A Rapid Derivatization for Quantitation of Perfluorinated Carboxylic Acids from Aqueous Matrices by Gas Chromatography–Mass Spectrometry

化学 衍生化 超纯水 色谱法 检出限 分析物 水溶液 质谱法 萃取(化学) 气相色谱-质谱法 气相色谱法 重氮甲烷 样品制备 自来水 有机化学 材料科学 环境工程 工程类 纳米技术
作者
RenXi Ye,Robert A. Di Lorenzo,Jessica T. Clouthier,Cora J. Young,Trevor C. VandenBoer
出处
期刊:Analytical Chemistry [American Chemical Society]
卷期号:95 (19): 7648-7655 被引量:8
标识
DOI:10.1021/acs.analchem.3c00593
摘要

Ultrashort-chain perfluorinated carboxylic acids (PFCAs) are receiving more attention due to their ever-increasing presence in the environment. Methods have been established for the analysis of short- and long-chain PFCAs, while robust quantitation of ultrashort-chain species is scarce. Here, we develop a novel derivatization method using diphenyl diazomethane for quantitation of C2-C14 PFCAs in aqueous matrices. The method is highlighted by rapid completion of derivatization (<1 min) and retention and separation of ultrashort-chain (C2/C3) PFCA derivatives using H2 carrier gas (R > 1.5). A weak anion exchange solid-phase extraction procedure for analyte recovery from representative aqueous samples was developed and validated by spike and recovery from ultrapure water, synthetic ocean water, and simulated denuder extracts used for collecting gaseous PFCAs. Recoveries for PFCAs ranged from 83 to 130% for the majority of analytes and matrices. The instrument detection limits (IDLs) range from 8 to 220 fg per injection, and method detection limits (MDLs) range from 0.06 to 14.6 pg/mL for 500 mL aqueous samples, which are within an order of magnitude to conventional LC-MS/MS methods. The method was applied to the analysis of real samples of tap water, rainwater, ocean water, and annular denuder extracts. The overall method provides a cost-effective alternative to conventional LC-MS/MS methods, overcoming the typical GC-MS drawbacks of high detection limits and long sample preparation times while being able to simultaneously analyze the complete spectrum of environmentally relevant PFCAs.
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