Co-Polymer sequence determination over the molar mass distribution by size-exclusion chromatography combined with pyrolysis - gas chromatography

Introducing an algorithm to accurately determine copolymer block-length distributions

BACKGROUND

Copolymers are attractive for developing advanced materials with widespread applications such as medical devices, implants, or self-healing coatings for space stations and satellites. Their physical properties are tunable by controlling polymeric characteristics such as molecular weight and chemical composition. Another characteristic that has a significant influence on the material properties is the block-length distribution (BLD). Synthetic chemists can alter the BLD independently from molecular weight and chemical composition. However, analytically characterizing these BLDs, for copolymers composed out of multiple monomers, remains a huge challenge.

RESULTS

In this study, an algorithm was developed that enables the accurate determination of copolymer BLDs. Copolymers were computationally simulated and fragmented by either a repeated-sampling approach or an analytical solution to obtain unbiased ground-truth data to objectively evaluate such algorithms. The performance of the novel analytical solution, coupled with an optimization algorithm, was assessed under various conditions. We have demonstrated that a trust-region-reflective algorithm yields highly accurate BLDs when fragment data up to the tetramer level is available. Although the presence of noise in the input data led to some noise in the output, it did not notably impact the overall performance of the algorithm.

SIGNIFICANCE

The proposed algorithm demonstrated significant improvements over existing algorithms for the determination of copolymer BLDs. Using accurately simulated copolymer fragment data, which can be obtained through chemical reactions or physical processes, such algorithms could objectively be evaluated on their performance for the first time. These observations indicate that the proposed algorithm holds great potential for application to experimental copolymer fragment data.

Sample transformation in online separations: how chemical conversion advances analytical technology

While the advent of modern analytical technology has allowed scientists to determine the complexity of mixtures, it also spurred the demand to understand these sophisticated mixtures better. Chemical transformation can be used to provide insights into properties of complex samples such as degradation pathways or molecular heterogeneity that are otherwise unaccessible. In this article, we explore how sample transformation is exploited across different application fields to empower analytical methods. Transformation mechanisms include molecular-weight reduction, controlled degradation, and derivatization. Both offline and online transformation methods have been explored. The covered studies show that sample transformation facilitates faster reactions (e.g. several hours to minutes), reduces sample complexity, unlocks new sample dimensions (e.g. functional groups), provides correlations between multiple sample dimensions, and improves detectability. The article highlights the state-of-the-art and future prospects, focusing in particular on the characterization of protein and nucleic-acid therapeutics, nanoparticles, synthetic polymers, and small molecules.

Principles and potential of solvent gradient size-exclusion chromatography for polymer analysis

The properties of a polymeric material are influenced by its underlying molecular distributions, including the molecular-weight (MWD), chemical-composition (CCD), and/or block-length (BLD) distributions. Gradient-elution liquid chromatography (LC) is commonly used to determine the CCD. Due to the limited solubility of polymers, samples are often dissolved in strong solvents. Upon injection of the sample, such solvents may lead to broadened or poorly shaped peaks and, in unfavourable cases, to "breakthrough" phenomena, where a part of the sample travels through the column unretained. To remedy this, a technique called size-exclusion-chromatography gradients or gradient size-exclusion chromatography (gSEC) was developed in 2011. In this work, we aim to further explore the potential of gSEC for the analysis of the CCD, also in comparison with conventional gradient-elution reversed-phase LC, which in this work corresponded to gradient-elution reversed-phase liquid chromatography (RPLC). The influence of the mobile-phase composition, the pore size of the stationary-phase particles, and the column temperature were investigated. The separation of five styrene/ethyl acrylate copolymers was studied with one-dimensional RPLC and gSEC. RPLC was shown to lead to a more-accurate CCD in shorter analysis time. The separation of five styrene/methyl methacrylate copolymers was also explored using comprehensive two-dimensional (2D) LC involving gSEC, i.e. SEC × gSEC and SEC × RPLC. In 2D-LC, the use of gSEC was especially advantageous as no breakthrough could occur.

Hyphenation of liquid chromatography and pyrolysis-flame ionization detection/mass spectrometry for polymer quantification and characterization

Size-exclusion chromatography (SEC) hyphenated to pyrolysis-gas chromatography (Py-GC) has been demonstrated as a powerful tool in polymer analysis. A main limitation to the wider application of the method are the long second-dimension Py-GC analysis times, resulting in limited first-dimension sampling and/or long overall run times. Therefore, we set out to develop an online hyphenated SEC×Py-MS/FID method, removing the GC separation and allowing for a drastically reduced second-dimension analysis time compared to SEC-Py-GC. The pyrolysis method had a cycle time of 1.31 min, which was facilitated by liquid nitrogen cooling of the programmable temperature vaporizer (PTV) used for pyrolysis. The developed method featured no molar mass discrimination for masses above ±1.3 kDa, rendering it applicable to most commercial polymer systems. The method was demonstrated on multiple samples, including a complex industrial sample, yielding chemical composition heterogeneity and in some cases sequence heterogeneity information over the molar mass distribution.

Online hyphenation of size-exclusion chromatography and pyrolysis-gas chromatography for polymer characterization

An understanding of the composition and molecular heterogeneities of complex industrial polymers forms the basis of gaining control of the physical properties of materials. In the current work we report on the development of an online method to hyphenate liquid polymer chromatography with pyrolysis-GC (Py-GC). The designed workflow included a 10-port valve for fractionation of the first-dimension effluent. Collected fractions were transferred to the Py-GC by means of a second LC pump, a 6-port valve was used to control injection in the Py-GC, allowing the second pump to operate continuously. The optimized large volume injection (LVI) method was capable of analyzing 117 µL of the LC effluent in a 6 min GC separation with a total cycle time of 8.45 min. This resulted in a total run time of 2.1 h while obtaining 15 Py-GC runs over the molar mass separation. The method was demonstrated on various real-life samples including a complex industrial copolymer with a bimodal molar mass distribution. The developed method was used to monitor the relative concentration of 5 different monomers over the molar mass distribution. Furthermore, the molar mass-dependent distribution of a low abundant comonomer (styrene, <1% of total composition) was demonstrated, highlighting the low detection limits and increased resolving power of this approach over e.g. online NMR or IR spectroscopy. The developed method provides a flexible and widely applicable approach to LC-Py-GC hyphenation without having to resort to costly and specialized instrumentation.

Co-Polymer Sequence Determination Using SEC Combined with Py-GC

LCGC Europe spoke to Wouter Knol from the University of Amsterdam, The Netherlands, about co‑polymers, the difficulties surrounding their analysis, and what pyrolysis-gas chromatography (Py‑GC) offers in comparison to other techniques when analyzing co-polymers and their sequence.