Triple-Band Terahertz Metamaterial Absorber for Label-Free Detection of Industrial Contaminants  with Machine Learning Driven Technique

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Abstract

Abstract In this study, we present a triple-band terahertz metamaterial absorber optimized for label-free detection of industrial contaminants and various analytes using a machine learning (ML) optimization technique. The proposed structure consists of asymmetric copper resonators on an FR-4 substrate with a continuous copper ground plane, achieving near-unity absorption at 0.96 THz, 2.112 THz, and 3.94 THz. Detailed parametric studies and near-field analyses demonstrate high refractive index sensitivity, with maximum sensitivity values of 771.3 GHz/RIU and high Figure of Merit (FoM) values of 2.541, 2.376, and 3.361. The sensor exhibits strong polarization insensitivity and angular stability, making it highly robust for real-world applications. The integration of an ML accelerated framework for absorptivity prediction in circular-shaped terahertz metamaterial absorbers achieved superior performance, with the proposed Random Forest model attaining an unprecedented R² = 0.999156 (99.9156%) using an 80–20 train test split, surpassing Extra Trees and KNN, while reducing computational time by over 85% and effectively predicting missing parameter values. Extensive performance evaluations confirm the capability of sensors to detect contaminants in food, fuel, chemicals, pesticides, and biological samples, underscoring its potential for high-precision industrial sensing and real-time quality monitoring.
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Triple-Band Terahertz Metamaterial Absorber for Label-Free Detection of Industrial Contaminants with Machine Learning Driven Technique | 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 Triple-Band Terahertz Metamaterial Absorber for Label-Free Detection of Industrial Contaminants with Machine Learning Driven Technique Mohammad Mahmudul Alam Mia Mohammad Mahmudul Alam Mia, Sayed Shifat Ahmed Sayed Shifat Ahmed, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7911872/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 In this study, we present a triple-band terahertz metamaterial absorber optimized for label-free detection of industrial contaminants and various analytes using a machine learning (ML) optimization technique. The proposed structure consists of asymmetric copper resonators on an FR-4 substrate with a continuous copper ground plane, achieving near-unity absorption at 0.96 THz, 2.112 THz, and 3.94 THz. Detailed parametric studies and near-field analyses demonstrate high refractive index sensitivity, with maximum sensitivity values of 771.3 GHz/RIU and high Figure of Merit (FoM) values of 2.541, 2.376, and 3.361. The sensor exhibits strong polarization insensitivity and angular stability, making it highly robust for real-world applications. The integration of an ML accelerated framework for absorptivity prediction in circular-shaped terahertz metamaterial absorbers achieved superior performance, with the proposed Random Forest model attaining an unprecedented R² = 0.999156 (99.9156%) using an 80–20 train test split, surpassing Extra Trees and KNN, while reducing computational time by over 85% and effectively predicting missing parameter values. Extensive performance evaluations confirm the capability of sensors to detect contaminants in food, fuel, chemicals, pesticides, and biological samples, underscoring its potential for high-precision industrial sensing and real-time quality monitoring. Absorber Metamaterial Sensor Machine Learning Optimization Triple-band Absorptivity Prediction Label-free Sensing Random Forest Model Industrial Contaminant Detection Food Quality Monitoring Refractive Index Sensitivity Polarization Insensitivity 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-7911872","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":538350800,"identity":"82760424-7727-4b8d-b4d1-1e09f2c9458b","order_by":0,"name":"Mohammad Mahmudul Alam Mia Mohammad Mahmudul Alam Mia","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYFACHiBmA7OYGRgqGBgMSNRyhmQtjG1EaNFtP3t0M08Zgxx/ewOzMe+8w/Lm7M0HGH5UbMOpxexMXtptnnMMxhJnDjAn8247bLiz51gCY8+Z27i1HMgxu83bxpC4QSKB+TBQC+OGGzkGzIxteLScfwPWUr9B/gFQy5zD9oS13IDYkmAgwQB0WMPhRCK0vDG7Oeccg+GMMwnMhnOOpSdvOHMs4SBev5zPAeoqY5Dnbz/ALPGmxtp2w/Hmgw9+VODWAgX/gZj/A5BoBnMPEFKPDOpIUTwKRsEoGAUjBAAAXz5XrtXobdsAAAAASUVORK5CYII=","orcid":"","institution":"RTM Al-Kabir Technical University","correspondingAuthor":true,"prefix":"","firstName":"Mohammad","middleName":"Mahmudul Alam Mia Mohammad Mahmudul Alam","lastName":"Mia","suffix":""},{"id":538350801,"identity":"d85b7ac0-f2ac-4739-b61f-d9092349ef0e","order_by":1,"name":"Sayed Shifat Ahmed Sayed Shifat Ahmed","email":"","orcid":"","institution":"RTM Al-Kabir Technical University","correspondingAuthor":false,"prefix":"","firstName":"Sayed","middleName":"Shifat Ahmed Sayed Shifat","lastName":"Ahmed","suffix":""},{"id":538350802,"identity":"4864c678-03b2-4432-8032-722d14448d39","order_by":2,"name":"Md. 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