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Related Experiment Video

Updated: Sep 10, 2025

Author Spotlight: Advancements in DNA Nanosensors &#8211; Addressing Sensitivity and Selectivity Challenges in Molecular Detection
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Electron Transfer-Tailored D-Band Center to Boost Nanozyme Catalysis for Interpretable Machine Learning-Empowered

Yuechun Li1, Chenxin Ji1, Zhaowen Cui1

  • 1College of Food Science and Engineering, Northwest A&F University, Yangling, Shaanxi, 712100, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|August 23, 2025
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Summary

A novel biosensor combines enhanced nanozymes and smart AI for ultrasensitive pathogen detection. This machine learning approach improves accuracy and sensitivity, offering a powerful tool against infectious diseases.

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AIEgensD‐band centerSHAPelectron transferimmunoassaymachine learningnanozymepathogens

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Area of Science:

  • Biotechnology
  • Nanotechnology
  • Machine Learning

Background:

  • Infectious diseases pose a significant global health challenge, necessitating advanced diagnostic tools.
  • Existing biosensing technologies often face limitations in sensitivity, accuracy, and real-world applicability.
  • There is a critical need for innovative biosensors that overcome the complexity-sensitivity-accuracy trade-off.

Purpose of the Study:

  • To develop an interpretable machine learning-empowered multimodal biosensor for ultrasensitive pathogen detection.
  • To synergize electron transfer-enhanced nanozymes and aggregation-induced emission luminogens (AIEgens) for improved biosensing performance.
  • To establish a nanozyme-AI co-design framework for enhanced biosensing paradigms.

Main Methods:

  • Engineering aminophenol formaldehyde resin nanobowls with monodisperse Pt nanoparticles to enhance peroxidase-like activity.
  • Integrating aggregation-induced emission luminogens (AIEgens) for cross-validated, anti-interference signal generation.
  • Employing a SHapley Additive exPlanations (SHAP)-guided eXtreme Gradient Boosting (XGBoost) algorithm to fuse multimodal signals.

Main Results:

  • Achieved a 3.4-fold enhancement in nanozyme peroxidase-like activity through electronic modulation.
  • Demonstrated a record-low detection limit for Salmonella typhimurium, surpassing classical immunoassays.
  • The AI algorithm enhanced sensitivity five-fold and achieved 100% diagnostic accuracy for positive samples.
  • SHAP analysis elucidated the synergistic mechanism and validated the AI's decision logic.

Conclusions:

  • The developed biosensor offers ultrasensitive and accurate pathogen detection, addressing limitations of current technologies.
  • The nanozyme-AI co-design framework represents a significant advancement in biosensing for public health.
  • This approach holds promise for rapid and reliable diagnostics in combating infectious disease outbreaks.