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Abstract <p>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 configurations. 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 <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="604_2025_7388_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="21" /> </InlineMediaObject> <EquationSource Format="TEX">\(R^2\)</EquationSource> <EquationSource Format="MATHML"><math> <msup> <mi>R</mi> <mn>2</mn> </msup> </math></EquationSource> </InlineEquation> 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.</p> Graphical abstract <p></p>

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Laser-ablated nanoparticle-enhanced quartz tuning fork (QTF) sensor array for detection of volatile organic compounds (VOCs) and their mixtures assisted by neural network

  • 期刊论文

  • 发表日期：

    2025年7月28日

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  • Satyendra Vishwakarma,
  • Aarathi Krishna G,
  • Pankaj Bhujbal,
  • Devnath Dhirhe,
  • Suwarna Datar

摘要

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 configurations. 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 \(R^2\) 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.

Graphical abstract