Detection of micro- and nanoplastic contamination in water and vegetables using sers coupled with nanoparticles and nanocellulose composites

Abstract

The widespread occurrence of micro- and nanoplastic particles (MNPs) in food systems has raised significant concerns regarding their potential impacts on the environment and human health. However, the reliable detection and quantification of MNPs remain highly challenging because of their small size, heterogeneous chemical composition, and the complexity of food matrices. Existing analytical methods often suffer from labor-intensive sample preparation, dependence on expensive instrumentation, or insufficient sensitivity for nanoscale detection. Therefore, there is a critical need for rapid, sensitive, and cost-effective detection methods that can be applied directly to real-world food systems. This dissertation presents the development of a surface-enhanced Raman spectroscopy (SERS)-based sensing platform integrated with cellulose-based materials for detecting MNPs in food matrices. Initially, a SERS method utilizing gold–silver core–shell nanoparticles (Au@Ag) was developed to detect polystyrene (PS) and polyethylene (PE) in both water and leafy vegetables. The method successfully enabled the simultaneous detection of both polymers, achieving limits of detection ranging from approximately 12–50 mg kg⁻¹ for PS and 173–744 mg kg⁻¹ for PE. These results demonstrated the feasibility of SERS for the rapid detection of mixed MNPs. To further improve sensitivity and reproducibility, bacterial cellulose (BNC) was employed as a functional scaffold for the in-situ synthesis of Au@Ag nanoparticles. The resulting BC-based SERS substrate exhibited more uniform nanoparticle distribution and superior plasmonic performance. This enhanced platform enabled reliable detection of MNPs in kale samples at concentrations as low as 4 mg kg⁻¹, with estimated limits of detection of 1.22 mg kg⁻¹ for PS and 3.95 mg kg⁻¹ for PE, showing a significant improvement compared to conventional nanoparticle-based systems. In addition, an ecosystem-informed strategy was developed to enhance bacterial cellulose production using a multi-kingdom microbial consortium. By integrating acetic acid bacteria, yeasts, and cyanobacteria, the optimized system achieved a maximum cellulose yield of 1.69 g L⁻¹, along with improved material properties, such as uniform thickness (2.56 mm) and reduced water vapor permeability. These advancements support the scalable production of high-quality cellulose substrates for sensing applications. Overall, this work demonstrates that integrating SERS with engineered cellulose-based substrates provides a practical, sensitive, and scalable approach for detecting MNPs in complex food matrices. The findings contribute to the advancement of detection technologies for emerging contaminants and support the development of sustainable strategies for food safety monitoring and environmental analysis.