High-entropy nanozyme biosensors: Machine learning-assisted design and stimulus-responsive applications.

Area of Science:

• Materials Science
• Nanotechnology
• Biochemistry

Background:

• High-entropy nanozymes (HENs) are bio-inspired catalysts with integrated multi-enzyme activities and environmental responsiveness.
• They present significant opportunities for developing next-generation biosensing technologies.

Purpose of the Study:

• To systematically review recent advancements in the rational design of programmable and stimulus-responsive HENs.
• To analyze innovative synthetic strategies and elucidate synergistic mechanisms for enhanced catalytic efficiency.

Main Methods:

• Review of innovative synthetic strategies: cation-exchange templating, microwave-assisted solvothermal synthesis, laser ablation.
• Analysis of synergistic mechanisms: multi-metallic coordination and defect engineering.
• Discussion of advanced applications in biomarker detection (dopamine, alkaline phosphatase).

Main Results:

• Precise control over compositional complexity and surface topological features of HENs.
• Enhanced catalytic efficiency in biological microenvironments due to synergistic effects.
• Demonstrated ultra-sensitive detection of critical biomarkers for point-of-care diagnostics.

Conclusions:

• HENs show great potential for point-of-care diagnostics and precision medicine.
• Future directions include surface functionalization, machine learning optimization, and integration with AI-enhanced platforms.
• Addressing challenges in biocompatibility and manufacturing is crucial for widespread adoption.