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通过掺杂剂辅助大气压化学电离的热解气相色谱-飞行时间质谱法对聚氨酯单体进行结构分析

Structural Analysis of Polyurethane Monomers by Pyrolysis GC TOFMS via Dopant-Assisted Atmospheric Pressure Chemical Ionization.

作者信息

Larson Evan A, Lee Junghyun, Paulson Andrew, Lee Young Jin

机构信息

Department of Chemistry, Iowa State University, Ames, IA, 50011, USA.

Materials Technology & Analysis Team, Hyundai Motor Company, Hwaseong-Si, Gyeonggi-do, 18280, South Korea.

出版信息

J Am Soc Mass Spectrom. 2019 Jun;30(6):1046-1058. doi: 10.1007/s13361-019-02165-y. Epub 2019 Apr 9.

Abstract

Polyurethane is one of the most widely used copolymers and is formed by the cross-linking of isocyanates and polyols. Its physical properties have a strong dependence on the monomer structures, making it very important to characterize the monomers in polyurethane. In this study, we developed a method to analyze unknown polyurethane samples using pyrolysis gas chromatography time-of-flight mass spectrometry (Py-GC-TOFMS) with dopant-assisted atmospheric pressure chemical ionization (dAPCI). A set of standard polyurethane foams produced with several different monomers are analyzed by Py-GC-TOFMS. GC-dAPCI-TOFMS is a high-resolution, soft ionization method for GC-MS analysis that provides accurate mass information of GC separated molecules. The data obtained by this approach could effectively classify different monomers using principal component analysis (PCA), grouping polymers with the same monomers, and providing structural features significant to each monomer. Furthermore, characteristic compounds are identified using in-source collision-induced dissociation (CID) and CSI:FingerID analysis. In contrast, the same set of samples analyzed by Py-GC-electron ionization (EI)-MS could only partially separate some of the monomers. Graphical Abstract .

摘要

聚氨酯是应用最为广泛的共聚物之一,由异氰酸酯和多元醇交联而成。其物理性质强烈依赖于单体结构,因此表征聚氨酯中的单体非常重要。在本研究中,我们开发了一种方法,使用掺杂剂辅助大气压化学电离(dAPCI)的热解气相色谱-飞行时间质谱(Py-GC-TOFMS)来分析未知的聚氨酯样品。通过Py-GC-TOFMS分析了一组由几种不同单体生产的标准聚氨酯泡沫。GC-dAPCI-TOFMS是一种用于GC-MS分析的高分辨率、软电离方法,可提供GC分离分子的精确质量信息。通过这种方法获得的数据可以使用主成分分析(PCA)有效地对不同单体进行分类,将具有相同单体的聚合物分组,并提供对每个单体具有重要意义的结构特征。此外,使用源内碰撞诱导解离(CID)和CSI:FingerID分析来鉴定特征化合物。相比之下,通过Py-GC-电子电离(EI)-MS分析同一组样品只能部分分离一些单体。图形摘要 。

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