锰
X射线光电子能谱
催化作用
兴奋剂
傅里叶变换红外光谱
铵
无机化学
化学
材料科学
化学工程
有机化学
工程类
光电子学
冶金
作者
Ya Cheng,Yongzhi Zhang,Weiyao Xiong,Tinglin Huang,Huan Tang
标识
DOI:10.1016/j.jclepro.2021.130091
摘要
Mn-based co-oxides filter media with different Fe/Mn molar ratios were synthesized to investigate the influence of Fe doping on the catalytic activities for ammonium and manganese removal. A pilot-scale filter column system was adopted to prepare the filter media and the actual Fe amount in filter media was regulated during the start-up period. Mn-based co-oxides with a Fe/Mn molar ratio of 1:2 presented excellent catalytic property, and the removal efficiency of the ammonium and manganese of the filter reached over 90% on the 7th and 15th days, respectively. A large Fe doping ratio (Fe/Mn = 2:2) was not conducive to the improvement of the catalytic activity. An extended characterization of the Mn-based co-oxides samples with different Fe/Mn molar ratios by X-ray diffraction (XRD), Fourier transform-infrared spectroscopy (FTIR), and X-ray photoelectron spectroscopy (XPS) was performed. The modification of Fe in the Mn-based co-oxides filter media was found to inhibit the formation of crystal clusters, increase the proportion of Mn3+ content on the surface, and promote the formation of defects of the lattice oxygen. These modifications led to the enhancement of the electron transfer capacity in the catalytic reaction. In addition, the doped Fe mainly existed in the form of FeOOH and Fe2O3. With increased Fe doping, the amount of FeOOH increased and Fe2O3 decreased. Fe2O3 facilitated interfacial charge transfer, which may be the reason for the high catalytic activity. These results indicate that Fe doping is an effective method for accelerating catalytic activity formation during the start-up period of Mn-based co-oxides filters for ammonium and manganese removal. This study provides new insights on the interaction mechanisms between Fe and Mn-based co-oxides and on the catalytic oxidation of ammonium and manganese.
科研通智能强力驱动
Strongly Powered by AbleSci AI