超级电容器
阴极
材料科学
阳极
电容
电解质
水溶液
储能
电化学
电极
功率密度
化学工程
纳米技术
尖晶石
导电体
箔法
桥接(联网)
电导率
电流密度
电解电容器
比能量
电容器
混合材料
能量密度
作者
Suresh Jayakumar,P. Chinnappan Santhosh,A.V. Radhamani
出处
期刊:Energy & Fuels
[American Chemical Society]
日期:2026-04-21
卷期号:40 (17): 9730-9742
标识
DOI:10.1021/acs.energyfuels.6c00992
摘要
The rising demand for reliable, affordable, and high-performance energy-storage solutions has fueled the development of aqueous zinc-ion hybrid supercapacitors (ZIHSs), which effectively operate between conventional supercapacitors and batteries. However, their practical advancement has been hampered by slow Zn2+ diffusion kinetics and a lack of cathode materials that facilitate fast charge transfer and long-term durability. In this study, we engineered a conductive hybrid cathode composed of spinel NiCo2O4 integrated with multilayer Ti3C2Tx MXene (NMX) to overcome the inherent conductivity limitations of NiCo2O4. The synergistic combination of redox-active NiCo2O4 with the highly conductive MXene scaffold offers abundant electrochemically accessible sites, shortened ion-transport pathways, and enhanced interfacial charge-transfer kinetics. The optimized hybrid electrode in a three-electrode setup shows a good electrochemical performance of 1250 F/g at 1 A/g. A ZIHS device, assembled with a Zn foil anode in an electrolyte containing 3 M KOH and 0.05 M ZnSO4, achieved an excellent specific capacitance of 183 F/g at 1 A/g, yielding an energy density of 73.4 Wh/kg and a power density of 851.2 W/kg. Notably, the device retained nearly 84.9% of its capacitance after 9000 cycles at 6 A/g. Moreover, postcycling analyses confirm structural stability and reveal Zn2+ insertion, sulfate adsorption, and K+ interaction, supporting a highly reversible hybrid storage mechanism. Furthermore, two NMX//Zn in series powered a red LED for 7 min, showing their practical feasibility. Collectively, these results indicate that NiCo2O4/Ti3C2Tx cathodes are promising platforms for future aqueous zinc-ion energy storage.
科研通智能强力驱动
Strongly Powered by AbleSci AI