Exploring the potential of SnO2 nanoparticles for CO2 capture using RSM and ANN

Exploring the potential of SnO2 nanoparticles for CO2 capture using RSM and ANN | 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 Exploring the potential of SnO 2 nanoparticles for CO 2 capture using RSM and ANN Firouzeh Salimian, Alireza Hemmati, Ahad Ghaemi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6428531/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract This study investigates the properties of SnO 2 nanomaterials as CO₂ adsorbents. Although CO₂ capture technologies have significantly advanced, challenges such as high costs and limited scalability persist. In recent years, nanomaterials have emerged as promising candidates for CO₂ adsorption due to their high adsorption capacity, lower cost, and wide availability. However, future research should focus on developing low-cost and efficient nanomaterials to enable large-scale industrial CO₂ capture. This study examined the CO₂ adsorption capacity using SnO 2 nanoadsorbents by employing Response Surface Methodology (RSM) and Artificial Neural Networks (ANNs), specifically Radial Basis Function (RBF) and Multi-Layer Perceptron (MLP) networks, for process modeling and optimization. Through the analysis of experimental data, temperature, pressure, and adsorption time were identified as crucial influencing factors. The R 2 value of 0.9933 for RSM indicated a great match, whereas the R 2 value of one for ANNs indicated superior predictive accuracy. With a minimum Mean Squared Error (MSE) of 0.00011975 for the dataset, the MLP network was trained using a three-layer activation function. With 288 neurons and a spread of 2, the RBF network achieved an R 2 value of 0.9985 and a minimal MSE of 0.000750002. The smooth MLP plots effectively captured complex discontinuities, showcasing the superior predictive abilities of ANNs for optimizing the CO₂ adsorption process using SnO 2 nanoadsorbents, while the RSM surfaces exhibited rigid, polynomial-based patterns. Earth and environmental sciences/Environmental sciences/Environmental chemistry Physical sciences/Engineering/Chemical engineering CO2 nanomaterials metal oxide SnO2 RSM ANN Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 08 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 23 Jun, 2025 Reviews received at journal 22 Jun, 2025 Reviewers agreed at journal 13 Jun, 2025 Reviews received at journal 13 Jun, 2025 Reviewers agreed at journal 13 Jun, 2025 Reviewers agreed at journal 13 Jun, 2025 Reviewers invited by journal 13 Jun, 2025 Editor assigned by journal 15 Apr, 2025 Editor invited by journal 15 Apr, 2025 Submission checks completed at journal 11 Apr, 2025 First submitted to journal 11 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-6428531","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":470793996,"identity":"08db7dbd-6dec-4290-b9de-3031035cf25d","order_by":0,"name":"Firouzeh Salimian","email":"","orcid":"","institution":"Iran University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Firouzeh","middleName":"","lastName":"Salimian","suffix":""},{"id":470793997,"identity":"bec3909c-18a3-441e-9a3e-a01af95f34c6","order_by":1,"name":"Alireza Hemmati","email":"","orcid":"","institution":"Iran University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Alireza","middleName":"","lastName":"Hemmati","suffix":""},{"id":470793998,"identity":"f7d00240-275f-410d-9d71-1016c11d806c","order_by":2,"name":"Ahad Ghaemi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYFACHhAhIccG47PhVoqqxRiokrGBFC0MiQ0wLQQBf//agx8+5lik90mkP3/AUGPHwCd9AL8WiRvvkiVnbpPIbZPIMWxgOJbMwMaXQMCaG2cMpHkhWoAOYzvAwMZDQIf8jTPGv/9uk0hnk0h/2MDwjwgtBud7zKQZt0kksEkkGDYwthGhxfAGj5ll7zYJwzaeN4YzEvuSeQhqkTt/xvjGz2118vLt6Q8+fPhmJyffQ0ALg0QCEicBFk14Af8BwmpGwSgYBaNghAMABKw68LruZoAAAAAASUVORK5CYII=","orcid":"","institution":"Iran University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Ahad","middleName":"","lastName":"Ghaemi","suffix":""}],"badges":[],"createdAt":"2025-04-11 12:53:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6428531/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6428531/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-34472-8","type":"published","date":"2026-01-08T15:57:38+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":100070047,"identity":"6cb1b3cb-55b0-440c-a1d6-fe78836e9da2","added_by":"auto","created_at":"2026-01-12 16:16:10","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2025967,"visible":true,"origin":"","legend":"","description":"","filename":"Salimian14040121.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6428531/v1_covered_d1c97926-1e66-4c32-bcdc-3233c619bc78.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eExploring the potential of SnO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2 \u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003enanoparticles\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003efor CO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e capture using RSM and ANN\u003c/strong\u003e\u003c/p\u003e","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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"CO2, nanomaterials, metal oxide, SnO2, RSM, ANN","lastPublishedDoi":"10.21203/rs.3.rs-6428531/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6428531/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the properties of SnO\u003csub\u003e2\u003c/sub\u003e nanomaterials as CO₂ adsorbents. 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