Simple, affordable, and rapid methods for determining simultaneously molecular mass distributions and macromolecular compositions of polymeric materials are limited. Current approaches require extensive solvent consumption, linear calibrations, or expensive ionization sources. A simple method for determining average molecular weights, repeat units, and chain end groups of synthetic homopolymer standards was demonstrated using direct injection electrospray ionization mass spectrometry (ESI-MS) and an open-sourced charge deconvolution algorithm. Five commercial homopolymer standards ranging from 1-7 kDa were analyzed using direct injection ESI-MS on a quadrupole/time-of-flight (Q/ToF) instrument. Two poly(ethylene oxide) and two poly(styrene sulfonic acid) standards with narrow polydispersity, and one poly(D,L-alanine) standard with undefined polydispersity, were chosen to represent common challenges with ESI-MS quantitation. Using the UniDec algorithm, center of mass alignments and calculated number average molecular weights (Mn) were obtained from the charge deconvoluted spectra and compared to the reported chromatographic data of the standards. The average errors were within 10%, with larger discrepancies resulting from inconsistencies in the reported data (Mn, Mw, Mp, avg. MW). The method was able to quantify standards of narrow (PDI&lt;1.2) and broader (PDI&gt;1.2) polydispersity, providing an advantage over MALDI-MS analyses for ESI-compatible materials. Oligomeric resolution and chain end information was observed for all samples, and a clear distinction could be made between PSS standards that vary in average mass by one to two repeat units after optimizing processing parameters. Overall, the free, easy-to-use deconvolution algorithm provides a simple alternative to gaining polymer molecular weight plus compositional information without high solvent consumption, expensive ionization sources, or calibration curves.

Rapid and simple quantitation of average polymer molecular weight and composition via ESI-MS and a Bayesian universal charge deconvolution

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