基于显微拉曼面扫的小尺寸微塑料检测方法

刘丹童, 宋洋, 李菲菲, 陈吕军

中国环境科学 ›› 2020, Vol. 40 ›› Issue (10) : 4429-4438.

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中国环境科学 ›› 2020, Vol. 40 ›› Issue (10) : 4429-4438.
固体废物

基于显微拉曼面扫的小尺寸微塑料检测方法

  • 刘丹童, 宋洋, 李菲菲, 陈吕军
作者信息 +

A detection method of small-sized microplastics based on micro-Raman mapping

  • LIU Dan-tong, SONG Yang, LI Fei-fei, CHEN Lyu-jun
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文章历史 +

摘要

由于检测方法的限制,小尺寸(<50 μm)微塑料的检测研究较少.提出一种无须挑选、能够较为准确高效识别小尺寸微塑料的方法.筛选出2种合适的过滤膜作为分离和分析微塑料所用基底,使用显微拉曼光谱的面扫模式对5种常见的微塑料进行定性和定量检测.结果表明,该方法能够将最小粒径为1μm的小尺寸微塑料从滤膜背景上识别出来,并作伪彩色图直观显示微塑料的尺寸、形状和种类.以石英砂颗粒模拟环境样品中的杂质,该方法也可排除杂质的影响,准确定位微塑料.在3种浓度梯度下,不同粒径(5~50μm)的聚苯乙烯微塑料回收率为33.3%~79.0%.

Abstract

Due to the limitation of detection methods, there are few studies on the detection involved small-sized microplastics (<50μm). A method that can identify small-sized microplastics more accurately and efficiently without manual selection was proposed. Two kinds of suitable membranes were screened out as substrates for the separation and analysis of microplastics. The mapping mode of micro-Raman spectra was used to qualitatively and quantitatively detect five types of common microplastics. The results showed that this method can identify small-sized microplastics with the minimum particle size of 1μm from the background of the filter membrane and make pseudo color images to visualize the size, shape and type of microplastics. By simulating the impurities in the environmental samples with quartz sand particles, the method can also eliminate the influence of impurities and accurately locate the microplastics. The recovery rate of polystyrene microplastics with different particle sizes (5~50μm) was ranged from 33.3% to 79.0% under three concentration gradients.

关键词

可视化 / 面扫模式 / 微塑料 / 显微拉曼光谱 / 小尺寸

Key words

mapping / microplastics / micro-Raman spectra / small-sized / visualization

引用本文

导出引用
刘丹童, 宋洋, 李菲菲, 陈吕军. 基于显微拉曼面扫的小尺寸微塑料检测方法[J]. 中国环境科学. 2020, 40(10): 4429-4438
LIU Dan-tong, SONG Yang, LI Fei-fei, CHEN Lyu-jun. A detection method of small-sized microplastics based on micro-Raman mapping[J]. China Environmental Science. 2020, 40(10): 4429-4438
中图分类号: X132   

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