A Multi-Scale Theoretical Framework for Quantum-Enhanced Carbon Capture via Zn-Porphyrin Metal-Organic Frameworks

A Multi-Scale Theoretical Framework for Quantum-Enhanced Carbon Capture via Zn-Porphyrin Metal-Organic Frameworks | 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 A Multi-Scale Theoretical Framework for Quantum-Enhanced Carbon Capture via Zn-Porphyrin Metal-Organic Frameworks Sami Shibah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7592096/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 Mitigating climate change necessitates a paradigm shift in carbon capture technologies, moving towards solutions with high efficiency and low energetic cost. This paper introduces a multi-scale theoretical framework for the ab initio design of a novel Zn-porphyrin-based metal-organic framework (MOF) tailored for superior CO2 sequestration. By integrating density functional theory (DFT) for quantum-level accuracy, molecular dynamics (MD) for structural stability analysis, and a machine learning (ML) model for high-throughput screening, we predict a material with a CO2 binding energy of -48.2 kJ/mol and a selectivity of over 98% against N2. The framework further predicts a 60% reduction in regeneration energy compared to conventional amine-based systems. We conclude by presenting a detailed experimental validation roadmap, positioning this work as a foundational blueprint for translating quantum-informed computational design into tangible climate mitigation technologies. Physical sciences/Chemistry Physical sciences/Materials science Carbon Capture Metal-Organic Frameworks (MOFs) Density Functional Theory (DFT) Quantum Chemistry Climate Change Machine Learning Full Text Additional Declarations No competing interests reported. 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. 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-7592096","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":513667637,"identity":"ea823148-cafb-43fe-b87a-36db6bc5467c","order_by":0,"name":"Sami Shibah","email":"data:image/png;base64,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","orcid":"","institution":"Independent researcher","correspondingAuthor":true,"prefix":"","firstName":"Sami","middleName":"","lastName":"Shibah","suffix":""}],"badges":[],"createdAt":"2025-09-11 12:53:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7592096/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7592096/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91523178,"identity":"db211473-6059-4f34-98ff-3f657cfc7637","added_by":"auto","created_at":"2025-09-17 10:39:08","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":184295,"visible":true,"origin":"","legend":"","description":"","filename":"Maintex20250911T151059.781.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7592096/v1_covered_4ce5f7f8-183d-47f5-97e7-7a0ce452c499.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Multi-Scale Theoretical Framework for Quantum-Enhanced Carbon Capture via Zn-Porphyrin Metal-Organic Frameworks","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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 Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Carbon Capture, Metal-Organic Frameworks (MOFs), Density Functional Theory (DFT), Quantum Chemistry, Climate Change, Machine Learning","lastPublishedDoi":"10.21203/rs.3.rs-7592096/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7592096/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Mitigating climate change necessitates a paradigm shift in carbon capture technologies, moving towards solutions with high efficiency and low energetic cost. 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