Arsenic Sensor Using Fluorescent GQD Nanocomposites: Real-Time Monitoring with AI and IoT Capabilities

Arsenic Sensor Using Fluorescent GQD Nanocomposites: Real-Time Monitoring with AI and IoT Capabilities | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Arsenic Sensor Using Fluorescent GQD Nanocomposites: Real-Time Monitoring with AI and IoT Capabilities shivani pandey, satanand mishra, Tanmay Sardar, Aayush Mishra This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6807060/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Arsenic contamination of drinking water is a major global problem for portable, low-cost, and extremely sensitive detection technologies. In this study, a multi-disciplinary approach was adopted to produce a fluorescent graphene quantum dot (GQD) nanocomposite through synthesis, combined with artificial intelligence (AI) and Internet of Things (IoT) technologies for real-time detection of arsenic. GQDs were manufactured through a one-pot hydrothermal method and functionalized using polyethylene glycol (PEG) and chitosan, the latter of which improved colloidal stability, fluorescence intensity, and analyte binding. The sensor operates by quenching fluorescence in the presence of arsenic ions, which was quantitatively interpreted using a linear regression prediction model. The IoT-enhanced system has a microcontroller-based user-friendly dashboard for real-time data access. The sensor obtained a detection limit of 0.4637 ppm, response time of 2.21 ns and reproducibility of 1.308% (RSD = 1.308%). Thus, this research frontier combined functional nanomaterials, AI-based analytics, and wireless sensor networks, and in doing so, constructed a new platform for scalable, real-world environmental diagnostics. Graphical Abstract Fluorescence Quenching Water Quality Monitoring Internet of Things (IoT) Linear Regression Model Nanocomposite Sensor Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6807060","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":465613739,"identity":"c98ece26-3b9b-4266-8546-7ebb462fad35","order_by":0,"name":"shivani pandey","email":"","orcid":"","institution":"AcSIR -CSIR AMPRI BHOPAL","correspondingAuthor":false,"prefix":"","firstName":"shivani","middleName":"","lastName":"pandey","suffix":""},{"id":465613740,"identity":"aa20caee-2914-4562-bcda-6646f579ada7","order_by":1,"name":"satanand mishra","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYJCCA4wNDAxs8o8PANkSMsRr4WdISwBp4SHOGpAWyYYcAxCbsBb52d2JB3/uuCdvcODM51c3aix4GNgPH92AT4vBnbMbDvOeKTbccLB3m3XOMaDDeNLSbuDVIpG74TBjWwIjUOM24xw2oBYJHjO8WuRn5G44+LMtwX7DMZ5nxjn/iNDCcCN3wwHetoTEmT08zI9z24jQYgDUchioJblfgs2MObdPgoeNkF+ADtv8Eegw2zYJ5sefc77VyfGzHz6G32FIgE0CTBKrHASYP5CiehSMglEwCkYOAABGqU0V6zzaVgAAAABJRU5ErkJggg==","orcid":"","institution":"AcSIR -CSIR AMPRI BHOPAL","correspondingAuthor":true,"prefix":"","firstName":"satanand","middleName":"","lastName":"mishra","suffix":""},{"id":465613741,"identity":"c6d1149e-6b73-4a79-b17f-38ce8929e54d","order_by":2,"name":"Tanmay Sardar","email":"","orcid":"","institution":"AcSIR -CSIR AMPRI BHOPAL","correspondingAuthor":false,"prefix":"","firstName":"Tanmay","middleName":"","lastName":"Sardar","suffix":""},{"id":465613742,"identity":"5832962c-0093-471f-bed5-e4a785b2b4fe","order_by":3,"name":"Aayush Mishra","email":"","orcid":"","institution":"IISER BHOPAL","correspondingAuthor":false,"prefix":"","firstName":"Aayush","middleName":"","lastName":"Mishra","suffix":""}],"badges":[],"createdAt":"2025-06-03 05:12:30","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6807060/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6807060/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83978592,"identity":"16f9bb5b-9c79-4ec7-a5bd-ee3104ed03be","added_by":"auto","created_at":"2025-06-05 09:34:02","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1495650,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6807060/v1_covered_dae96dd2-7d0c-4846-8767-751a1825ccc5.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eArsenic Sensor Using Fluorescent GQD Nanocomposites: Real-Time Monitoring with AI and IoT Capabilities\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"csir ampri bhopal ","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Fluorescence Quenching, Water Quality Monitoring, Internet of Things (IoT), Linear Regression Model, Nanocomposite Sensor","lastPublishedDoi":"10.21203/rs.3.rs-6807060/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6807060/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eArsenic contamination of drinking water is a major global problem for portable, low-cost, and extremely sensitive detection technologies. In this study, a multi-disciplinary approach was adopted to produce a fluorescent graphene quantum dot (GQD) nanocomposite through synthesis, combined with artificial intelligence (AI) and Internet of Things (IoT) technologies for real-time detection of arsenic. GQDs were manufactured through a one-pot hydrothermal method and functionalized using polyethylene glycol (PEG) and chitosan, the latter of which improved colloidal stability, fluorescence intensity, and analyte binding. The sensor operates by quenching fluorescence in the presence of arsenic ions, which was quantitatively interpreted using a linear regression prediction model. The IoT-enhanced system has a microcontroller-based user-friendly dashboard for real-time data access. The sensor obtained a detection limit of 0.4637 ppm, response time of 2.21 ns and reproducibility of 1.308% (RSD\u0026thinsp;=\u0026thinsp;1.308%). 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