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An Adaptive Trimodal Bioanalysis System via Heterogeneous Interface Engineering and Enzyme-Powered Molecular

Lin Sun1, Wan-Zhen Xie2, Yu Ya3

  • 1Key Laboratory of Optic-electric Chemo/Biosensing and Molecular Recognition (Guangxi Minzu University), Education Department of Guangxi Zhuang Autonomous Region; Key Laboratory of Chemistry and Engineering of Forest Products, State Ethnic Affairs Commission; School of Chemistry and Chemical Engineering, Guangxi Minzu University, Nanning 530006, China.

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This study introduces a novel trimodal biosensing platform for ultrasensitive pathogen detection. Its adaptive signal fusion and enzyme-powered machines achieve exceptional sensitivity and reliability for diagnostics.

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Area of Science:

  • Nanomaterials and Biosensing
  • Heterogeneous Interface Engineering
  • Enzyme-Powered Molecular Machines

Background:

  • Pathogen detection requires ultrasensitive and reliable methods.
  • Existing biosensing platforms face limitations in sensitivity, specificity, and multi-modal detection.
  • Advanced nanomaterials and signal amplification strategies are crucial for improving biosensor performance.

Purpose of the Study:

  • To develop a trimodal biosensing platform for ultrasensitive pathogen detection.
  • To integrate heterogeneous interface engineering and enzyme-powered molecular machines for enhanced performance.
  • To achieve adaptive signal-fusion for reliable and accurate detection.

Main Methods:

  • Fabrication of a Ni-MOF-on-Co-MOF heterostructure with enhanced surface area and electron transport.
  • Incorporation of Au@Ni/Co ZIF@PDA for efficient photothermal conversion.
  • Utilized exonuclease III-mediated target cycling and DNA Walker cascade amplification for signal amplification.
  • Integrated electrochemical, colorimetric, and photothermal detection modes.

Main Results:

  • Achieved ultrasensitive detection limits: 31.4 aM (electrochemical), 1.32 fM (colorimetric), and 1.14 fM (photothermal).
  • Demonstrated built-in self-verification and correction for enhanced reliability.
  • Validated with real samples, showing strong agreement with qPCR and high recovery rates (96.1-103.4%).

Conclusions:

  • The trimodal biosensing platform offers a novel, robust, and highly sensitive approach for pathogen detection.
  • The adaptive intelligent multisignal cross-checking enhances reliability for complex sample analysis.
  • This technology shows significant promise for agricultural disease diagnosis, food safety, and clinical diagnostics.