Coordination bond-driven rational design of biorecognition nanomaterials for precision localization and environmental safety in agriculture
Inefficient global localization systems for agricultural inputs pose significant risks to crop production and the environment. Achieving precise localization and environmental safety requires the design of a delivery system with tailored interactions of specific plant structures. Coordination bond-driven biorecognition nanoparticles have shown significant progress in the precise localization of plant cells and organelle surfaces, owing to their biocompatibility, high specificity, and cost-effectiveness. However, no comprehensive analysis and systematic summary of biorecognition-supported nanomaterials for precision localization and environmental safety have been reported. This review provides a comprehensive map of biorecognition-powered nanomaterials, encompassing their coordination-driven strategies, structure-property relationships, and artificial intelligence (AI)-supported designs. First, we highlight the fundamentals of biorecognition-supported nanomaterials in agriculture. We then discuss the applications of biorecognition ligand-supported nanomaterials, focusing on carbohydrates, amino acids, peptides, antibodies, and nucleic acids. Furthermore, we summarized the challenges of biorecognition-powered nanomaterials and their future directions. This study provides a foundation for biorecognition-based nanomaterials, thereby supporting the development of sustainable agriculture. • This review presents a coordination chemistry-driven strategy for designing biorecognition nanomaterials. • A systematic analysis of biorecognition ligands for targeting plant tissues. • The emerging role of AI and machine learning in discovering high-affinity biorecognition elements. • Design of biorecognition nanomaterials enables precision localization and environmental safety in agriculture.
Publication Details
- Journal
- Coordination Chemistry Reviews
- Published
- 2025-12-08
- DOI
- https://doi.org/10.1016/j.ccr.2025.217439
- Citations
- 1
- Primary Topic
- Biosensors and Analytical Detection
- Type
- article
- Field-Weighted Citation Impact
- 0.67