This includes the design of novel algorithms for extracting chemically relevant information from large and complex datasets and the formulation of objective functions capable of handling the inherent complexity of polymer systems.
While these developments build upon foundational tools such as the Auto-LC platform, the emphasis here is on extending the analytical capabilities to polymers, where standard methods often fall short. This interdisciplinary approach, spanning polymer chemistry, analytical science, and computational modeling, offers a powerful framework for unlocking structural insights into industrially and technologically relevant polymers.
By enabling a more precise understanding of polymer microstructure, this work aims to support the rational design of materials with targeted properties and functionalities, thus advancing both fundamental polymer science and its industrial applications.
In addition to the chemical composition, the macromolecular structures of polymers are also of interest. One notable property is the shape of the polymer in solution, as it significantly influences its behavior in various applications. Traditionally, determining this involved establishing an average shape through Mark-Houwink plots. However, we have developed an algorithm capable of determining the shape across the molecular weight distribution instead of just the average. This approach provides more detailed insights into both inter- and intramolecular interactions. In the figure below we could determine the differences in behaviour between four celullose ethers.
Copolymers form the structural basis of many advanced materials used in cutting-edge applications, from biomedical implants and wearable electronics to self-healing aerospace coatings. Their performance characteristics, such as elasticity and toughness, are intrinsically linked to molecular-level features, including molecular weight, chemical composition, and block-length distribution (BLD). Among these, BLD, the statistical distribution of contiguous monomer units along the polymer chain, plays a critical role in dictating thermal, mechanical, and phase separation behavior. Yet, it remains one of the least accessible parameters due to analytical challenges, especially in complex copolymer systems.
While molecular weight and composition are routinely characterized, accurately determining BLD has remained elusive. Existing techniques such as nuclear magnetic resonance (NMR) spectroscopy and pyrolysis-GC-MS offer only limited insight, particularly when dealing with copolymers consisting of more than two monomer types or exhibiting irregular sequencing.
To address this gap, we have developed a computational algorithm for reconstructing block-length distributions from fragmentation data, marking a significant step forward in polymer analytics. The algorithm uses a trust-region-reflective optimization framework and was validated against ground-truth data derived from both simulated copolymer sequences and analytical solutions. This enabled objective benchmarking, something previously not achievable in BLD analysis.
Key features of our method include:
Importantly, the algorithm has been successfully applied to real-world systems such as polyamides and polyurethanes, underscoring its practical relevance in industrial contexts. This advancement empowers synthetic chemists to rationally design polymer microstructures with enhanced control over material properties.
Living with Breakthrough: Two-Dimensional Liquid-Chromatography Separations of a Water-Soluble Synthetically Grafted Bio-Polymer
H.C. van de Ven, J. Purmova, G. Groeneveld, T.S. Bos, A.F.G. Gargano, Sj. van der Wal, Y. Mengerink, P.J. Schoenmakers,
Separations, 2020, 7 (3), 41. DOI: 10.3390/separations7030041.
Composition mapping of highly substituted cellulose-ether monomers by liquid chromatography–mass spectrometry and probability-based data deconvolution
T.S. Bos, J.S. Desport, A. Buijtenhuijs, J. Purmova, L. Karlson, B.W.J. Pirok, P.J. Schoenmakers, G.W. Somsen, J Chromatogr A 1689, 2023, 463758. DOI: 10.1016/j.chroma.2022.463758.
Quantitative assessment of polymer molecular shape based on changes in the slope of the Mark-Houwink plot derived from size-exclusion chromatography with triple detection
T.S. Bos, H.J.A. Philipsen, B.B.P. Staal, J. Purmova, R.J.L. Beerends, A. Buijtenhuijs, L. Karlson, P.J. Schoenmakers, G.W. Somsen,
J Appl Polym Sci 141, 2024, e55013. DOI: 10.1002/app.55013.
Fingerprinting of hydroxy propyl methyl cellulose by comprehensive two-dimensional liquid chromatography-mass spectrometry of monomers resulting from acid hydrolysis
Tijmen S Bos, Bob WJ Pirok, Leif Karlson, Staffan Schantz, Tina A Dahlseid, Dwight R Stoll, Govert W Somsen
Journal of Chromatography A, 2024, 464874. DOI: 10.1016/j.chroma.2024.464874
Development of a comprehensive normal-phase liquid chromatography × size-exclusion chromatography platform with ultraviolet spectroscopy and high-resolution mass spectrometry detection for the chemical characterization of complex polyesters
Gino Groeneveld, Andrea FG Gargano, Robert LC Voeten, Tijmen S Bos, Paul Buijsen, Ron AH Peters, Peter J Schoenmakers
Analytica Chimica Acta, 2024, 342086. DOI: 10.1016/j.aca.2024.343086
The Role of Artificial Intelligence and Machine Learning in Polymer Characterization: Emerging Trends and Perspectives
Rick S van den Hurk, Bob WJ Pirok, Tijmen S Bos
Introducing an algorithm to accurately determine copolymer block-length distributions
Rick S van den Hurk, Ynze Mengerink, Ron AH Peters, Arian C van Asten, Bob WJ Pirok, Tijmen S Bos
Analytica Chimica Acta, 2025, 343990. DOI: 10.1016/j.aca.2025.343990
Determination of Copolymer Block-Length Distributions Using Fragmentation Data Obtained from Tandem Mass Spectrometry
Tijmen S Bos, Rick S van den Hurk, Ynze Mengerink, Ton Brooijmans, Ron AH Peters, Arian C van Asten, Bob WJ Pirok
Negotiating system’s band broadening in hydrodynamic chromatography for the determination of particle-size distributions of polymeric nanoparticles
Joshka Verduin, Jordy D Kruijswijk, Stef RA Molenaar, Nino BL Milani, Tijmen S Bos, Ron AH Peters, Bob WJ Pirok, Govert W Somsen
Analytica Chimica Acta, 2025, 344342. DOI: 10.1016/j.aca.2025.344342
Ervin sz. Kováts Award for Young Scientists Awarded to Dr. Tijmen S. Bos At the 14th Balaton Symposium on High-Performance Separation Methods, held this
Researchers Rick van den Hurk, Dr. Tijmen Bos, and Dr. Bob Pirok have, together with scientists Dr. Ynze Mengerink (Brightlands Chemelot Campus) and Prof. Dr.
A novel algorithm enables accurate reconstruction of copolymer block-length distributions (BLDs) from fragment data, advancing polymer characterization.
