LEAPS-MPS: Tuning Curvature-Dependent Optical Selectivity of Functionalized Metal Nanoparticles for Biomimetic Recognition of Bacterial Lipopolysaccharides

About This Grant

NON-TECHNICAL SUMMARY: Many of the world’s most dangerous bacteria carry hidden warning labels on their surface—special types of molecules called lipopolysaccharides. Detecting this molecular badge is one of the most reliable ways to identify harmful microbes before they have a chance to spread. However, most current technologies for detecting lipopolysaccharides are too slow, too costly, or not sensitive enough to reliably protect food, water, and healthcare systems. This project tackles that challenge by creating innovative materials called nanoparticles, which are so small that thousands could fit across a single human hair. These gold and silver nanoparticles are carefully shaped and engineered to recognize and bind to lipopolysaccharide molecules, similar to how a key fits into a lock. By precisely controlling the size and surface features of these nanoparticles, the research aims to develop rapid, accurate, and affordable sensors for bacterial contamination. These tools have the potential to offer early warnings about invisible threats, improving public health responses before outbreaks occur. By advancing the science of material design and developing better ways to protect public systems, this work directly supports national priorities in health, security, and scientific innovation. The knowledge and technologies generated through this research could contribute to a safer, healthier future, and will train the future STEM workforce. Finally, this work includes development of an AI-based virtual lab which that be incorporated into outreach activities. TECHNICAL SUMMARY: This project focuses on the rational design and synthesis of curvature-engineered gold and silver nanoparticles with diverse morphologies, including spheres, rods, prisms, and concave structures, to create advanced biomaterials for the selective and ultra-sensitive detection of bacterial lipopolysaccharides. Precise control over nanoparticle morphology and surface curvature, combined with tailored chemical functionalization, is expected to significantly enhance molecular recognition at the nano–bio interface, resulting in improved binding affinity and selectivity toward lipopolysaccharide targets. This research will employ mechanochemical synthesis, seed-mediated growth, and template-assisted fabrication to produce nanoparticles with finely tuned dimensions and well-defined shapes. These nanomaterials will be further functionalized with biomimetic ligands, such as synthetic peptides and molecularly imprinted receptors, designed to mimic natural recognition processes. Advanced spectroscopic, microscopic, and analytical techniques will characterize nanoparticle geometry, surface chemistry, ligand presentation, and sensor performance. Quantitative affinity assays and computational modeling will elucidate how nanoparticle shape and curvature influence ligand density, accessibility, binding affinity, and selectivity toward lipopolysaccharides. The project aims to establish robust structure–function relationships, linking nanoparticle geometry and surface engineering to biosensor sensitivity, selectivity, and operational stability in complex biological and environmental samples. Conventional detection methods often require lengthy culturing steps or sophisticated laboratory infrastructure, limiting their effectiveness in time-sensitive scenarios. By contrast, these nanomaterial-based sensors offer real-time, point-of-need diagnostics, potentially transforming how we monitor pathogens in healthcare, environmental surveillance, and the food industry, ultimately reducing morbidity, mortality, and economic burden. Prototype sensors developed from these shape-controlled nanomaterials will enable rapid optical or electrochemical detection of bacterial contamination. This capability is crucial for addressing urgent global challenges related to microbial resistance, waterborne diseases, and food safety. This interdisciplinary effort will advance the fundamental understanding of biomaterials and biosensing, with broad implications for diagnostic technologies, environmental monitoring, and public health safeguarding. In addition, this work will train the future STEM workforce in science by including development of an AI-based virtual lab which that be incorporated into outreach activities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.