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基于氟化表面处理对单纤维进行分类。

Classifying single fibers based on fluorinated surface treatments.

作者信息

Dolan Michael J, Blackledge Robert D, Jorabchi Kaveh

机构信息

Department of Chemistry, Georgetown University, Washington, DC, USA.

, San Diego, USA.

出版信息

Anal Bioanal Chem. 2019 Jul;411(19):4775-4784. doi: 10.1007/s00216-019-01596-6. Epub 2019 Feb 14.

Abstract

Fibers are an important form of forensic evidence, but their evidential value can be severely limited when the identified characteristics of the fibers are common, such as blue cotton. Detecting chemical fiber treatments offers an avenue to further classify fibers and to improve their evidential value. In this report, we investigate the potential of fluoropolymer fiber coatings, used to impart oil and water-repellent properties in fabrics, for differentiating between fibers. The thin nature of these fiber surface modifications creates an analytical challenge for their detection on a single fiber, a typical sample size for forensic evidence. Specifically, pyrolysis-gas chromatography-mass spectrometry (py-GC-MS) has shown promising selectivity but the sensitivity of the method is not adequate for single-fiber analysis of fluorinated coatings. To overcome this challenge, we utilize a newly developed elemental ionization source, plasma-assisted reaction chemical ionization (PARCI). The high sensitivity of py-GC-PARCI-MS for elemental fluorine analysis offers selective and sensitive detection of fluorinated pyrolysates among the non-fluorinated pyrolysates of the fiber core. As a result, fluoropolymer coatings are detected from 10-mm single-fiber samples. The technique is applied for classification of 22 fiber types, resulting in 4 distinct groups via hierarchical cluster analysis based on similarity of fluorine pyrograms. These results present the first study to classify fibers based on fluorinated coatings, and highlight the potential of py-GC-PARCI-MS for forensic analyses. Graphical Abstract ᅟ.

摘要

纤维是法医证据的一种重要形式,但其证据价值在纤维的识别特征很常见时可能会受到严重限制,例如蓝色棉纤维。检测化学纤维处理方法为进一步对纤维进行分类并提高其证据价值提供了一条途径。在本报告中,我们研究了用于赋予织物拒油拒水性能的含氟聚合物纤维涂层在区分纤维方面的潜力。这些纤维表面改性很薄,这给在单根纤维(法医证据的典型样本大小)上检测它们带来了分析挑战。具体而言,热解-气相色谱-质谱联用技术(py-GC-MS)已显示出有前景的选择性,但该方法的灵敏度不足以对含氟涂层进行单纤维分析。为克服这一挑战,我们采用了一种新开发的元素电离源,即等离子体辅助反应化学电离(PARCI)。py-GC-PARCI-MS对元素氟分析的高灵敏度使得能够在纤维芯的非氟化热解产物中选择性且灵敏地检测氟化热解产物。结果,从10毫米的单纤维样本中检测到了含氟聚合物涂层。该技术被应用于对22种纤维类型进行分类,通过基于氟热解图谱相似性的层次聚类分析得到了4个不同的组。这些结果展示了第一项基于氟化涂层对纤维进行分类的研究,并突出了py-GC-PARCI-MS在法医分析中的潜力。图形摘要ᅟ 。

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