石墨烯
材料科学
镧
超级电容器
复合数
氧化物
镍
氧化镍
兴奋剂
氧化锡
锡
冶金
化学工程
无机化学
纳米技术
复合材料
电容
光电子学
化学
电极
工程类
物理化学
作者
Aparna Paul,Souvik Ghosh,Haradhan Kolya,Chun-Won Kang,Naresh Chandra Murmu,Tapas Kuila
标识
DOI:10.1016/j.est.2022.105526
摘要
Doping is the most promising approach to improve the electrochemical performance of metal oxide-based supercapacitor. Rare-earth element doping is helpful to improve the electrochemical performance of metal oxides due to the presence of unique 4f electronic configuration. Herein, Lanthanum (La) doped Nickel‑tin oxide/reduced graphene oxide (LNSRG) composite was successfully prepared via one-step hydrothermal method. The optimized LNSRG 2 composite exhibited ∼1238 F g −1 specific capacitance at 3 A g −1 current density using 6 M KOH as electrolyte. The effect of electrolyte's pH on the electrochemical performances was investigated using various electrolytes. The asymmetric supercapacitor (ASC) device fabricated with LNSRG 2 as positive electrode material and sonochemically reduced graphene oxide (SRGO) as negative electrode material exhibited energy density of ∼38 W h kg −1 at a power density of ∼870 W kg −1 in the potential window of ∼1.6 V using 1 M Na 2 SO 4 as electrolyte. The ASC device retained ∼75 % of its initial capacitance after 10,000 continuous charge-discharge cycles at 15 A g −1 current density. Two ASC devices connected in series was capable of powering a 1.8 V red LED light. Controlled nanoflakes morphology with highest relative ratio of oxygen vacancies exhibited highest specific capacitance. ASC device operated in the potential window of 0–1.6 V in 1 M Na 2 SO 4 electrolyte. Two ASC devices in series are capable lighting up a red LED of 1.8 V. • ASC device exhibited maximum energy density of ∼38 W h kg −1 at a power density of 870 W kg −1 using 1 M Na 2 SO 4 as electrolyte. • The fabricated device can retain ∼75 % of its initial capacitance after continuous 10,000 GCD cycles • Two ASC devices connected in series lighting up a red LED indicator of 1.8 V.
科研通智能强力驱动
Strongly Powered by AbleSci AI