Laser-Ablated Nanoparticle-Enhanced Quartz Tuning Fork (QTF) Sensor Array for Detection of Volatile Organic Compounds (VOCs) and their Mixtures Assisted by Neural Network

Laser-Ablated Nanoparticle-Enhanced Quartz Tuning Fork (QTF) Sensor Array for Detection of Volatile Organic Compounds (VOCs) and their Mixtures Assisted by Neural Network | 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 Laser-Ablated Nanoparticle-Enhanced Quartz Tuning Fork (QTF) Sensor Array for Detection of Volatile Organic Compounds (VOCs) and their Mixtures Assisted by Neural Network Satyendra Vishwakarma, Aarathi Krishna G, Pankaj Bhujbal, Devnath Dhirhe, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6712512/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Jul, 2025 Read the published version in Microchimica Acta → Version 1 posted 10 You are reading this latest preprint version Abstract The detection of volatile organic compounds (VOCs) and their mixtures is critical for applications ranging from environmental monitoring and industrial process control to non-invasive disease diagnostics. Electronic noses offer a promising route for selective VOC identification. In this work, we report an enhanced e-nose platform based on quartz tuning fork (QTF) sensors functionalized with polymer–nanoparticle (NP) composites. Silver (Ag), copper (Cu), and zinc oxide (ZnO) nanoparticles were synthesized via laser ablation at 532 nm and characterized. These nanoparticles were integrated into a polymer matrix and QTFs were modified using these to fabricate four sensor configuration. The sensors were evaluated across a wide concentration range (200 ppb to 100 ppm) for acetone, isoprene, acetaldehyde, and their binary and ternary mixtures. Compared to polymer-only sensors, the NP-functionalized QTFs exhibited significantly improved sensitivity and stability. A neural network regressor trained on sensor response data achieved a prediction accuracy of 0.93 and an average area under the curve (AUC) of 0.98, demonstrating excellent classification performance. Double-blind tests yielded a mean prediction error of 6 ppm and an \(R^2\) score of 0.85, with the model performing best at concentrations below 60 ppm. This work highlights a scalable approach for constructing high-performance, machine-learning-enabled VOC sensing platforms. laser ablation QTF sensor VOC detection mixture of VOCs neural networks Electronic Nose Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 Jul, 2025 Read the published version in Microchimica Acta → Version 1 posted Editorial decision: Revision requested 02 Jul, 2025 Reviews received at journal 25 Jun, 2025 Reviewers agreed at journal 18 Jun, 2025 Reviews received at journal 17 Jun, 2025 Reviewers agreed at journal 16 Jun, 2025 Reviewers agreed at journal 16 Jun, 2025 Reviewers invited by journal 16 Jun, 2025 Editor assigned by journal 03 Jun, 2025 Submission checks completed at journal 02 Jun, 2025 First submitted to journal 21 May, 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. 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-6712512","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":472758295,"identity":"35139a37-fa06-4fd8-b28c-098c0641a9a4","order_by":0,"name":"Satyendra Vishwakarma","email":"","orcid":"","institution":"Defence Institute of Advanced Technology","correspondingAuthor":false,"prefix":"","firstName":"Satyendra","middleName":"","lastName":"Vishwakarma","suffix":""},{"id":472758296,"identity":"bff94eea-4966-4456-a46c-ddef0bcb0b94","order_by":1,"name":"Aarathi Krishna G","email":"","orcid":"","institution":"Defence Institute of Advanced 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Electronic noses offer a promising route for selective VOC identification. In this work, we report an enhanced e-nose platform based on quartz tuning fork (QTF) sensors functionalized with polymer\u0026ndash;nanoparticle (NP) composites. Silver (Ag), copper (Cu), and zinc oxide (ZnO) nanoparticles were synthesized via laser ablation at 532 nm and characterized. These nanoparticles were integrated into a polymer matrix and QTFs were modified using these to fabricate four sensor configuration. The sensors were evaluated across a wide concentration range (200 ppb to 100 ppm) for acetone, isoprene, acetaldehyde, and their binary and ternary mixtures. Compared to polymer-only sensors, the NP-functionalized QTFs exhibited significantly improved sensitivity and stability. A neural network regressor trained on sensor response data achieved a prediction accuracy of 0.93 and an average area under the curve (AUC) of 0.98, demonstrating excellent classification performance. 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