Identification of common textile microplastics via autofluorescence spectroscopy coupled with k-means cluster analysis,Analyst

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Identification of common textile microplastics via autofluorescence spectroscopy coupled with k-means cluster analysis
Analyst ( IF 3.6 ) Pub Date : 2024-08-05 , DOI: 10.1039/d4an00658e
Marcus A Johns ₁ , Hongying Zhao ₂ , Mike Gattrell ₂ , James Lockhart ₂ , Emily D Cranston _{1,

3,

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Affiliation

Microplastics are an emerging anthropogenic pollutant risk with a significant body of research dedicated to understanding the implications further. To generate the databases required to characterize the impact of microplastics on our environment, and improve recovery and recycling of current plastic materials, we need rapid, in-line characterization that can distinguish individual polymer types. Here, autofluorescence spectroscopy was investigated as an alternative characterization method to the current leading techniques based on vibrational spectroscopy. It was confirmed that the autofluorescence of seven common textile polymers (acrylic, polyester, nylon, polyethylene, polypropylene, cellulose/cotton, wool) arose due to the cluster-triggered emission phenomenon. Both simulated polymer aging via photooxidation and dyeing of the polymers were found to affect the resultant autofluorescence spectra. A total of 1485 spectra from 39 unique sample groups (polymer type, colour, and degree of photooxidation) were analysed via machine learning (k-means cluster analysis). Correct identification of the polymer type was achieved in 71% of the cases from only eight input values (normalized intensity values at three autofluorescence emission wavelengths, the total autofluorescence emission intensity, the sample RGB colour values, and the sample shape). This represents a significant step towards automated polymer identification at the sub-second time scales required for the in-line characterization of microplastics.

中文翻译：

通过自发荧光光谱结合 k 均值聚类分析识别常见的纺织微塑料

微塑料是一种新兴的人为污染物风险，大量研究致力于进一步了解其影响。为了生成描述微塑料对环境影响所需的数据库，并改善当前塑料材料的回收和再循环，我们需要快速、在线的表征来区分各种聚合物类型。在此，研究了自发荧光光谱作为当前基于振动光谱的领先技术的替代表征方法。已证实七种常见纺织聚合物（丙烯酸、聚酯、尼龙、聚乙烯、聚丙烯、纤维素/棉、羊毛）的自发荧光是由于簇触发发射现象而产生的。发现通过光氧化模拟聚合物老化和聚合物染色都会影响所得的自发荧光光谱。通过机器学习（k 均值聚类分析）对来自 39 个独特样品组（聚合物类型、颜色和光氧化程度）的总共 1485 个光谱进行了分析。仅通过八个输入值（三个自发荧光发射波长的归一化强度值、总自发荧光发射强度、样品 RGB 颜色值和样品形状）就可以在 71% 的情况下正确识别聚合物类型。这代表着微塑料在线表征所需的亚秒级自动聚合物识别迈出了重要一步。

更新日期：2024-08-08

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