Department of Chemical Science and Engineering, School of Materials and Chemical Technology, Tokyo Institute of Technology, 2-12-1-H121 Ookayama, Meguro-ku, Tokyo 152-8550, Japan.
Precursory Research for Embryonic Science and Technology, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi-shi, Saitama 332-0012, Japan.
ACS Appl Mater Interfaces. 2021 Dec 1;13(47):55978-55987. doi: 10.1021/acsami.1c11794. Epub 2021 Nov 4.
The pollution of water environments is a worldwide concern. Not only marine pollution by plastic litter, including microplastics, but also the spillage of water-soluble synthetic polymers in wastewater have recently gained increasing attention due to their potential risks to soil and water environments. However, conventional methods to identify polymers dissolved in water are laborious and time-consuming. Here, we propose a simple approach to identify synthetic polymers dissolved in water using a peptide-based molecular sensor with a fluorophore unit. Supervised machine learning of multiple fluorescence signals from the sensor, which specifically or nonspecifically interacted with the polymers, was applied for polymer classification as a proof of principle demonstration. Aqueous solutions containing different polymers or multiple polymer species with different mixture ratios were identified successfully. We found that fluorophore-introduced biomolecular sensors have great potential to provide discriminative information regarding water-soluble polymers. Our approach based on the discrimination of multiple optical signals of water-soluble polymers from peptide-based molecular sensors through machine learning will be applicable to next-generation sensing systems for polymers in wastewater or natural environments.
水环境的污染是一个全球性的问题。不仅是海洋受到塑料垃圾（包括微塑料）的污染，而且由于水溶性合成聚合物在废水中的泄漏，它们对土壤和水环境的潜在风险也引起了越来越多的关注。然而，传统的识别水中聚合物的方法既繁琐又耗时。在这里，我们提出了一种使用基于肽的分子传感器和荧光团单元识别水中溶解的合成聚合物的简单方法。通过对传感器与聚合物特异性或非特异性相互作用的多个荧光信号进行监督机器学习，将其作为原理验证进行了聚合物分类。成功识别了含有不同聚合物的水溶液或具有不同混合比的多种聚合物种类。我们发现，引入荧光团的生物分子传感器在提供有关水溶性聚合物的有区别信息方面具有很大的潜力。我们的方法基于通过机器学习从基于肽的分子传感器对水溶性聚合物的多个光学信号进行区分，将适用于废水中或自然环境中聚合物的下一代传感系统。
