Thermodynamic Microenvironment Engineering in Mesoporous Nanoreactors to Enhance Biocatalysis for AI-Empowered...

Area of Science:

• Nanobiocatalysis
• Biointerface Science
• Chemical Engineering

Background:

• Enzyme-support microenvironments are crucial for nanobiocatalysis in pathogen detection.
• Understanding microenvironment modulation's effect on enzyme immobilization energetics is underexplored.

Purpose of the Study:

• To engineer mesoporous resorcinol formaldehyde nanospheres (mRFNSs) with tailored surface chemistry.
• To elucidate how microenvironment modulation dictates enzyme immobilization energetics and binding affinity.
• To develop an AI-enhanced diagnostic platform for ultrasensitive pathogen detection.

Main Methods:

• Architectural engineering of mRFNSs with controlled pore sizes (9.95 nm) and surface chemistry.
• Thermodynamic dissection of enzyme immobilization energetics, including binding constant (Ka) and Gibbs free energy (ΔG).
• Development of a ratiometric fluorescence immunoassay utilizing silicon quantum dots (SiQDs) and a convolutional neural network (CNN) for pathogen detection and classification.

Main Results:

• Betaine-tailored mRFNSs demonstrated optimal immobilization efficiency and activity, significantly reshaping binding energetics.
• Achieved a 4.01-fold enhancement in binding constant (Ka = 1.12 × 10^8 M^-1) and superior thermodynamic spontaneity (ΔG = -46.0 kJ mol^-1).
• Developed a ratiometric fluorescence immunoassay for ultrasensitive Salmonella typhimurium detection (100 CFU mL^-1) and portable classification (93.75% accuracy) using CNN analysis of smartphone-captured fluorescence.

Conclusions:

• Established a direct correlation between thermodynamic-microenvironment interactions and nanobiocatalyst performance.
• Demonstrated a blueprint for next-generation nanobiocatalysts by harmonizing enzyme-support microenvironments.
• Bridged biointerface science with AI-enhanced diagnostics for improved food safety and pathogen detection.