Automated Feature Mining for Two-Dimensional Liquid Chromatography Applied to Polymers Enabled by Mass Remainder Analysis

A fast algorithm for automated feature mining of synthetic (industrial) homopolymers or perfectly alternating copolymers was developed. Comprehensive two-dimensional liquid chromatography-mass spectrometry data (LC × LC-MS) was utilized, undergoing four distinct parts within the algorithm. Initially, the data is reduced by selecting regions of interest within the data. Then, all regions of interest are clustered on the time and mass-to-charge domain to obtain isotopic distributions. Afterward, single-value clusters and background signals are removed from the data structure. In the second part of the algorithm, the isotopic distributions are employed to define the charge state of the polymeric units and the charge-state reduced masses of the units are calculated. In the third part, the mass of the repeating unit (i.e., the monomer) is automatically selected by comparing all mass differences within the data structure. Using the mass of the repeating unit, mass remainder analysis can be performed on the data. This results in groups sharing the same end-group compositions. Lastly, combining information from the clustering step in the first part and the mass remainder analysis results in the creation of compositional series, which are mapped on the chromatogram. Series with similar chromatographic behavior are separated in the mass-remainder domain, whereas series with an overlapping mass remainder are separated in the chromatographic domain. These series were extracted within a calculation time of 3 min. The false positives were then assessed within a reasonable time. The algorithm is verified with LC × LC-MS data of an industrial hexahydrophthalic anhydride-derivatized propylene glycol-terephthalic acid copolyester. Afterward, a chemical structure proposal has been made for each compositional series found within the data

Composition mapping of highly substituted cellulose-ether monomers by liquid chromatography–mass spectrometry and probability-based data deconvolution

Cellulose ethers (CEs) are semi-synthetic polymers produced by derivatization of natural cellulose, yielding highly substituted products such as ethyl hydroxyethyl cellulose (EHEC) or methyl ethyl hydroxyethyl cellulose (MEHEC). CEs are commonly applied as pharmaceutical excipients and thickening agents in paints and drymix mortars. CE properties, such as high viscosity in solution, solubility, and bio-stability are of high interest to achieve required product qualities, which may be strongly affected by the substitution pattern obtained after derivatization. The average and molar degree of substitution often cannot explain functional differences observed among CE batches, and more in-depth analysis is needed. In this work, a new method was developed for the comprehensive mapping of the substitution degree and composition of β-glucose monomers of CE samples. To this end, CEs were acid-hydrolyzed and then analyzed by gradient reversed-phase liquid chromatography-mass spectrometry (LC-MS) using an acid-stable LC column and time-of-flight (TOF) mass spectrometer. LC-MS provided monomer resolution based on ethylene oxide, hydroxyl, and terminating methyl/ethyl content, allowing the assignment of detailed compositional distributions. An essential further distinction of constitutional isomer distributions was achieved using an in-house developed probability-based deconvolution algorithm. Aided by differential heat maps for visualization and straightforward interpretation of the measured LC-MS data, compositional variation between bio-stable and non-bio-stable CEs could be identified using this new approach. Moreover, it disclosed unexpected methylations in EHEC samples. Overall, the obtained molecular information on relevant CE samples demonstrated the method's potential for the study of CE structure-property relationships.</p