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Selective Profiling of Carboxylic Acid in Crude Oil by Halogen Labeling Combined with Liquid Chromatography and High-Resolution Mass Spectrometry

化学 质谱法 羧酸 色谱法 卤素 分辨率(逻辑) 原油 有机化学 烷基 计算机科学 石油工程 工程类 人工智能
作者
Khoa Huynh,Karen Louise Feilberg,Jonas Sundberg
出处
期刊:Journal of the American Society for Mass Spectrometry [American Chemical Society]
卷期号:35 (8): 1680-1691 被引量:3
标识
DOI:10.1021/jasms.4c00085
摘要

Carboxylic acids are a small but essential compound class within petroleum chemistry, influencing crude oil behaviors in production and processing and causing environmental impacts. Detailed structural information is fundamental to understanding their influence on petroleum characteristics. However, characterizing acids in crude oil remains challenging due to matrix effects, structural diversity, and low abundance. In this work, we present a new methodology for profiling carboxylic acids by liquid–liquid extraction and selective derivatization using 4-bromo-N-methylbenzylamine (4-BNMA) followed by liquid chromatography and electrospray ionization Orbitrap mass spectrometry (LC-ESI-Orbitrap MS). The fragmentation of 4-BNMA derivatives produces a unique product ion pair, m/z 169/171, enabling the identification of chromatographic fractions containing carboxylic acids. The mass spectra of the corresponding fractions are extracted, and the acids are further computationally isolated based on the isotopic pattern. The method was optimized and validated using acid standards and systematic experimental designs, assuring robustness and sensitivity for nontarget screening purposes. This method detected up to 380 carboxylic acids in six Danish North Sea crude oils, with up to two carboxyl and other heteroatom functionalities (NSO). The results indicated that the most populated species are fatty acids (double bond equivalent (DBE) = 1) and small aromatic acids (DBE = 2–6). The predominance and diversities of compound classes in different samples are consistent with their corresponding bulk properties. Polyfunctional acids (Ox, NxOx, and SxOx) were observed due to exposure to oxidation and biodegradation. Also, the approach’s applicability benefits high-resolution MS analysis by simplifying data processing for crude oil and potentially other high-organic and aqueous samples.
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