Advanced Optimization of 2D Material-BasedBiosensor Through Machine Learning

Advanced Optimization of 2D Material-BasedBiosensor Through Machine Learning | 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 Advanced Optimization of 2D Material-BasedBiosensor Through Machine Learning Aymen Hlali, Hassen Zairi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6414260/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Jun, 2025 Read the published version in Sensing and Imaging → Version 1 posted 12 You are reading this latest preprint version Abstract This work introduces a highly sensitive and tunableTHz biosensor designed for detecting breast cancer cells. Theproposed sensor is based on a ring resonator with a centrallypositioned sample carrier, utilizing a hybrid structure of blackphosphorus (BP) and graphene. To optimize the interactionbetween Electronic Chemical Potential of Graphene and BP’selectron doping, a machine learning approach employingthe K-Nearest Neighbors (KNN) model was implemented.Electromagnetic simulations demonstrate exceptional sensitivity,reaching 24.165 T Hz/RIU for healthy cells and 30.534T Hz/RIU for cancerous cells. This design, characterizedby its high sensitivity, structural simplicity, and tunability,highlights significant potential for applications in THz biomedicaldiagnostics, particularly in early breast cancer detection. Biosensor machine learning black phosphorus graphene cancer cells K-Nearest Neighbors (KNN) Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 16 Jun, 2025 Read the published version in Sensing and Imaging → Version 1 posted Editorial decision: Revision requested 05 May, 2025 Reviews received at journal 04 May, 2025 Reviewers agreed at journal 04 May, 2025 Reviewers agreed at journal 29 Apr, 2025 Reviewers agreed at journal 28 Apr, 2025 Reviews received at journal 28 Apr, 2025 Reviewers agreed at journal 28 Apr, 2025 Reviewers agreed at journal 28 Apr, 2025 Reviewers invited by journal 28 Apr, 2025 Editor assigned by journal 11 Apr, 2025 Submission checks completed at journal 11 Apr, 2025 First submitted to journal 09 Apr, 2025 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. 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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-6414260","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":449537343,"identity":"b1440d25-4de4-4017-9b48-c4e69f0c18ea","order_by":0,"name":"Aymen Hlali","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYBACA3YGxgMJYCbzwYcfKkA0cwN+LcwMDFAtbMnGEmdAWhiJ0AJh8qhJ8LaBGAS0mDMzPzjwoOZwNL90D7OB5LzaaP52oJYfFdtwarFsZjM4kHDscO7MOWcPPijcdjx3xmHGBsaeM7dxO+wwA1AL2+HcDTfykg0ktx3LbQBqYWZsw6eF/cOBhH+Hc/ffyDGT4J1zLHc+YS08BgcS24C2SIC0NNTkbiBCS8GBxL703Bk30oCBfOxA7kagloN4/XK8fePDH9+sc/tnJAOjsqYud975wwcf/KjArQUKmmGMw2DyACH1QFCHwRgFo2AUjIJRAAcA6IVmgN00EI8AAAAASUVORK5CYII=","orcid":"","institution":"University of Carthage","correspondingAuthor":true,"prefix":"","firstName":"Aymen","middleName":"","lastName":"Hlali","suffix":""},{"id":449537344,"identity":"6f3d516f-81dc-43f9-851d-d4ef140d3d74","order_by":1,"name":"Hassen Zairi","email":"","orcid":"","institution":"University of Carthage","correspondingAuthor":false,"prefix":"","firstName":"Hassen","middleName":"","lastName":"Zairi","suffix":""}],"badges":[],"createdAt":"2025-04-09 18:23:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6414260/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6414260/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11220-025-00617-2","type":"published","date":"2025-06-16T15:57:25+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":85231317,"identity":"a7a3b259-46c1-4226-9ab4-e7f9bf57b58a","added_by":"auto","created_at":"2025-06-23 16:06:02","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":400950,"visible":true,"origin":"","legend":"","description":"","filename":"Article2025.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6414260/v1_covered_bd442236-a111-4eff-a5c4-89b8363b9f18.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Advanced Optimization of 2D Material-BasedBiosensor Through Machine Learning","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"sensing-and-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssta","sideBox":"Learn more about [Sensing and Imaging](http://link.springer.com/journal/11220)","snPcode":"11220","submissionUrl":"https://submission.nature.com/new-submission/11220/3","title":"Sensing and Imaging","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Biosensor, machine learning, black phosphorus, graphene, cancer cells, K-Nearest Neighbors (KNN)","lastPublishedDoi":"10.21203/rs.3.rs-6414260/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6414260/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This work introduces a highly sensitive and tunableTHz biosensor designed for detecting breast cancer cells. 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