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

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.

Multi-Parameter Analysis of Nanoplastics in Flow: Taking Advantage of High Sensitivity and Time Resolution Enabled by Stimulated Raman Scattering

Here, we demonstrate the detection of nanoplastics (NPLs) in flow with stimulated Raman scattering (SRS) for the first time. NPLs (plastic particles <1000 nm) have recently been detected in different environmental samples and personal care products. However, their characterization is still an analytical challenge. Multiple parameters, including size, chemical composition, and concentration (particle number and mass), need to be determined. In an earlier paper, online field flow fractionation (FFF)-Raman analysis with optical trapping was shown to be a promising tool for the detection of particles in this size range. SRS, which is based on the enhancement of a vibrational transition by the matching energy difference of two laser beams, would allow for much more sensitive detection and, hence, much shorter acquisition times compared to spontaneous Raman microspectroscopy (RM). Here, we show the applicability of SRS for the flow-based analysis of individual, untrapped NPLs. It was possible to detect polyethylene (PE), polystyrene (PS), and poly(methyl methacrylate) (PMMA) beads with diameters of 100-5000 nm. The high time resolution of 60.5 μs allows us to detect individual signals per particle and to correlate the number of detected particles to the injected mass concentration. Furthermore, due to the high time resolution, optically trapped beads could be distinguished from untrapped beads by their peak shapes. The SRS wavenumber settings add chemical selectivity to the measurement. Whereas optical trapping is necessary for the flow-based detection of particles by spontaneous RM, the current study demonstrates that SRS can detect particles in a flow without trapping. Additionally, the mean particle size could be estimated using the mean width (duration) and intensity of the SRS signals.

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.

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.