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

Abstract

High-entropy nanozymes (HENs) have emerged as a revolutionary class of bio-inspired catalysts that integrate multi-enzyme mimetic activities with environmental responsiveness, creating transformative opportunities for next-generation biosensing technologies. This review systematically examines recent breakthroughs in the rational design of programmable catalytic systems and stimulus-responsive HENs architectures. We critically analyze innovative synthetic strategies, including cation-exchange templating, microwave-assisted solvothermal synthesis, and laser ablation techniques, which enable precise control over compositional complexity and surface topological features. Particular emphasis is placed on elucidating the synergistic mechanisms of multi-metallic coordination and defect engineering that enhance catalytic efficiency in biological microenvironments. Advanced applications are discussed in the context of ultra-sensitive detection of critical biomarkers such as dopamine and alkaline phosphatase, demonstrating their potential for point-of-care diagnostic implementations. While addressing current challenges in biocompatibility optimization and standardized manufacturing protocols, we propose novel solutions incorporating surface functionalization paradigms and machine learning-driven design optimization. Looking forward, the strategic integration of HENs with artificial intelligence-enhanced biosensing platforms and multimodal detection systems is envisioned to pioneer new frontiers in precision medicine and intelligent diagnostic paradigms, potentially revolutionizing disease surveillance and personalized healthcare solutions.

Keywords: Active sites; Biomarker detection; Catalysis; High-entropy; Machine learning.