铕
再分配(选举)
氯
催化作用
吸附
选择性
析氧
化学
氯化物
无机化学
电化学
电极
离子
物理化学
有机化学
政治学
政治
法学
作者
Lingshu Qiu,Fan Zhang,Qian Yang,Weiwei Han,Yi He,Xiangdong Feng,Jianxin Jin,Yongping Gu,Shaoyun Hao,Xingwang Zhang
标识
DOI:10.1016/j.cej.2023.142623
摘要
• Ru 0.9 Eu 0.1 O x @TP enhances catalytic selectivity and stability towards chlorine evolution reaction. • Doping rare earth metal Eu into RuO 2 causes a strong charge redistribution between Eu and Ru. • The DFT calculations prove that the doped Eu could improve the adsorption ability with OCl and suppress OOH intermediates. The chlorine evolution reaction (CER) plays a significant role in the generation of Cl 2 and active chlorine during chlor-alkali electrolysis. However, CER competing with oxygen evolution reaction (OER) on the anode side always suffers from low selectivity due to the adsorption of OCl and OOH intermediates on active sites. Herein, Eu-doped RuO 2 catalysts on Ti plate (TP) with excellent CER performance is reported by the rational design of doping rare earth metal Eu into RuO 2 , which induces a strong charge redistribution between Eu and Ru and tunes the adsorption ability. The representative Ru 0.9 Eu 0.1 O x @TP reveals a yield of active chlorine of 12.8 mg h −1 with a high selectivity of 96.5% toward CER at 20 mA cm −2 , and a mass activity of about 2.94 times higher than that on Ru-based DSA in 0.5 M chloride-containing solution. Additionally, Ru 0.9 Eu 0.1 O x @TP shows a robust stability of over 225 h at 1000 mA cm −2 in 0.5 M of H 2 SO 4 . The microstructural characterizations and density functional theory (DFT) reveal that doping Eu into RuO 2 decreases the charge on Ru sites, thus enhancing the adsorption ability with OCl and supressing OOH. In addition, Ru 0.9 Eu 0.1 O x @TP exhibits the activity toward NH 4 + -N removal rate with a degradation rate of 95% in 1 h at 20 mA cm −2 , suggesting its potential application for waste-water treatment. Moreover, the expected service life of the electrode is estimated to exceed 13.4 years.
科研通智能强力驱动
Strongly Powered by AbleSci AI