This study presents efficient photocatalytic degradation of tetracycline (TC) via impregnation of Cu₂O nanoparticles on TiO₂ nanosheets/reduced graphene oxide nanocomposites. Different techniques such as PXRD, UV–Vis DRS, TEM, FESEM/EDX, Raman spectroscopy and N₂ physisorption were employed to identify synthesized samples. The results of PXRD and Raman analyses indicated that TNs had a pure anatase phase, and graphene oxide was changed to reduced graphene oxide during the hydrothermal procedure. In addition, UV–Vis DRS results proved that modification of TNs with rGO and Cu₂O facilitated the absorption of visible light. The best performance for the degradation of TC was obtained by the CGT(40) sample, which was 71.91% for visible and 83.1% for UV irradiation. The data obtained from degradation tests were modeled by the artificial neural network (ANN) as an intelligent tool. A three-layer network with 20 hidden neurons provided the best performance.

这项研究提出了通过在TiO ₂纳米片/还原的氧化石墨烯纳米复合材料上浸渍Cu ₂ O纳米粒子，有效地光催化降解四环素（TC）。采用不同的技术，例如PXRD，UV-Vis DRS，TEM，FESEM / EDX，拉曼光谱和N ₂物理吸附来鉴定合成样品。PXRD和拉曼分析的结果表明，TNs具有纯的锐钛矿相，并且在水热过程中氧化石墨烯变为还原的氧化石墨烯。此外，UV-Vis DRS结果证明了用rGO和Cu ₂修饰TNsO促进了可见光的吸收。CGT（40）样品获得了TC降解的最佳性能，可见光率为71.91％，紫外线辐射为83.1％。从降级测试获得的数据通过人工神经网络（ANN）作为智能工具进行建模。具有20个隐藏神经元的三层网络提供了最佳性能。