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<p>Interface-driven hotspot-engineered flexible optical sensors are reported having been prepared by immobilizing anisotropic nanostars (NS) on polypropylene (PP) films for in situ multiplexed detection of pesticide and synthetic colourant residues using a chemometric-assisted surface-enhanced Raman spectroscopy (SERS) strategy. Highly anisotropic nanostars were synthesized via a facile one-pot chemical reduction approach, with controlled nanostar morphologies facilitating tunable plasmonic activity. Detailed structural, optical and chemical compositional investigation confirmed effective integration of nanostars into PP fibrous matrices. The SERS performance was systematically modulated by varying the nanostar spike geometry, achieving a maximum enhancement factor of 9.6 × 10<sup>7</sup>. Furthermore, finite-element electromagnetic (EM) simulations revealed strong localization of the electromagnetic field around nanostar tips, with optimal enhancement at 785&#xa0;nm excitation, thereby confirming the dominance of tip-induced EM enhancement. The surface interactions leading to dense nanostar hotspots on fibrous PP mats have been well elucidated, considering the hydrophobicity-assisted nanostar confined assembly. The developed sensors demonstrated superior signal reproducibility, stability, and detection limits reaching femtomolar levels for methylene blue and nanomolar levels for thiram and carmoisine. The selective identification of pesticides and synthetic colourant residues was achieved through multivariate chemometric analysis, which demonstrated clear spectral discrimination. The real-time detection of thiram and carmoisine in commercial fruit juice samples was achieved with excellent specificity and recoveries of 92 to 109%, which illustrated the practical applicability of PP/NS sensor for food safety monitoring applications.</p> Graphical Abstract <p></p>

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Self-assembled anisotropic nanostars on flexible polypropylene for chemometrics-assisted SERS real-time detection of pesticides and food colours

  • 期刊论文

  • 发表日期：

    2026年8月4日

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  • Kais Daoudi,
  • Nikoo Mohajer,
  • Anakha Udayan,
  • Soumya Columbus,
  • Shaiju S. Nazeer,
  • Roqiya Belmerabet,
  • Krithikadevi Ramachandran,
  • Mounir Gaidi

摘要

Interface-driven hotspot-engineered flexible optical sensors are reported having been prepared by immobilizing anisotropic nanostars (NS) on polypropylene (PP) films for in situ multiplexed detection of pesticide and synthetic colourant residues using a chemometric-assisted surface-enhanced Raman spectroscopy (SERS) strategy. Highly anisotropic nanostars were synthesized via a facile one-pot chemical reduction approach, with controlled nanostar morphologies facilitating tunable plasmonic activity. Detailed structural, optical and chemical compositional investigation confirmed effective integration of nanostars into PP fibrous matrices. The SERS performance was systematically modulated by varying the nanostar spike geometry, achieving a maximum enhancement factor of 9.6 × 107. Furthermore, finite-element electromagnetic (EM) simulations revealed strong localization of the electromagnetic field around nanostar tips, with optimal enhancement at 785 nm excitation, thereby confirming the dominance of tip-induced EM enhancement. The surface interactions leading to dense nanostar hotspots on fibrous PP mats have been well elucidated, considering the hydrophobicity-assisted nanostar confined assembly. The developed sensors demonstrated superior signal reproducibility, stability, and detection limits reaching femtomolar levels for methylene blue and nanomolar levels for thiram and carmoisine. The selective identification of pesticides and synthetic colourant residues was achieved through multivariate chemometric analysis, which demonstrated clear spectral discrimination. The real-time detection of thiram and carmoisine in commercial fruit juice samples was achieved with excellent specificity and recoveries of 92 to 109%, which illustrated the practical applicability of PP/NS sensor for food safety monitoring applications.

Graphical Abstract