A Short-Cut Data Mining Method for the Mass Spectrometric Characterization of Block Copolymers

Ákos Kuki,Tibor Nagy,Anna Nagy,Gergő Róth,Sándor Kéki,Miklós Zsuga

doi:10.3390/pr10010042

Abstract

A new data mining approach as a short cut method is given for the determination of the copolymer composition from mass spectra. Our method simplifies the copolymer mass spectra by reduction of the number of mass peaks. The proposed procedure, namely the selection of the mass peaks, which is based on the most abundant peak of the mass spectrum, can be performed manually or more efficiently using our recently invented Mass-remainder analysis (MARA). The considerable reduction of the MS spectra also simplifies the calculation of the copolymer quantities for instance the number- and weight-average molecular weights (Mn and Mw, respectively), polydispersity index (Đ = Mw/Mn), average molar fraction (cA) and weight fraction (wA) of the comonomer A and so on. These copolymer properties are in line with those calculated by a reference method taking into account all the mass peaks of the copolymer distribution. We also suggest a highly efficient method and template for the determination of the composition drift by processing the reduced mass spectra.

Highlights

For a thorough understanding of the influence of the copolymer structure on the copolymer properties, accurate and detailed characterization of the copolymer chains at molecular level is crucial
We developed the Mass-remainder analysis (MARA), which is capable of handling the mass spectra of copolymers, even those obtained by low-resolution mass spectrometer
Ethylene oxide (EO)—propylene oxide (PO)-based amphiphilic block copolymers—is widely used in the chemical industry. Their diverse applications emphasize the importance of the mass spectrometric characterization of these copolymers

Summary

Introduction

For a thorough understanding of the influence of the copolymer structure on the copolymer properties, accurate and detailed characterization of the copolymer chains at molecular level is crucial. Several data processing approaches and algorithms have been developed and implemented to determine the elemental composition of the numerous m/z peaks of the copolymer mass spectra [8,9,10,11,12,13]. Some issues are not yet completely resolved: (i) many of the methods merely assign the chemical composition to the mass spectral peaks; they do not or only partly compute the essential copolymer quantities (e.g., the averaged molecular weights of the repeat units, the composition drift [14] and so forth); (ii) the implementation of the mass spectral processing algorithms requires special skills or software development experts; (iii) the general algorithms are not capable of handling the specific copolymer systems

Methods

Results

Conclusion