表面工程调控合成CQDs修饰n-n型MFe12O19(M=Sr, Ba, Cu, Pb)/CeO2磁分离光催化剂及智能预测技术研究

半导体光催化技术是有效缓解能源危机和净化环境的途径之一。由于单组分的光催化剂很难同时满足高效太阳光利用率、高效光生载流子分离效率及可回收等要求，而n-n型磁分离光催化剂可克服单组分光催化剂的固有缺陷，引起了科研人员的广泛关注。本项目拟通过表面工程调控技术合成碳量子点(CQDs)修饰的n-n型MFe12O19(M=Sr, Ba, Cu, Pb)/CeO2磁分离光催化剂，通过表面修饰增强光催化剂的电荷转移和分离效率，进而提高其光催化效率。基于中子散射技术和相关磁性表征，分析光催化剂的晶体结构和磁学性质，深入揭示双异质结型磁分离光催化剂的形成机理及磁性组分在调制异质结生长、界面结构与性能中所起的关键作用；探讨磁分离光催化剂的磁学、发光与光催化活性间的内在关联机制；采用最优的神经网络算法建立性能优化预测模型，提出CQDs含量、磁性组分与双异质结构建之间对增强磁分离光催化剂光催化活性的协同作用机制。

Semiconductor photocatalytic technology is one of the effective ways to alleviate the energy crisis and purify environment. It is well known that it is difficult for one-component photocatalyst to simultaneously meet the requirements of high efficiency of solar energy utilization, high efficiency of photogenic carrier separation and recyclability, and n-n magnetic separation photocatalyst can overcome the inherent defects of a single component of photocatalyst, it has attracted extensive attention of researchers. This project is to synthesize n-n MFe12O19(M=Sr, Ba, Cu, Pb)/CeO2 magnetically separated photocatalyst modified by carbon quantum dots (CQDs) through surface engineering regulate and control technology. The charge transfer and separation efficiency of MFe12O19(M=Sr, Ba, Cu, Pb)/CeO2 magnetically separated photocatalyst were enhanced by surface modification, and then improve its photocatalytic efficiency. Based on the neutron scattering technique and related magnetic characterization methods, the crystal structure and magnetic properties of CQDs/MFe12O19(M=Sr, Ba, Cu, Pb)/CeO2 photocatalyst for magnetic separation were analyzed, and the formation mechanism of double heterojunction CQDs/MFe12O19(M=Sr, Ba, Cu, Pb)/CeO2 photocatalyst for magnetic separation and the key role of magnetic components in the growth, interface structure and performance of the modulated heterojunction were further revealed. The intrinsic correlation mechanism between the magnetics, luminescence and photocatalytic activity of the photocatalyst was discussed. The optimal neural network algorithm was used to establish the performance optimization prediction model, and the synergistic mechanism of carbon quantum dot content, magnetic component and double heterostructure construction to enhance the photocatalytic activity of magnetic separation photocatalyst was proposed.