Guojian Wu, Chenxing Du, Chuanyi Peng, Zitong Qiu, Si Li, Wenjuan Chen, Huimin Qiu, Zhi Zheng, Zhiwei Lu, Yizhong Shen
{"title":"Machine learning-assisted laccase-like activity nanozyme for intelligently onsite real-time and dynamic analysis of pyrethroid pesticides","authors":"Guojian Wu, Chenxing Du, Chuanyi Peng, Zitong Qiu, Si Li, Wenjuan Chen, Huimin Qiu, Zhi Zheng, Zhiwei Lu, Yizhong Shen","doi":"10.1016/j.jhazmat.2024.136015","DOIUrl":null,"url":null,"abstract":"The intelligently efficient, reliable, economical and portable onsite assay toward pyrethroid pesticides (PPs) residues is critical for food safety analysis and environmental pollution traceability. Here, a fluorescent nanozyme Cu-ATP@[Ru(bpy)<sub>3</sub>]<sup>2+</sup> with laccase-like activity was designed to develop a versatile machine learning-assisted colorimetric and fluorescence dual-modal assay for efficient onsite intelligent decision recognition and quantification of PPs residues. In the presence of alkaline phosphatase (ALP), the laccase-like activity of Cu-ATP@[Ru(bpy)<sub>3</sub>]<sup>2+</sup> was enhanced to oxidize colorless <em>o</em>-phenylenediamine (OPD) into dark-yellow 2,3-diaminophenazine (DAP) via electron transfer, appearing a new yellow fluorescence at 550<!-- --> <!-- -->nm. Meanwhile, the red fluorescence of Cu-ATP@[Ru(bpy)<sub>3</sub>]<sup>2+</sup> at 600<!-- --> <!-- -->nm was quenched due to the internal filter effect (IFE) of DAP towards Cu-ATP@[Ru(bpy)<sub>3</sub>]<sup>2+</sup>. However, the selective inhibition of PPs toward ALP activity enabled to observe a dual-modal response of PPs concentration-dependent decrease in colorimetric signal and enhancement in the fluorescence intensity ratio of <em>F</em><sub>600 nm</sub>/<em>F</em><sub>550 nm</sub>. On this basis, both the colorimetric and fluorescence images were captured and processed with a home-made WeChat applet-installed smartphone to extract the corresponding image color information, thus achieving machine learning-assisted onsite real-time and dynamic intelligent decision recognition and quantification of PPs residues in real samples, which shows a promising potential in safeguarding food safety and environmental health.","PeriodicalId":361,"journal":{"name":"Journal of Hazardous Materials","volume":null,"pages":null},"PeriodicalIF":12.2000,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Hazardous Materials","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1016/j.jhazmat.2024.136015","RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ENVIRONMENTAL","Score":null,"Total":0}
引用次数: 0
Abstract
The intelligently efficient, reliable, economical and portable onsite assay toward pyrethroid pesticides (PPs) residues is critical for food safety analysis and environmental pollution traceability. Here, a fluorescent nanozyme Cu-ATP@[Ru(bpy)3]2+ with laccase-like activity was designed to develop a versatile machine learning-assisted colorimetric and fluorescence dual-modal assay for efficient onsite intelligent decision recognition and quantification of PPs residues. In the presence of alkaline phosphatase (ALP), the laccase-like activity of Cu-ATP@[Ru(bpy)3]2+ was enhanced to oxidize colorless o-phenylenediamine (OPD) into dark-yellow 2,3-diaminophenazine (DAP) via electron transfer, appearing a new yellow fluorescence at 550 nm. Meanwhile, the red fluorescence of Cu-ATP@[Ru(bpy)3]2+ at 600 nm was quenched due to the internal filter effect (IFE) of DAP towards Cu-ATP@[Ru(bpy)3]2+. However, the selective inhibition of PPs toward ALP activity enabled to observe a dual-modal response of PPs concentration-dependent decrease in colorimetric signal and enhancement in the fluorescence intensity ratio of F600 nm/F550 nm. On this basis, both the colorimetric and fluorescence images were captured and processed with a home-made WeChat applet-installed smartphone to extract the corresponding image color information, thus achieving machine learning-assisted onsite real-time and dynamic intelligent decision recognition and quantification of PPs residues in real samples, which shows a promising potential in safeguarding food safety and environmental health.
期刊介绍:
The Journal of Hazardous Materials serves as a global platform for promoting cutting-edge research in the field of Environmental Science and Engineering. Our publication features a wide range of articles, including full-length research papers, review articles, and perspectives, with the aim of enhancing our understanding of the dangers and risks associated with various materials concerning public health and the environment. It is important to note that the term "environmental contaminants" refers specifically to substances that pose hazardous effects through contamination, while excluding those that do not have such impacts on the environment or human health. Moreover, we emphasize the distinction between wastes and hazardous materials in order to provide further clarity on the scope of the journal. We have a keen interest in exploring specific compounds and microbial agents that have adverse effects on the environment.