Menglun Zhang, Xi Zhang, Pengfei Niu, Tao Shen, Yi Yuan, Yuantao Bai, Zhilin Wang
{"title":"用于分布式监测水中重金属离子的现场低功率传感节点","authors":"Menglun Zhang, Xi Zhang, Pengfei Niu, Tao Shen, Yi Yuan, Yuantao Bai, Zhilin Wang","doi":"10.1063/10.0003511","DOIUrl":null,"url":null,"abstract":"Heavy metal pollution in water environments poses a great threat to public health and to the ecological environment due to its high toxicity and non-degradability. However, many existing detection methods require laboratory-based bulky instruments and time-consuming manual operations. Although some on-site systems exist, they are difficult to deploy on a large scale owing to their large size and high cost. Here, we report a sensing node featuring low power consumption and low cost, achieved by integrating microsensor, microfluidic, and electronic modules into a compact size for automatic and scalable heavy metal pollution monitoring. Digital microfluidic and electrochemical sensing modules are integrated on a chip, thereby combining the procedures of sample pretreatment, electrochemical sensing, and waste removal for automatic and continuous monitoring. The feasibility of the platform is demonstrated by Pb2+ detection in tap water. With a 3500 mA·h battery, the compact sensing node could work for several years in principle. There is scope for further improvements to the system in terms of wider functionality and reductions in size, power consumption, and cost. The sensing node presented here is a strong candidate for distributed monitoring of water quality as an Internet-of-Things application.","PeriodicalId":35428,"journal":{"name":"Nami Jishu yu Jingmi Gongcheng/Nanotechnology and Precision Engineering","volume":"4 1","pages":"013005"},"PeriodicalIF":3.5000,"publicationDate":"2021-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1063/10.0003511","citationCount":"5","resultStr":"{\"title\":\"On-site low-power sensing nodes for distributed monitoring of heavy metal ions in water\",\"authors\":\"Menglun Zhang, Xi Zhang, Pengfei Niu, Tao Shen, Yi Yuan, Yuantao Bai, Zhilin Wang\",\"doi\":\"10.1063/10.0003511\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Heavy metal pollution in water environments poses a great threat to public health and to the ecological environment due to its high toxicity and non-degradability. However, many existing detection methods require laboratory-based bulky instruments and time-consuming manual operations. Although some on-site systems exist, they are difficult to deploy on a large scale owing to their large size and high cost. Here, we report a sensing node featuring low power consumption and low cost, achieved by integrating microsensor, microfluidic, and electronic modules into a compact size for automatic and scalable heavy metal pollution monitoring. Digital microfluidic and electrochemical sensing modules are integrated on a chip, thereby combining the procedures of sample pretreatment, electrochemical sensing, and waste removal for automatic and continuous monitoring. The feasibility of the platform is demonstrated by Pb2+ detection in tap water. With a 3500 mA·h battery, the compact sensing node could work for several years in principle. There is scope for further improvements to the system in terms of wider functionality and reductions in size, power consumption, and cost. The sensing node presented here is a strong candidate for distributed monitoring of water quality as an Internet-of-Things application.\",\"PeriodicalId\":35428,\"journal\":{\"name\":\"Nami Jishu yu Jingmi Gongcheng/Nanotechnology and Precision Engineering\",\"volume\":\"4 1\",\"pages\":\"013005\"},\"PeriodicalIF\":3.5000,\"publicationDate\":\"2021-02-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1063/10.0003511\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nami Jishu yu Jingmi Gongcheng/Nanotechnology and Precision Engineering\",\"FirstCategoryId\":\"1087\",\"ListUrlMain\":\"https://doi.org/10.1063/10.0003511\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nami Jishu yu Jingmi Gongcheng/Nanotechnology and Precision Engineering","FirstCategoryId":"1087","ListUrlMain":"https://doi.org/10.1063/10.0003511","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
On-site low-power sensing nodes for distributed monitoring of heavy metal ions in water
Heavy metal pollution in water environments poses a great threat to public health and to the ecological environment due to its high toxicity and non-degradability. However, many existing detection methods require laboratory-based bulky instruments and time-consuming manual operations. Although some on-site systems exist, they are difficult to deploy on a large scale owing to their large size and high cost. Here, we report a sensing node featuring low power consumption and low cost, achieved by integrating microsensor, microfluidic, and electronic modules into a compact size for automatic and scalable heavy metal pollution monitoring. Digital microfluidic and electrochemical sensing modules are integrated on a chip, thereby combining the procedures of sample pretreatment, electrochemical sensing, and waste removal for automatic and continuous monitoring. The feasibility of the platform is demonstrated by Pb2+ detection in tap water. With a 3500 mA·h battery, the compact sensing node could work for several years in principle. There is scope for further improvements to the system in terms of wider functionality and reductions in size, power consumption, and cost. The sensing node presented here is a strong candidate for distributed monitoring of water quality as an Internet-of-Things application.