Tag: Block-length distributions

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Block-Length Distributions Using Fragmentation Data Obtained from Tandem Mass Spectrometry

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. Ron Peters (Covestro, HIMS), developed a new algorithm that can analyze copolymers and determine their block structure, something that was previously out of reach with existing techniques.

Polymers are all around us, from the coatings on your phone and the materials in your running shoes to life-saving drug-delivery systems and medical implants. Many of these advanced materials are copolymers, which are made by combining different types of chemical building blocks.

Interestingly, even when two copolymers have the same overall composition, the way these building blocks are arranged can lead to drastically different properties. For example, one polymer might be rigid while another is flexible, or one transparent while another is opaque. Within a single batch, this arrangement can vary from molecule to molecule. This variability can be described using a concept called the block-length distribution (BLD), which captures how frequently different block arrangements occur. This distribution plays a key role in determining a material’s performance characteristics, including its flexibility, strength, and biodegradability.

Until now, accurately measuring these distributions at the molecular level has been a major challenge. Traditional techniques like nuclear magnetic resonance could only offer averaged information. The team’s newly developed algorithm changes that by combining tandem mass spectrometry (MS/MS) data with a smart computational approach that takes fragmentation behavior into account. The algorithm allows researchers to reconstruct how blocks are distributed within a copolymer sample, giving a much more detailed picture of the material’s internal structure.

This method has already been successfully applied to study polyamides and polyurethanes, important industrial polymers found in everything from textiles to insulation foams. Notably, the findings showed that even polymers with the same chemical makeup can have very different block distributions, depending on how they were synthesized. These subtle differences can explain variations in material performance that would otherwise remain hidden.

 

The ability to determine BLDs with such precision not only improves our understanding of polymer chemistry but also opens the door to the rational design of next-generation materials. By fine-tuning the block arrangement, scientists and engineers can tailor materials more precisely to specific applications. It could also support the development of more sustainable materials, as better control over structure may lead to improved recyclability or allow for the use of bio-based feedstocks.

This work is part of the PARADISE project, a collaboration between academic institutions (VU Amsterdam and the University of Amsterdam) and industrial partners including Covestro, DSM, Shell, and Genentech, aimed at driving forward innovation in polymer research.

Relevant article: https://doi.org/10.1021/acs.macromol.5c00297,

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Introducing an algorithm to accurately determine copolymer block-length distributions

Copolymers are the foundation of many high-performance materials used in advanced applications such as medical devices, implants, electronics, and self-healing coatings for aerospace and space exploration. Their material properties—such as flexibility, toughness, or responsiveness—can be finely tuned by adjusting polymer characteristics like molecular weight, chemical composition, and block-length distribution (BLD).

While molecular weight and composition are routinely analyzed, the BLD—describing how monomer blocks are arranged along the polymer chain—remains difficult to measure, particularly for copolymers composed of more than one type of monomer. Understanding and controlling BLD is crucial because it plays a pivotal role in determining mechanical, thermal, and phase-separation behavior. However, current methods, such as NMR or pyrolysis-GC-MS, have limitations in accurately and comprehensively characterizing BLDs.

 

Our Solution
In this study, we introduce a computational approach that enables the quantitative determination of block-length distributions from copolymer fragmentation data. We developed and validated an algorithm using both simulated copolymer sequences and analytical solutions to generate ground-truth fragment data. This allowed for an objective evaluation of algorithm performance—something not previously achievable.

The algorithm incorporates a trust-region-reflective optimization strategy and was tested under various conditions, including data noise and fragment size limitations. When fragment data containing chains of up to four monomers (tetramers) were included, the algorithm consistently reconstructed BLDs with high accuracy, achieving similarity coefficients (SC) above 0.99 compared to the known distributions.

https://doi.org/10.1016/j.aca.2025.343990

 

Key Innovations

  • High Accuracy: Outperforms existing algorithms in BLD reconstruction from mass spectrometry-based data.

  • Versatile: Capable of handling complex distribution shapes, including non-unimodal and asymmetric distributions.

  • Robust to Noise: Maintains accuracy even when fragment data includes measurement noise.

  • Objective Evaluation: Enables benchmarking of BLD algorithms using simulated data with known parameters.

 

Practical Relevance
The algorithm was also applied to experimental polymer systems such as polyamides and polyurethanes, demonstrating its applicability to real-world materials. This makes it a powerful tool for synthetic chemists seeking to design materials with tailored properties by manipulating block structures.

 

Future Directions
Translating this approach from simulation to experimental data introduces new challenges. Mass spectrometry data may be affected by ionization efficiencies and fragmentation biases, while NMR may suffer from overlapping signals in complex systems. To address this, future research will focus on:

  • Incorporating fragmentation preferences based on bond type or analytical method.

  • Developing preprocessing pipelines tailored to specific instrumentation.

  • Extending the algorithm to support more than two monomer types, while managing the increased computational complexity.

 

Conclusion
This work represents a significant advancement in the field of polymer analytics. For the first time, researchers can objectively and accurately reconstruct the block-length distribution of complex copolymers from fragment data. By making this tool available, we aim to empower chemists and materials scientists in designing next-generation materials with precisely engineered microstructures.