Yttrium iron oxide embedded reduced graphene oxide: A trace level detection platform for carbamate pesticide in agricultural products

IF 7.8 2区 环境科学与生态学 Q1 ENGINEERING, CHEMICAL Process Safety and Environmental Protection Pub Date : 2025-02-07 DOI:10.1016/j.psep.2025.106875
Chandini Ragumoorthy , Nandini Nataraj , Shen-Ming Chen , G. Kiruthiga
{"title":"Yttrium iron oxide embedded reduced graphene oxide: A trace level detection platform for carbamate pesticide in agricultural products","authors":"Chandini Ragumoorthy ,&nbsp;Nandini Nataraj ,&nbsp;Shen-Ming Chen ,&nbsp;G. Kiruthiga","doi":"10.1016/j.psep.2025.106875","DOIUrl":null,"url":null,"abstract":"<div><div>Carbofuran (CRF) is an important pesticide that is widely used in agriculture to achieve economic benefits. However, its high toxicity raises serious concerns about food and agricultural safety. To address this issue, our study proposes an electrochemical sensor for the detection of the oxidized form of carbofuran by applying yttrium iron oxide (YFO) embedded reduced graphene oxide (rGO). The YFO-rGO composite was synthesized via hydrothermal and sonochemical methods and characterized for its structural integrity and electrochemical performances. The precise detection of CRF was facilitated through the application of voltammetry (CV) and amperometry (i-t) techniques. Notably, the resulting YFO-rGO/RDE sensor exhibited exceptional selectivity and sensitivity toward CRF under optimized conditions, with a lower detection limit of 0.018 µM with a broad linear range of about 0.0299–303.5 µM respectively. It also demonstrates consistent repeatability and reproducibility. Practical validation of the sensor was conducted through the analysis of actual food samples, confirming its efficacy in real-world agricultural settings.</div></div>","PeriodicalId":20743,"journal":{"name":"Process Safety and Environmental Protection","volume":"196 ","pages":"Article 106875"},"PeriodicalIF":7.8000,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Process Safety and Environmental Protection","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0957582025001429","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
引用次数: 0

Abstract

Carbofuran (CRF) is an important pesticide that is widely used in agriculture to achieve economic benefits. However, its high toxicity raises serious concerns about food and agricultural safety. To address this issue, our study proposes an electrochemical sensor for the detection of the oxidized form of carbofuran by applying yttrium iron oxide (YFO) embedded reduced graphene oxide (rGO). The YFO-rGO composite was synthesized via hydrothermal and sonochemical methods and characterized for its structural integrity and electrochemical performances. The precise detection of CRF was facilitated through the application of voltammetry (CV) and amperometry (i-t) techniques. Notably, the resulting YFO-rGO/RDE sensor exhibited exceptional selectivity and sensitivity toward CRF under optimized conditions, with a lower detection limit of 0.018 µM with a broad linear range of about 0.0299–303.5 µM respectively. It also demonstrates consistent repeatability and reproducibility. Practical validation of the sensor was conducted through the analysis of actual food samples, confirming its efficacy in real-world agricultural settings.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
氧化钇铁嵌入还原性氧化石墨烯:农产品中氨基甲酸酯类农药痕量水平检测平台
呋喃丹(CRF)是一种重要的农药,广泛应用于农业,以实现经济效益。然而,它的高毒性引起了人们对食品和农业安全的严重关注。为了解决这个问题,我们的研究提出了一种电化学传感器,通过应用钇氧化铁(YFO)嵌入还原氧化石墨烯(rGO)来检测氧化形式的呋喃。采用水热法和声化学法合成了YFO-rGO复合材料,并对其结构完整性和电化学性能进行了表征。通过伏安法(CV)和安培法(i-t)技术的应用,促进了CRF的精确检测。值得注意的是,在优化条件下,YFO-rGO/RDE传感器对CRF表现出优异的选择性和灵敏度,检测限较低,分别为0.018 µM,线性范围约为0.0299-303.5 µM。它还展示了一致的可重复性和再现性。通过对实际食品样品的分析,对传感器进行了实际验证,确认了其在实际农业环境中的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Process Safety and Environmental Protection
Process Safety and Environmental Protection 环境科学-工程:化工
CiteScore
11.40
自引率
15.40%
发文量
929
审稿时长
8.0 months
期刊介绍: The Process Safety and Environmental Protection (PSEP) journal is a leading international publication that focuses on the publication of high-quality, original research papers in the field of engineering, specifically those related to the safety of industrial processes and environmental protection. The journal encourages submissions that present new developments in safety and environmental aspects, particularly those that show how research findings can be applied in process engineering design and practice. PSEP is particularly interested in research that brings fresh perspectives to established engineering principles, identifies unsolved problems, or suggests directions for future research. The journal also values contributions that push the boundaries of traditional engineering and welcomes multidisciplinary papers. PSEP's articles are abstracted and indexed by a range of databases and services, which helps to ensure that the journal's research is accessible and recognized in the academic and professional communities. These databases include ANTE, Chemical Abstracts, Chemical Hazards in Industry, Current Contents, Elsevier Engineering Information database, Pascal Francis, Web of Science, Scopus, Engineering Information Database EnCompass LIT (Elsevier), and INSPEC. This wide coverage facilitates the dissemination of the journal's content to a global audience interested in process safety and environmental engineering.
期刊最新文献
Study on Wetting and Inhibition Performance and Mechanism of Hp-β-CD/TBHQ Composite Inhibitor on Spontaneous coal combustion Removal of polyethylene micro(nano)plastics from water: exploring the combination of anodic oxidation and membrane microfiltration Insight into the synergistic effects between pine sawdust and polyethylene during co-pyrolysis Synergistic Dust Suppression in Fully Mechanized Mining Faces: Integrating Negative Pressure Induction with Supersonic Atomization Interpretable machine learning for early multi-accident classification in CPR1000 reactors with global hyperparameter optimization and mechanism-informed explanations
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1