Synthesis and Characterization of Nile Red Carbon Dots for Fluorescent Strip-Based Detection of Microplastics in Food Products

Abstract

Microplastic (MP) contamination in food has become an emerging food safety concern due to its ubiquity and potential health implications. This study presents the development of a novel dual-mode detection platform based on Nile Red-functionalized carbon dots (NR-CDs) for the rapid identification of microplastics in food matrices through fluorescence microscopy and a strip-based visual detection approach. Low-density polyethylene (LDPE) and polypropylene (PP) microplastics (<100 µm) were cryogenically prepared from standard pellets and characterized by FTIR-ATR, showing polymer match scores of 0.935 and 0.912, respectively. Model food samplesmilk, tetra pack milk, bottled juice, and cheesewere spiked with 3 mg of MPs per 30 mL (or 15 g for cheese) and successfully visualized via Nile Red staining under green excitation (525–545 nm), producing strong red fluorescence without background interference. The synthesized NR-CDs exhibited a broad absorption near 280 nm and red photoluminescence under green light excitation, with a mean particle size of ~2–4 nm confirmed by TEM. NR-CDs were incorporated onto filter paper strips, which enabled distinct red fluorescence under fluorescence microscope in the presence of as little as 0.2 mg of MPs in 2 mL water. The developed detection strips provided a simple, rapid, and low-cost method for on-site screening of MPs in food. This work demonstrates, for the first time, the application of NR-CDs for portable, semi-quantitative detection of microplastics, offering a promising tool for real-world food safety monitoring and surveillance.

Presenters

Arvind Kumar
Associate Professor, Dairy Science and Food Technology, Banaras Hindu University, Uttar Pradesh, India

Details

Presentation Type

Paper Presentation in a Themed Session

Theme

2026 Special Focus—Living with Water: Food and Life

KEYWORDS

Microplastics, Carbon Dots, Fluorescence Detection, Nile Red, Strip-Based Sensing, Food