Design of Silicene Nanopore Sensors for DNASequencing Application: Machine learning assisted DFT+NEGF Study

Design of Silicene Nanopore Sensors for DNASequencing Application: Machine learning assisted DFT+NEGF Study | 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 Design of Silicene Nanopore Sensors for DNASequencing Application: Machine learning assisted DFT+NEGF Study Ashutosh shah, Abhishek Sharma, Arti Kashyap This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8673181/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Decoding the genetic code with electronic sensorsrequires devices that combine high signal fidelity with intelligent data interpretation. In this work, we present a firstprinciples quantum transport investigation of a Z-shaped silicene nanoribbon field-effect transistor (FET) incorporating ananopore for single-nucleobase detection. The electronic structureand current–voltage characteristics are computed using densityfunctional theory combined with the nonequilibrium Green’sfunction formalism. The proposed Z-shaped architecture produces enhanced current levels, which are highly advantageous forachieving an improved signal-to-noise ratio in nanoscale sensing.Asymmetric electrode passivation yields substantially highercurrent discrimination among DNA bases than the symmetricall-hydrogen-passivated device. To enable reliable and automatedbase identification from the transport responses, a machinelearning-assisted framework is employed, where a Random Forestclassifier attains 99.2% classification accuracy. SHAP analysis isused to interpret the model and identify the dominant physicaldescriptors governing nucleobase discrimination. The combineddevice–algorithm co-design establishes this platform as a highsensitivity, label-free, and resource-efficient approach for nextgeneration electronic DNA sequencing. Density Functional Theory DNA sequencing electronic transport silicene nanoribbon nanopore quantum transport supervised machine learning classification SHAP Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 12 Mar, 2026 Reviews received at journal 25 Feb, 2026 Reviewers agreed at journal 23 Feb, 2026 Reviewers agreed at journal 16 Feb, 2026 Reviewers invited by journal 14 Feb, 2026 Editor assigned by journal 28 Jan, 2026 Submission checks completed at journal 28 Jan, 2026 First submitted to journal 22 Jan, 2026 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8673181","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":579112708,"identity":"8f68174e-111d-4d2a-8ed2-65d832f7935c","order_by":0,"name":"Ashutosh shah","email":"","orcid":"","institution":"Indian Institute of Technology Mandi","correspondingAuthor":false,"prefix":"","firstName":"Ashutosh","middleName":"","lastName":"shah","suffix":""},{"id":579112709,"identity":"89240707-9183-4040-8b39-fa1493b0e468","order_by":1,"name":"Abhishek Sharma","email":"","orcid":"","institution":"Indian Institute of Technology Mandi","correspondingAuthor":false,"prefix":"","firstName":"Abhishek","middleName":"","lastName":"Sharma","suffix":""},{"id":579112711,"identity":"815d5c66-e2b2-4574-ae3a-0c97c7a4a71f","order_by":2,"name":"Arti Kashyap","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYJCCAx8MJMAMiQQQyUxQAzPDwRkGEhKkaWHmAaoGayHKUfyzzx88bFNgUafbfvbgjQcMdvIM7LwH8GqROJfMcDgH6DCzM3nJFgkMyYYNzHwJ+K05wwzVciDHDOgXZiDiMcCrQx6kxQKk5fwbkJZ6wloMQFoYQFpugG05TFiL4Rlmg4M9BhKS2268MbZIMDhu2EZIi9wZxscffvyp4zc7n2N480dFtTw//xn8WtDdycDARor6UTAKRsEoGAXYAQBD3jklExUUdQAAAABJRU5ErkJggg==","orcid":"","institution":"Indian Institute of Technology Mandi","correspondingAuthor":true,"prefix":"","firstName":"Arti","middleName":"","lastName":"Kashyap","suffix":""}],"badges":[],"createdAt":"2026-01-22 21:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8673181/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8673181/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101180290,"identity":"f418fa59-9fab-4b82-b535-b304fd6ea572","added_by":"auto","created_at":"2026-01-27 03:45:31","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5032,"visible":true,"origin":"","legend":"","description":"","filename":"7c5e058e49474161ab77e9530bde7237.json","url":"https://assets-eu.researchsquare.com/files/rs-8673181/v1/a524d2487fcb4e6ba1de452e.json"},{"id":101207179,"identity":"a7bd71a3-ffab-488a-bb03-ccac416dbe4a","added_by":"auto","created_at":"2026-01-27 09:58:12","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":35239692,"visible":true,"origin":"","legend":"","description":"","filename":"DNAsequencingSilicene3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8673181/v1_covered_afb44385-1b88-477f-a73d-e230b04fcc19.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eDesign of Silicene Nanopore Sensors for DNASequencing Application: Machine learning assisted DFT+NEGF Study\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"silicon","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scon","sideBox":"Learn more about [Silicon](https://www.springer.com/journal/12633)","snPcode":"12633","submissionUrl":"https://submission.nature.com/new-submission/12633/3","title":"Silicon","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Density Functional Theory, DNA sequencing, electronic transport, silicene nanoribbon, nanopore, quantum transport, supervised machine learning, classification, SHAP","lastPublishedDoi":"10.21203/rs.3.rs-8673181/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8673181/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Decoding the genetic code with electronic sensorsrequires devices that combine high signal fidelity with intelligent data interpretation. 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