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Autex Research Journal

The Journal of Association of Universities for Textiles (AUTEX)

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Open Access
Online
ISSN
2300-0929
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Quick Detection of Aldehydes and Ketones in Automotive Textiles

Yanxue Ma
  • Key Laboratory of Textile Science & Technology, Ministry of Education, College of Textiles, Donghua University, Shanghai 201620, China
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Wenliang Xue
  • Corresponding author
  • Key Laboratory of Textile Science & Technology, Ministry of Education, College of Textiles, Donghua University, Shanghai 201620, China
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  • Other articles by this author:
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/ Mengyuan Wei / Jingfang Qian
Published Online: 2020-01-07 | DOI: https://doi.org/10.2478/aut-2019-0072

Abstract

This study was aimed to develop a quick detection method to test aldehydes and ketones in textiles in order to control the quality of automotive textiles in the development process from fabric production to end-use in vehicles. In this study, a pretreatment of samples was applied to simulate the actual environment of textiles used in vehicles. Collected volatiles were reacted with 2,4-dinitrophenylhydrazine and then eluted with acetonitrile tetrahydrofuran. The eluent was analyzed with high-performance liquid chromatography. Findings showed more than 90% volatiles could be detected in the established method; the lowest determination limit was 0.0297 mg/mL; and the lowest quantification limit was 0.0991 mg/mL, which meant sensitivity and capability of the method were high. Regression coefficients of linear models between volatile concentrations and chromatographic peak characteristics were >0.995, indicating that the method could effectively and efficiently determine the contents of volatiles in automotive textiles.

Keywords: Volatile organic compounds; automotive textiles; high-performance liquid chromatography; aldehydes; ketones

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About the article

Published Online: 2020-01-07


Citation Information: Autex Research Journal, ISSN (Online) 2300-0929, DOI: https://doi.org/10.2478/aut-2019-0072.

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© 2019 Yanxue Ma et al., published by Sciendo. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

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