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Morphologically engineered mixed metal oxides on carbon fibers as a binder-free electrode for diffusion capacitance-dominated hybrid supercapacitors

Journal 2025 · IF 11 · 发表于 2025 年 8 月 13 日
作者: Arishi Mohammad
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摘要

Engineering the morphology of spinel mixed metal oxides is a critical strategy for developing high-performance hybrid supercapacitors as they enhance both energy storage performance and cyclic stability. Herein, we present a simple, binder-free method to fabricate hierarchical, pineapple-like CuCo 2 O 4 nanostructures on carbon fibers via low-temperature wet-chemical method. By utilizing a combination of hexamine and urea, we tailored the morphology and crystallinity of CuCo 2 O 4 , improving ion accessibility and interconnectivity, which led to superior electrochemical performance compared to individual components. Particularly, the pineapple-like CuCo 2 O 4 demonstrated diffusion-dominated behavior, achieving a higher specific capacitance of 745 F g −1 at 1 A g −1 and excellent cycling stability. Moreover, a hybrid supercapacitor was fabricated using diffusion-type CuCo 2 O 4 electrode and activated carbon as the capacitive electrode, which exhibited good synergy in delivering excellent energy storage performance. The device achieved a specific capacity of 140.5 C g −1 at 0.5 A g −1 and an energy density of 45.5 Wh kg −1 with a high-power density of 5950 W kg −1 . Even at a high current density of 10 A g −1 , the hybrid supercapacitor maintained excellent rate capability and remarkable cycling stability (89.6% retention after 10,000 cycles), demonstrating efficient charge storage and transfer. Benefiting from high voltage and energy density, the fabricated hybrid supercapacitors successfully powered various LEDs, illustrating their potential for real-world applications. Our work demonstrates the importance of spinal-type nanostructure engineering to achieve enhanced electrochemical performance, providing a straightforward pathway for developing next-generation supercapacitors and battery materials. Graphical abstract

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