Dual fluorescence-enhanced AuAg-ZIF@Content structure cleavage-mediated ratiometric emission binding interpretable...

Area of Science:

• Materials Science
• Biotechnology
• Analytical Chemistry

Background:

• Biosensors are shifting from single-area breakthroughs to integrated performance improvements.
• Signal transduction and data processing are key challenges in fluorescent biosensor development.

Purpose of the Study:

• To develop a generalized biosensing platform integrating dual-enhanced fluorescent materials, ratiometric detection, and interpretable machine learning.
• To improve signal transduction and data processing for fluorescent material-based biosensors.

Main Methods:

• Synthesized AuAg-ZIF with doubly enhanced fluorescence via anti-galvanic reaction and ZIF-8 confinement.
• Prepared AuAg-ZIF@CD for ratiometric fluorescence detection based on ZIF-8 degradation.
• Developed an interpretable machine learning algorithm for automated data processing within 5 seconds.

Main Results:

• AuAg-ZIF@CD demonstrated ratiometric fluorescence detection of targets via ZIF-8 acid degradation.
• Achieved accurate glucose detection (R² = 0.95) using AuAg-ZIF@CD.
• Validated the platform in 48 clinical samples with high accuracy (AUC = 0.988).

Conclusions:

• The developed platform offers immediate, intelligent, and accurate detection capabilities.
• Integration of enhanced fluorescent materials and machine learning significantly advances biosensor technology.
• The platform shows strong potential for practical applications in clinical diagnostics.