مشخصات پژوهش

Mercury contamination in food remains a critical public health concern due to its extreme toxicity and bioaccumulation in the environment. Developing rapid, sensitive, and environmentally sustainable methods for its detection is of great importance. In this study, a biodegradable nanocomposite comprising silver oxide nanoparticles and boron-doped carbon quantum dots derived from orange peel waste was introduced. This material served as a high-performance sorbent for pipette-tip solid-phase extraction of inorganic mercury from food samples. Synthesized via a green hydrothermal process using agricultural waste, the resulting ternary nanohybrid leverages the synergistic affinity of Ag2O and OP-CDs toward Hg (II), yielding exceptional extraction efficiency. Notable advantages included rapid extraction kinetics, minimal consumption of sorbent and solvent, and the elimination of toxic reagents, thereby fully aligning with the principles of green analytical chemistry. Extraction parameters were optimized using chemometric and statistical approaches, specifically artificial neural networks and evolutionary polynomial regression, to ensure robust performance. Under optimized conditions, the method represented trace-level detection capability and excellent precision in diverse food matrices. Comprehensive green metric evaluation confirms the sustainability of the approach. This sustainable, AI-enhanced extraction strategy provides an efficient and practical solution for trace mercury analysis in food, with broad potential in food safety monitoring.