Magnetic carbon nanoparticles derived from Co-based metal-organic frameworks for magnetic solid-phase extraction and determination of phenylurea herbicides

Magnetic carbon nanoparticles composed of Co, CoO and graphitic carbon (Co/CoO@C) were prepared by direct carbonization of Co-MOF without the need of extra carbon precursors and magnetic particles under N2 atmo-sphere. The Co/CoO@C prepared by self-sacrificial template method possess high mesoporous architecture, a large number of adsorption sites, good graphitization degree and strong magnetic response. The material was constructed as an adsorbent for magnetic solid-phase extraction of four phenylurea herbicides from water and rice samples prior to high-performance liquid chromatography analysis. A series of experimental parameters affecting the extraction efficiency were optimized. Under optimal conditions, the established method revealed extremely fast extraction times (10 s), low limits of detection (0.05 mu g/L, S/N = 3), good linearity (0.1-100 mu g/L) within the linear correlation coefficient range of 0.9990-0.9997 and good precision (RSD < 5.72 %). The results confirmed that the Co/CoO@C exhibited an excellent adsorption capability and could be a stable and effective adsorbent for preconcentrating trace levels of phenylurea herbicides from water and rice samples. Moreover, the excellent adsorption performance together with low-cost, facile synthesis method endowed the Co/CoO@C great application prospect in sample preparation.

Automated summary

Magnetic carbon nanoparticles composed of Co, CoO, and graphitic carbon were prepared via direct carbonization of Co-MOF without the need for additional carbon precursors or magnetic particles. The resulting material, Co/CoO@C, exhibited high mesoporous architecture, a large number of adsorption sites, good graphitization degree, and strong magnetic response. It was employed as an adsorbent for magnetic solid-phase extraction of phenylurea herbicides and showed excellent adsorption capability, fast extraction times, low limits of detection, good linearity, and precision.