<p>Conventional nanoparticle-based colorimetric sensor arrays typically require the synthesis of multiple differently modified nanoprobes as sensing elements, where the process is often complex and the number of available probes is limited. We designed a simple sensor array for bacteria identification using five different metal ions (Al³⁺, Fe³⁺, Cu²⁺, Ni²⁺, Co²⁺) and only one type of T-AgNPs as sensing elements. The bacteria were first treated with metal ions, which altered their surface properties, leading to differential binding with T-AgNPs. Subsequent addition of Cl<sup>−</sup> induced the etching of T-AgNPs, producing unique color change patterns for bacteria. Combined with multivariate analysis, the array showed clear discrimination of 11 bacterial species, bacterial mixtures, and bacteria at different concentrations in artificial plasma and urine. Linear discriminant analysis (LDA) with leave-one-out cross-validation showed complete separation within the current datasets, although further validation using larger independent datasets and blinded unknown samples is needed. This simple and cost-effective strategy provides a facile and expandable platform for bacterial identification.</p> Graphical abstract <p></p>

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A colorimetric sensor array for bacterial identification based on differential etching of T-AgNPs on metal ion-treated cells

  • Shahzad Naveed,
  • Hongyu Tang,
  • Zhijie Zhao,
  • Chuanzhi Dai,
  • Peng Yan,
  • Jianqiang Du,
  • Yayan Wu

摘要

Conventional nanoparticle-based colorimetric sensor arrays typically require the synthesis of multiple differently modified nanoprobes as sensing elements, where the process is often complex and the number of available probes is limited. We designed a simple sensor array for bacteria identification using five different metal ions (Al³⁺, Fe³⁺, Cu²⁺, Ni²⁺, Co²⁺) and only one type of T-AgNPs as sensing elements. The bacteria were first treated with metal ions, which altered their surface properties, leading to differential binding with T-AgNPs. Subsequent addition of Cl induced the etching of T-AgNPs, producing unique color change patterns for bacteria. Combined with multivariate analysis, the array showed clear discrimination of 11 bacterial species, bacterial mixtures, and bacteria at different concentrations in artificial plasma and urine. Linear discriminant analysis (LDA) with leave-one-out cross-validation showed complete separation within the current datasets, although further validation using larger independent datasets and blinded unknown samples is needed. This simple and cost-effective strategy provides a facile and expandable platform for bacterial identification.

Graphical abstract