Yaoli Zhao, Patartri Chakraborty, Zixia Meng, Asalatha Nair Syamala Amma, Amit Goyal, Thomas Thundat
{"title":"利用对峙光热光谱技术表征塑料废物的分子特性","authors":"Yaoli Zhao, Patartri Chakraborty, Zixia Meng, Asalatha Nair Syamala Amma, Amit Goyal, Thomas Thundat","doi":"10.1149/2754-2726/acfb92","DOIUrl":null,"url":null,"abstract":"An accurate molecular identification of plastic waste is important in increasing the efficacy of automatic plastic sorting in recycling. However, identification of real-world plastic waste, according to their resin identification code, remains challenging due to the lack of techniques that can provide high molecular selectivity. In this study, a standoff photothermal spectroscopy technique, utilizing a microcantilever, was used for acquiring mid-infrared spectra of real-world plastic waste, including those with additives, surface contaminants, and mixed plastics. Analysis of the standoff spectral data, using Convolutional Neural Network (CNN), showed 100% accuracy in selectively identifying real-world plastic waste according to their respective resin identification codes. Standoff photothermal spectroscopy, together with CNN analysis, offers a promising approach for the selective characterization of waste plastics in Material Recovery Facilities (MRFs).","PeriodicalId":72870,"journal":{"name":"ECS sensors plus","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Molecular Characterization of Plastic Waste Using Standoff Photothermal Spectroscopy\",\"authors\":\"Yaoli Zhao, Patartri Chakraborty, Zixia Meng, Asalatha Nair Syamala Amma, Amit Goyal, Thomas Thundat\",\"doi\":\"10.1149/2754-2726/acfb92\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An accurate molecular identification of plastic waste is important in increasing the efficacy of automatic plastic sorting in recycling. However, identification of real-world plastic waste, according to their resin identification code, remains challenging due to the lack of techniques that can provide high molecular selectivity. In this study, a standoff photothermal spectroscopy technique, utilizing a microcantilever, was used for acquiring mid-infrared spectra of real-world plastic waste, including those with additives, surface contaminants, and mixed plastics. Analysis of the standoff spectral data, using Convolutional Neural Network (CNN), showed 100% accuracy in selectively identifying real-world plastic waste according to their respective resin identification codes. Standoff photothermal spectroscopy, together with CNN analysis, offers a promising approach for the selective characterization of waste plastics in Material Recovery Facilities (MRFs).\",\"PeriodicalId\":72870,\"journal\":{\"name\":\"ECS sensors plus\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-10-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ECS sensors plus\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1149/2754-2726/acfb92\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ECS sensors plus","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1149/2754-2726/acfb92","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Molecular Characterization of Plastic Waste Using Standoff Photothermal Spectroscopy
An accurate molecular identification of plastic waste is important in increasing the efficacy of automatic plastic sorting in recycling. However, identification of real-world plastic waste, according to their resin identification code, remains challenging due to the lack of techniques that can provide high molecular selectivity. In this study, a standoff photothermal spectroscopy technique, utilizing a microcantilever, was used for acquiring mid-infrared spectra of real-world plastic waste, including those with additives, surface contaminants, and mixed plastics. Analysis of the standoff spectral data, using Convolutional Neural Network (CNN), showed 100% accuracy in selectively identifying real-world plastic waste according to their respective resin identification codes. Standoff photothermal spectroscopy, together with CNN analysis, offers a promising approach for the selective characterization of waste plastics in Material Recovery Facilities (MRFs).