谷歌浏览器插件
订阅小程序
在清言上使用

Synthesis and electrochemical performance of biomass-derived porous carbon materials for supercapacitors

Yalin Zhang,Yanqing Cai, Tianwang Li, Mengqian Wang,Xinggang Chen,Ying Xu

Journal of Materials Science: Materials in Electronics(2024)

引用 0|浏览2
暂无评分
摘要
Biomass-derived porous carbon materials are widely used as electrode materials for supercapacitors due to their low cost, wide availability, rich pore structures, and good stability. Different biomass-derived porous carbon materials have variations in compositions and structures, resulting in different electrochemical performances. Here, four types of biomass-derived porous carbon materials, namely corn stalk-derived porous carbon (CC), bamboo-derived porous carbon, peanut shell-derived porous carbon and rice straw-derived porous carbon, were prepared using raw materials such as corn stalk powders, bamboo powders, peanut shell powders and rice straw powders, respectively. The preparation process was carried out through high-temperature carbonization and activation methods. The prepared carbon electrode samples were characterized by XRD, Raman, SEM and BET, and the three-electrode performances and the assembled symmetric supercapacitors were also investigated by a series of electrochemical testing techniques. The results indicate that among the four biomass-derived porous carbon materials, CC exhibits the best electrochemical performances, and the specific capacitance is 103 F g −1 at a current density of 0.1 A g −1 . The assembled symmetric supercapacitors demonstrate a specific capacitance of 99 F g −1 at a current density of 0.1 A g −1 , a specific mass power density of 25 W kg −1 and a specific mass-energy density of 3.44 Wh kg −1 . After 20,000 cycles, the capacitance retention rate remains at 104%, which shows an increase. Therefore, the electrode samples prepared from corn stalk-derived porous carbon exhibit high specific energy, high specific power and long cycle life and have promising prospects for development in supercapacitors.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要