食品中酚类雌激素(PEEs)测定需进行样品前处理，分子印迹聚合物(MIPs)可选择性富集PEEs。但传统MIPs水容性差、传质速率慢、结合容量低和分离困难。本项目拟合成环境响应性功能单体，以PEEs为模板分子，在Fe3O4表面，结合活性聚合、互穿聚合物网络和接枝聚合物刷技术构建环境响应分子印迹磁性复合微球(ER-Fe3O4-MIMs)。利用分子动力学研究环境刺激下功能单体与模板分子相互作用能、力场和结构变化，阐述印迹空穴在刺激信号下表现的自组装性、协同性和应答性，建立数学模型从分子水平上说明识别机理。发展ER-Fe3O4-MIMs前处理方法，结合毛细管电泳(CE)，实现对食品中PEEs的特异识别、高效富集、快速分离和高灵敏度检测。本项目将磁分离、智能高分子材料、MIPs和CE集成于食品领域，构建灵敏度高、水体识别力强、环境友好和快速分离新方法，实现对食品中痕量污染物快速分离高灵敏检测。

The pollution of phenolic estrogens (PEEs) for foods became more and more serious. The detection of PEEs in foods need complex pretreatment. Molecularly imprinted polymers (MIPs) have the ability to selectively enrich PEEs. However, the traditional MIPs has the disadvantages of insoluble in water, low mass rate, low adsorb ability, and difficult separation. The aims of this project was to synthesis environment-responsive functional monomers, and then develop (dual/multiple) environment-responsive and magnetic molecularly imprinted microspheres (ER-Fe3O4-MIMs) on the Fe3O4 surface by living polymerization, interpenetrating polymer networks, and grafted polymers brushes methods. The responsive mechanism of functional monomer structure was investigated by molecular dynamics simulation and advanced analysis method under different environment stimulation. In order to explore the recognition mechanism of ER-Fe3O4-MIMs from molecular level, the self-assemble, coordination, and response of molecularly imprinted sites were explained, and their relationships were further researched for developing relevant mathematical models. Food samples pretreatment method of ER-Fe3O4-MIMs was also developed. Combination of ER-Fe3O4-MIMs and capillary electrophoresis (CE) can achieve the goals of specific recognition, higher enrichment, quickly separation and high sensitivity detection for PEEs in food samples. The technologies of magnetic separation, environment-responsive polymers, MIPs and CE were applied in food fields in this project, and a novel detection method with high sensitive, high ability recognition in water, and simple separation was developed. Thus, the method of this project can detect trace PEEs in food samples.