Nanoparticle-plant interaction as a key factor in the design process of herbicide nanoformulation

• Grant number: 25/02524-4
• Support Opportunities: Scholarships in Brazil - Master
• Start date: September 01, 2025
• End date: February 28, 2027
• Field of knowledge: Biological Sciences - Biochemistry - Chemistry of Macromolecules
• Principal Investigator: Leonardo Fernandes Fraceto
• Grantee: Brian Cintra Cardoso
• Host Institution: Instituto de Ciência e Tecnologia. Universidade Estadual Paulista (UNESP). Campus de Sorocaba. Sorocaba , SP, Brazil
• Associated research grant: 21/10639-5 - Center for Research on Biodiversity Dynamics and Climate Change, AP.CEPID

Abstract

Weeds represent a significant challenge for modern agriculture, which is responsible for supplying food and raw materials to meet the demands of the growing global population. Widely used for weed control, herbicides have raised increasing challenges for the sustainable development of agriculture. Herbicide resistance in weeds and the environmental impact of excessive use of these products have become global concerns. The former reduces the effectiveness of treatments and increases production costs, while the latter causes contamination of terrestrial and aquatic systems, leading to toxicity issues for non-target organisms. In this context, nanotechnology has emerged as an innovative approach for developing more effective herbicides, enabling dose reduction, and promoting the sustained release of active ingredients, thereby minimizing environmental impacts. This project aims to investigate herbicide nanoformulations' absorption and translocation mechanisms in weeds, correlating their physicochemical properties with leaf morphology and structural barriers, such as the cuticle and leaf wax, to guide the design of nanoformulations based on specific targets. Advanced techniques will be employed, such as confocal microscopy and fluorometry, to track nanoparticle absorption and structural characterization of leaves using scanning electron microscopy and infrared spectroscopy. These analyses will enable comparisons of how different nanosystems interact with weed leaves and determine which factors influence the efficiency of nanoparticle absorption and translocation. A machine learning-based predictive model will also be developed to estimate nanoparticle absorption efficiency, providing an innovative tool to optimize future formulations. Artificial intelligence algorithms will facilitate the identification of the most relevant variables in the absorption process, reducing the need for lengthy experimental trials and directing the development of more effective and sustainable nanoherbicides. With this approach, the study's results are expected to contribute significantly to the formulation of herbicides based on the "safe-by-design" concept, ensuring more excellent selectivity, reduced environmental impact, and more efficient weed control. The findings from this research could have direct applications in developing new strategies for sustainable weed management, focusing on optimized formulations that enable efficient delivery of active ingredients to their target sites. Thus, this study has the potential to advance agriculture and nanotechnology applied to the agricultural sector, supporting the implementation of safer, more effective, and more sustainable nanoherbicides.