Ricardo Sebastiaan van Den Hurk

End-group-selective size-exclusion chromatography (esSEC) coupled with mass spectrometry (MS) is presented as an analytical approach for the separation and characterization of acid-terminated and non-acid-terminated poly(lactic acid) (PLA) and poly(lactide-co-glycolide) (PLGA). The method employs a gradient normal-phase liquid chromatography system with basic (triethylamine) and acidic (formic acid) additives in ethyl acetate-based mobile phases, enabling selective retention of acid-terminated polymers, while non-acid-terminated species elute according to hydrodynamic volume, i.e., their effective size in solution. Following their release from the stationary phase, the acid-terminated polymers also undergo separation according to hydrodynamic volume. The separation involves adsorption of acid-terminated polymers under basic conditions, followed by their release upon introduction of an acidic mobile phase and subsequent size-based separation. Gradient steepness strongly influenced the elution behavior of acid-terminated polymers. Shallow gradients resulted in broader elution profiles reflecting prolonged adsorption–desorption, whereas steeper gradients produced sharper, more symmetrical peaks. The method can be directly coupled to MS, enabling molecular-level characterization of end-group structures and chemical composition across the chromatographic separation. The proposed esSEC–MS method provides a novel strategy for end-group-resolved polymer analysis. Its applicability is demonstrated by monitoring the formation of cyclic structures by intramolecular transesterification and the degradation of an ester-terminated PLGA copolymer over time.

The block-length distribution (BLD) of polyamide and polyurethane copolymers was determined from mass spectrometry (MS) data. While nuclear magnetic resonance (NMR) traditionally determines the number-average block length, this work demonstrates that MS/MS is a viable option for the characterization of the distribution. An algorithm was implemented and modified to accurately determine copolymer BLDs from fragmentation data. The algorithm incorporates preferences in bond fragmentation. Evaluation of the algorithm encompassed the use of polyamide and polyurethane model systems. In both scenarios, the algorithm successfully derived BLDs, that corresponded well with the average block lengths obtained with ¹³C NMR. The newly developed algorithm enabled the characterization of BLDs based on MS/MS data. Typical trends in BLDs corresponding to distinct synthesis methods were discernible. These BLDs offer uniquely detailed insights into copolymer chemistry, potentially elucidating variations in functional properties of copolymers despite overall identical compositions.

The application of artificial intelligence (AI) and machine learning (ML) is rapidly expanding and has begun to make a significant impact on polymer development and characterization. This perspective article explores the current state of AI in this field and highlights areas where its potential remains underutilized. While the optimization of polymer synthesis to achieve desired properties and the classification of polymer types are well-established, opportunities for AI integration in detailed characterization, analytical method development, and data processing remain largely untapped. Greater automation of the analytical laboratory, whether through dedicated algorithms or AI-driven solutions, will enable analytical chemists to focus more on addressing research questions and interpreting results, rather than on method development and routine measurements.

Despite the high gain in peak capacity, online comprehensive two-dimensional liquid chromatography coupled with high-resolution mass spectrometry (LC × LC-HRMS) has not yet been widely applied to the analysis of complex protein digests. One reason is the method's reduced sensitivity which can be linked to the high flow rates of the second separation dimension (²D). This results in higher dilution factors and the need for flow splitters to couple to ESI-MS. This study reports proof-of-principle results of the development of an RPLC × RPLC-HRMS method using parallel gradients (²D flow rate of 0.7 mL min^-1) and its comparison to shifted gradient methods (²D of 1.4 mL min^-1) for the analysis of complex digests using HRMS (QExactive-Plus MS). Shifted and parallel gradients resulted in high surface coverage (SC) and effective peak capacity (SC of 0.6226 and 0.7439 and effective peak capacity of 779 and 757 in 60 min). When applied to a cell line digest sample, parallel gradients allowed higher sensitivity (e.g., average MS intensity increased by a factor of 3), allowing for a higher number of identifications (e.g., about 2600 vs 3900 peptides). In addition, reducing the modulation time to 10 s significantly increased the number of MS/MS events that could be performed. When compared to a 1D-RPLC method, parallel RPLC × RPLC-HRMS methods offered a higher separation performance (FHWH from 0.12 to 0.018 min) with limited sensitivity losses resulting in an increase of analyte identifications (e.g., about 6000 vs 7000 peptides and 1500 vs 1990 proteins).

Despite the high gain in peak capacity, online comprehensive two-dimensional liquid chromatography coupled with high-resolution mass spectrometry (LC×LC-HRMS) has not yet been widely applied to the analysis of complex protein digests. One reason is the methods' reduced sensitivity that can be linked to the high flow rates of the second separation dimension (2D ). This results in higher dilution factors and the need for flow splitters to couple to ESI-MS. This study reports proof of principle results of the development of an RPLC×RPLC-HRMS using parallel-gradients (2D flow rate of 0.7 mL min-1) and its comparison to shifted gradient methods (2D of 1.4 mL min-1) for the analysis of complex digests using a QExactive-Plus MS. Shifted and parallel-gradients resulted in high surface coverage (SC) and effective peak capacity (SC of 0.6226 and 0.7439 and an effective peak capacity of 779 and 757 in 60 minutes). When applied to a cell line digest sample, parallel-gradients allowed higher sensitivity (e.g., average MS intensity increased by a factor of 3), allowing for a higher number of identifications (e.g., about 2600 vs 3900 peptides). In addition, reducing the modulation time to 10s significantly increased the number of MS/MS events that could be performed. When compared to a 1D-RPLC method, parallel RPLC×RPLC-HRMS methods offered higher separation performance (FHWH from 0.12 to 0.018 min) with limited sensitivity losses resulting in an increase of analyte identifications (e.g. about 6000 vs 7000 peptides).

Sample transformation can be used to provide insights into the properties of complex samples.