Systematic Optimization and Predictive Modeling of CVD-Grown Monolayer WS₂ for Opto-Nanoelectronics Applications

Systematic Optimization and Predictive Modeling of CVD-Grown Monolayer WS₂ for Opto-Nanoelectronics Applications | 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 Article Systematic Optimization and Predictive Modeling of CVD-Grown Monolayer WS₂ for Opto-Nanoelectronics Applications M Bakhtiar Azim, Munim Shahriar Jawad, Ahsiur Rahman Nirjhar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7772260/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 The scalable synthesis of high-quality monolayer tungsten disulfide (WS₂), a prominent two-dimensional (2D) transition metal dichalcogenide (TMD), is essential for its integration into next-generation electronic and optoelectronic devices. This study presents a systematic optimization of chemical vapor deposition (CVD) growth parameters using the Taguchi Design of Experiments (DoE) methodology to enhance lateral crystal size and uniformity. Key process variables—growth temperature, growth time, and furnace ramp rate—were varied across an L9 orthogonal array. Signal-to-noise (S/N) ratio analysis and analysis of variance (ANOVA) revealed that growth time had the most significant influence on domain size, followed by temperature and ramp rate. A regression model was developed to accurately predict maximum crystal size based on the optimized parameters. The best-performing condition (750°C, 30 min, 5°C/min) yielded monolayer WS₂ flakes with lateral sizes up to ~ 75 µm. Optical microscopy (OM), atomic force microscopy (AFM), Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS) confirmed the monolayer thickness, crystallinity, and chemical composition of the synthesized material. This integrated approach offers a robust framework for tailoring 2D TMD growth through process control and predictive modeling, paving the way for scalable and reproducible fabrication of monolayer WS₂. Physical sciences/Materials science Physical sciences/Nanoscience and technology Full Text Additional Declarations No competing interests reported. Supplementary Files ESIWS2CVDProcessOptimization.docx 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-7772260","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":530633864,"identity":"f449375a-1f9f-4873-82f5-5a21081bcedb","order_by":0,"name":"M Bakhtiar 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Applications","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","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 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This study presents a systematic optimization of chemical vapor deposition (CVD) growth parameters using the Taguchi Design of Experiments (DoE) methodology to enhance lateral crystal size and uniformity. Key process variables\u0026mdash;growth temperature, growth time, and furnace ramp rate\u0026mdash;were varied across an L9 orthogonal array. Signal-to-noise (S/N) ratio analysis and analysis of variance (ANOVA) revealed that growth time had the most significant influence on domain size, followed by temperature and ramp rate. A regression model was developed to accurately predict maximum crystal size based on the optimized parameters. The best-performing condition (750\u0026deg;C, 30 min, 5\u0026deg;C/min) yielded monolayer WS₂ flakes with lateral sizes up to ~\u0026thinsp;75 \u0026micro;m. Optical microscopy (OM), atomic force microscopy (AFM), Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS) confirmed the monolayer thickness, crystallinity, and chemical composition of the synthesized material. This integrated approach offers a robust framework for tailoring 2D TMD growth through process control and predictive modeling, paving the way for scalable and reproducible fabrication of monolayer WS₂.\u003c/p\u003e","manuscriptTitle":"Systematic Optimization and Predictive Modeling of CVD-Grown Monolayer WS₂ for Opto-Nanoelectronics Applications","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-29 16:37:06","doi":"10.21203/rs.3.rs-7772260/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"ebac0c09-98b1-4cef-9685-c459fb592cd6","owner":[],"postedDate":"October 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56410700,"name":"Physical sciences/Materials science"},{"id":56410701,"name":"Physical sciences/Nanoscience and technology"}],"tags":[],"updatedAt":"2025-11-12T19:53:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-29 16:37:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7772260","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7772260","identity":"rs-7772260","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}