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<p> A&#xa0;non-invasive, enzyme-based colorimetric biosensor was developed&#xa0;for urea detection in saliva, utilizing a growth-based method with Au@Ag core–shell nanostructures, including CTAB-coated gold nanoparticles (AuNPs) and CTAB-coated gold nanorods with short (SAuNRs) and high (HAuNRs) aspect ratios. The biosensing mechanism relies on urease-mediated hydrolysis of urea, which raises the pH and enhances the reduction capability of ascorbic acid, leading to the formation of a silver shell on the gold nanostructures, causing colorimetric changes correlated to urea concentration. A machine-learning comparative analysis was also performed to assess how the nanostructure morphology of AuNPs, SAuNRs, and HAuNRs affects sensor performance. The results showed that the biosensor effectively detected urea with all three nanostructures, achieving the highest precision and accuracy using HAuNRs, with a limit of detection (LOD) of 0.26&#xa0;mM and a linear range of 0.5–50&#xa0;mM urea. The best-performing machine learning algorithms, evaluated using <i>R</i><sup>2</sup>, RMSE, and NRMSE metrics, were gradient boosting and extreme gradient boosting when applied to high aspect ratio gold nanorods. This innovative approach provides a cost-effective and user-friendly platform for urea detection, making it a promising tool for non-invasive diagnostics.</p> Graphical Abstract <p></p>

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Urea-mediated growth engineering of Au@Ag core–shell nanostructures: an enzymatic detection strategy with machine learning-assisted comparative analysis

  • 期刊论文

  • 发表日期：

    2025年7月29日

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  • Farzaneh Shalileh,
  • Morteza Hosseini,
  • Mohammad Golbashy,
  • Mehdi Dadmehr,
  • Hossein Sabahi

摘要

A non-invasive, enzyme-based colorimetric biosensor was developed for urea detection in saliva, utilizing a growth-based method with Au@Ag core–shell nanostructures, including CTAB-coated gold nanoparticles (AuNPs) and CTAB-coated gold nanorods with short (SAuNRs) and high (HAuNRs) aspect ratios. The biosensing mechanism relies on urease-mediated hydrolysis of urea, which raises the pH and enhances the reduction capability of ascorbic acid, leading to the formation of a silver shell on the gold nanostructures, causing colorimetric changes correlated to urea concentration. A machine-learning comparative analysis was also performed to assess how the nanostructure morphology of AuNPs, SAuNRs, and HAuNRs affects sensor performance. The results showed that the biosensor effectively detected urea with all three nanostructures, achieving the highest precision and accuracy using HAuNRs, with a limit of detection (LOD) of 0.26 mM and a linear range of 0.5–50 mM urea. The best-performing machine learning algorithms, evaluated using R2, RMSE, and NRMSE metrics, were gradient boosting and extreme gradient boosting when applied to high aspect ratio gold nanorods. This innovative approach provides a cost-effective and user-friendly platform for urea detection, making it a promising tool for non-invasive diagnostics.

Graphical Abstract