Nanoengineered catalytic metal-organic frameworks for food safety monitoring: rational design strategies and biosensing applications

This is the authors' abstract. We don't add key points for this paper.

Catalytic metal-organic frameworks (cMOF) represent a rapidly advancing class of coordination materials that combine the structural versatility of coordination polymers with intrinsic enzyme-like activity. Built from coordinatively defined metal nodes and tunable organic ligands, cMOF exhibit highly porous architectures, ultrahigh surface areas, tunable coordination environments, and accessible active sites, enabling their application as biosensing platforms for food monitoring. This review comprehensively surveys developments in cMOF-based biosensors over the past five years for food safety monitoring, covering detection of foodborne pathogens (e.g., E. coli, Salmonella ), natural toxins (mycotoxins, marine toxins), chemical residues (pesticides, antibiotics, growth hormones, adulterants), environmental pollutants (heavy metals, plasticisers), and food quality indicators (biogenic amines, volatile organic compounds, oxidation markers). The review emphasises coordination chemistry principles that govern rational cMOF design, including metal-node selection, metal-ligand coordination strength, active-site engineering via ligand functionalisation, electronic-structure modulation, defect introduction, and composite formation with conductive or catalytic materials to enhance catalytic activity, selectivity, and electrical conductivity. Key challenges are critically examined: mass transport and diffusion limitations, active-site heterogeneity, metal leaching, hydrolytic instability, and poor electrical conductivity, all of which are rooted in coordination-environment stability, metal-ligand bond strength, and ligand design strategies. Future perspectives highlight strategies to enhance the robustness of the coordination sphere, active-site reproducibility, scalability, and real-sample applicability, while integrating computational modelling and AI/ML approaches to accelerate the rational design and optimisation of cMOF-based biosensors, improve catalytic performance, ensure structural consistency, and enhance predictive sensing reliability. These advancements position cMOF-based biosensors as promising POC tools for on-site food safety surveillance.