光催化
材料科学
石墨烯
拉曼光谱
化学工程
核化学
纳米复合材料
可见光谱
吸附
纳米颗粒
漫反射红外傅里叶变换
氧化物
布鲁克特
锐钛矿
纳米技术
化学
催化作用
有机化学
物理
工程类
光学
冶金
光电子学
作者
Fatemeh Firouzi,Azadeh Ebrahimian Pirbazari,Fatemeh Esmaeili Khalil Saraei,Fatemeh-Sadat Tabatabai-Yazdi,Amin Esmaeili,Ziba Khodaee
标识
DOI:10.1016/j.jece.2021.106795
摘要
In the present study, binary nanocomposites were prepared by hydrothermal growth of titanium dioxide nanosheets (TNs) on different amounts (5, 10, and 15 mg) of monolayer graphene oxide (TNs/rGO(x)). Cadmium sulfide (CdS) nanoparticles with various Cd/Ti molar ratios (0.35, 0.70, 1.40) were impregnated on the TNs/rGO(x) samples by the hydrothermal procedure. The formation of anatase phase for TNs, reduction of graphene oxide, and synthesis of CdS nanoparticles were proved by powder x-ray diffraction (PXRD) and Raman analyses. The photoluminescence (PL) and diffuse reflectance spectroscopy (DRS) analyses showed that the simultaneous presence of rGO and CdS in the synthesized samples decreased the electron-hole recombination and extended the spectral response of TNs to the visible light region. The highest obtained percentage removal of tetracycline (TC) (as a pharmaceutical pollutant model) was 84% under 180 min of visible light irradiation in the presence of the CdS-TNs/rGO(5) sample. Active species scavenging tests showed that O 2 •- was major active species in the removal process. In addition, the application of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in modeling the removal of tetracycline (TC) was evaluated in this study. The experimental results confirmed that the integration between adsorption and photocatalysis processes leads to a significant impact on the removal of TC. • Visible-light driven photocatalysts were fabricated using hydrothermal method. • TiO 2 nanosheets (TNs) and CdS semiconductor were synthesized on graphene layer. • The synthesized samples applied to treat tetracycline (TC) wastewater. • The percentage removal of TC was obtained 84% under visible light irradiation. • The removal process was modeled using two strong intelligence tools.
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