Ronnie Concepcion II , Bernardo Duarte , Maria Gemel Palconit , Jonah Jahara Baun , Argel Bandala , Ryan Rhay Vicerra , Elmer Dadios
{"title":"聚氯乙烯和羊皮纸带丝网印刷石墨电极与遗传程序集成用于水培池塘水的原位硝酸盐传感","authors":"Ronnie Concepcion II , Bernardo Duarte , Maria Gemel Palconit , Jonah Jahara Baun , Argel Bandala , Ryan Rhay Vicerra , Elmer Dadios","doi":"10.1016/j.inpa.2023.02.002","DOIUrl":null,"url":null,"abstract":"<div><p>Nitrate is the primary water-soluble macronutrient essential for plant growth that is converted from excess fish feeds, fish effluents, and degrading biomaterials on the aquaponic pond floor, and when aquacultural malpractices occur, large amounts of it retain in the water system causing increase rate in eutrophication and toxifies fish and aquaculture plants. Recent nitrate sensor prototypes still require performing the additional steps of water sample deionization and dilution and were constructed with expensive materials. In response to the challenge of sensor enhancement and aquaponic water quality monitoring, this study developed sensitive, repeatable, and reproducible screen-printed graphite electrodes on polyvinyl chloride and parchment paper substrates with silver as electrode material and 60:40 graphite powder:nail polish formulated conductive ink for electrical traces, integrated with 9-gene genetic expression model as a function of peak anodic current and electrochemical test time for nitrate concentration prediction that is embedded into low-power Arduino ESP32 for in situ nitrate sensing in aquaponic pond water. Five SPE electrical traces were designed on the two types of substrates. Scanning electron microscopy with energy dispersive X-ray confirmed the electrode surface morphology. Electrochemical cyclic voltammetry using 10 to 100 mg/L KNO<sub>3</sub> and water from three-depth regions of the actual pond established the electrochemical test time (10.5 s) and electrode potential (0.135 V) protocol necessary to produce peak current that corresponds to the strength of nitrate ions during redox. The findings from in situ testing revealed that the proposed sensors have strong linear predictions (R<sup>2</sup> = 0.968 MSE = 1.659 for nSPEv and R<sup>2</sup> = 0.966 MSE = 4.697 for nSPEp) in the range of 10 to 100 mg/L and best detection limit of 3.15 μg/L, which are comparable to other sensors of more complex construction. The developed three-electrode electrochemical nitrate sensor confirms that it is reliable for both biosensing in controlled solutions and in situ aquaponic pond water systems.</p></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"11 2","pages":"Pages 187-201"},"PeriodicalIF":7.7000,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214317323000124/pdfft?md5=1c102ba0ae00d448d4cc3c7accdbca99&pid=1-s2.0-S2214317323000124-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Screen-printed graphite electrode on polyvinyl chloride and parchment strips integrated with genetic programming for in situ nitrate sensing of aquaponic pond water\",\"authors\":\"Ronnie Concepcion II , Bernardo Duarte , Maria Gemel Palconit , Jonah Jahara Baun , Argel Bandala , Ryan Rhay Vicerra , Elmer Dadios\",\"doi\":\"10.1016/j.inpa.2023.02.002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Nitrate is the primary water-soluble macronutrient essential for plant growth that is converted from excess fish feeds, fish effluents, and degrading biomaterials on the aquaponic pond floor, and when aquacultural malpractices occur, large amounts of it retain in the water system causing increase rate in eutrophication and toxifies fish and aquaculture plants. Recent nitrate sensor prototypes still require performing the additional steps of water sample deionization and dilution and were constructed with expensive materials. In response to the challenge of sensor enhancement and aquaponic water quality monitoring, this study developed sensitive, repeatable, and reproducible screen-printed graphite electrodes on polyvinyl chloride and parchment paper substrates with silver as electrode material and 60:40 graphite powder:nail polish formulated conductive ink for electrical traces, integrated with 9-gene genetic expression model as a function of peak anodic current and electrochemical test time for nitrate concentration prediction that is embedded into low-power Arduino ESP32 for in situ nitrate sensing in aquaponic pond water. Five SPE electrical traces were designed on the two types of substrates. Scanning electron microscopy with energy dispersive X-ray confirmed the electrode surface morphology. Electrochemical cyclic voltammetry using 10 to 100 mg/L KNO<sub>3</sub> and water from three-depth regions of the actual pond established the electrochemical test time (10.5 s) and electrode potential (0.135 V) protocol necessary to produce peak current that corresponds to the strength of nitrate ions during redox. The findings from in situ testing revealed that the proposed sensors have strong linear predictions (R<sup>2</sup> = 0.968 MSE = 1.659 for nSPEv and R<sup>2</sup> = 0.966 MSE = 4.697 for nSPEp) in the range of 10 to 100 mg/L and best detection limit of 3.15 μg/L, which are comparable to other sensors of more complex construction. The developed three-electrode electrochemical nitrate sensor confirms that it is reliable for both biosensing in controlled solutions and in situ aquaponic pond water systems.</p></div>\",\"PeriodicalId\":53443,\"journal\":{\"name\":\"Information Processing in Agriculture\",\"volume\":\"11 2\",\"pages\":\"Pages 187-201\"},\"PeriodicalIF\":7.7000,\"publicationDate\":\"2023-02-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2214317323000124/pdfft?md5=1c102ba0ae00d448d4cc3c7accdbca99&pid=1-s2.0-S2214317323000124-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Information Processing in Agriculture\",\"FirstCategoryId\":\"1091\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2214317323000124\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AGRICULTURE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Processing in Agriculture","FirstCategoryId":"1091","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214317323000124","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
Screen-printed graphite electrode on polyvinyl chloride and parchment strips integrated with genetic programming for in situ nitrate sensing of aquaponic pond water
Nitrate is the primary water-soluble macronutrient essential for plant growth that is converted from excess fish feeds, fish effluents, and degrading biomaterials on the aquaponic pond floor, and when aquacultural malpractices occur, large amounts of it retain in the water system causing increase rate in eutrophication and toxifies fish and aquaculture plants. Recent nitrate sensor prototypes still require performing the additional steps of water sample deionization and dilution and were constructed with expensive materials. In response to the challenge of sensor enhancement and aquaponic water quality monitoring, this study developed sensitive, repeatable, and reproducible screen-printed graphite electrodes on polyvinyl chloride and parchment paper substrates with silver as electrode material and 60:40 graphite powder:nail polish formulated conductive ink for electrical traces, integrated with 9-gene genetic expression model as a function of peak anodic current and electrochemical test time for nitrate concentration prediction that is embedded into low-power Arduino ESP32 for in situ nitrate sensing in aquaponic pond water. Five SPE electrical traces were designed on the two types of substrates. Scanning electron microscopy with energy dispersive X-ray confirmed the electrode surface morphology. Electrochemical cyclic voltammetry using 10 to 100 mg/L KNO3 and water from three-depth regions of the actual pond established the electrochemical test time (10.5 s) and electrode potential (0.135 V) protocol necessary to produce peak current that corresponds to the strength of nitrate ions during redox. The findings from in situ testing revealed that the proposed sensors have strong linear predictions (R2 = 0.968 MSE = 1.659 for nSPEv and R2 = 0.966 MSE = 4.697 for nSPEp) in the range of 10 to 100 mg/L and best detection limit of 3.15 μg/L, which are comparable to other sensors of more complex construction. The developed three-electrode electrochemical nitrate sensor confirms that it is reliable for both biosensing in controlled solutions and in situ aquaponic pond water systems.
期刊介绍:
Information Processing in Agriculture (IPA) was established in 2013 and it encourages the development towards a science and technology of information processing in agriculture, through the following aims: • Promote the use of knowledge and methods from the information processing technologies in the agriculture; • Illustrate the experiences and publications of the institutes, universities and government, and also the profitable technologies on agriculture; • Provide opportunities and platform for exchanging knowledge, strategies and experiences among the researchers in information processing worldwide; • Promote and encourage interactions among agriculture Scientists, Meteorologists, Biologists (Pathologists/Entomologists) with IT Professionals and other stakeholders to develop and implement methods, techniques, tools, and issues related to information processing technology in agriculture; • Create and promote expert groups for development of agro-meteorological databases, crop and livestock modelling and applications for development of crop performance based decision support system. Topics of interest include, but are not limited to: • Smart Sensor and Wireless Sensor Network • Remote Sensing • Simulation, Optimization, Modeling and Automatic Control • Decision Support Systems, Intelligent Systems and Artificial Intelligence • Computer Vision and Image Processing • Inspection and Traceability for Food Quality • Precision Agriculture and Intelligent Instrument • The Internet of Things and Cloud Computing • Big Data and Data Mining