Pharmaceutical-Food Homologous Plant-Derived Carbon Dots: A Sustainable Nanoplatform for Integrated Detection and Remediation of Environmental Pollutants
刊名 Asian Agricultural Research
作者 Yang CAO, Jiefang HE, Chao ZHAO
作者单位 Key Laboratory for Information System of Mountainous Areas and Protection of Ecological Environment, Guizhou Normal University; Guizhou Normal University
DOI 10.19601/j.cnki.issn1943-9903.2025.06.008
年份 2025
刊期 6
页码 36-39
关键词 Pharmaceutical-Food Homologous Plant-Derived Carbon Dots (P-CDs), Environmental pollutant detection, Green synthesis
摘要 Pharmaceutical-Food Homologous Plant-Derived Carbon Dots (P-CDs) have emerged as revolutionary nanomaterials in environmental pollutant management, demonstrating transformative potential for green chemistry and sustainable material applications. These carbon dots establish an innovative technical framework by integrating dual "detection-remediation" functionalities through eco-friendly synthesis and high-value conversion of medicinal-edible plants and agroforestry waste. Their core advantages originate from structural templating effects induced by natural functional groups (polyphenols, amino acids) in plant precursors combined with heteroatom self-doping, which synergistically optimizes optical properties. This combination achieves quantum yields ranging from 3.06% to 84.9% and detection sensitivities spanning nanomolar to micromolar concentrations. In pollutant detection applications, P-CDs enable ultrasensitive identification of heavy metals (Hg2+, Cu2+, Fe3+) and organic contaminants (pesticides, antibiotics) through multi-mechanistic interactions including static quenching (SQ), dynamic quenching (DQ), and Förster resonance energy transfer (FRET). However, technological translation faces critical challenges including quantum yield heterogeneity (>40-fold variation), matrix interference in complex environmental samples (signal drift exceeding 12%), and scalability-related process inconsistencies. Future research priorities should focus on three key areas: standardization of synthesis protocols, development of surface passivation strategies (e.g., SiO2 encapsulation), and optimization of heterojunction designs to enhance interference resistance. The integration of in situ characterization techniques (particularly X-ray absorption spectroscopy) with machine learning-driven parameter optimization could significantly refine detection-remediation synergies. Concurrently, establishing a comprehensive lifecycle assessment framework becomes imperative for evaluating environmental impacts and scalability potential. This technology pioneers a sustainable paradigm for pollution control by bridging the gap between nanomaterial innovation and industrial deployment, thereby accelerating progress toward global ecological security objectives.