基于UV-FAIMS的挥发性氯代苯检测方法

发布时间：2018-03-06 15:08

本文选题：氯代苯　切入点：挥发性有机污染物　出处：《光谱学与光谱分析》2017年10期 　论文类型：期刊论文

【摘要】：挥发性氯代苯是一类典型的环境有机污染物,离子迁移谱(IMS)是一种大气压下基于离子迁移率的快速现场检测技术。采用自制的真空紫外离子源-高场不对称波形离子迁移谱(UV-FAIMS),对空气中氯苯、对二氯苯、邻二氯苯、间二氯苯进行了检测。获取了不同分离电压条件下的CV-I谱图,并以此得到了四种物质的CV-DV指纹谱图。在指纹谱图支持下,设计了对二氯苯、邻二氯苯、间二氯苯三种同分异构体最优化分离检测参数,结果表明在800和1 000V的分离电压下,通过特征峰的选取,在补偿电压(CV)为20.4V(DV=800V),3.2V(DV=800V)和11.9V(DV=1 000V)处三种物质的混合物可以得到有效的分离识别。研究了流速对谱图峰位置和峰形的影响,得到不同流速下灵敏度和识别精度关系,为优化流量的选取提供了依据。在特征识别前提下考察了DV=450 V条件下,CV=4.3 V的对二氯苯特征峰的UVFAIMS检测限,结果高达0.05mg·m-3。该研究为卤代苯等多同分异构体苯类环境污染物的快速高精度检测提供了一种方法参考。
[Abstract]:Volatile chlorobenzene is a typical environmental organic pollutant. Ion Migration Spectroscopy (IMS) is a rapid in situ detection technique based on ion mobility at atmospheric pressure. A self-made vacuum ultraviolet ion source-high field asymmetric waveform ion transfer spectroscopy (UV-FAIMSN) is used to detect chlorobenzene, p-dichlorobenzene and o-dichlorobenzene in air. M-dichlorobenzene was detected. CV-I spectra of four substances were obtained under different separation voltages, and CV-DV fingerprints of four substances were obtained. Supported by fingerprint spectra, p-dichlorobenzene, o-dichlorobenzene, p-dichlorobenzene and o-dichlorobenzene were designed. The optimum separation parameters of three isomers of m-dichlorobenzene were obtained. The results showed that the characteristic peaks were selected at the separation voltages of 800 and 1000 V. The mixture of the three substances can be effectively separated and identified at a compensated voltage of 20.4V / V (800V / 3.2V / V) and 11.9V / V / 1 000 V). The effect of flow rate on the peak position and shape of the spectrum has been studied, and the relationship between sensitivity and recognition accuracy at different flow rates has been obtained. Under the condition of DV=450 V, the UVFAIMS detection limit of the characteristic peak of p dichlorobenzene was investigated under the condition of DV=450 V. The results were as high as 0.05mg 路m ~ (-3). This study provides a method for the rapid and accurate detection of benzene environmental pollutants such as halogenated benzene.
【作者单位】：中国科学院合肥智能机械研究所传感技术国家重点实验室;合肥工业大学机械工程学院;
【基金】：国家自然科学基金项目(61374016) 中国科学院青年创新促进会人才项目(2013213) 国家重点研发计划(2016YFC0201102)资助
【分类号】：O657;X831

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