Redox cascade engineering in urea-crystallized manganese-integrated nickel-iron metal-organic frameworks for high-performance hybrid supercapacitors
บทความในวารสาร
ผู้เขียน/บรรณาธิการ
กลุ่มสาขาการวิจัยเชิงกลยุทธ์
รายละเอียดสำหรับงานพิมพ์
รายชื่อผู้แต่ง: Dong, S.-F.; Cheshideh, H.; Kubendhiran, S.; Kongvarhodom, C.; Saukani, M.; Yougbaré, S.; Chen, H.-M.; Wu, Y.-F.; Lin, L.-Y.
ผู้เผยแพร่: Elsevier
ปีที่เผยแพร่ (ค.ศ.): 2025
วารสาร: Journal of Power Sources (0378-7753)
Volume number: 660
หน้าแรก: 238566
ISBN: 444894810
นอก: 0378-7753
eISSN: 1873-2755
ภาษา: English-Great Britain (EN-GB)
บทคัดย่อ
The growing need for hybrid energy storage systems that merge the advantages of batteries and supercapacitors has sparked considerable interest. As a result, researchers have begun to explore advanced electrode materials that offer both strong redox activity and long-term structural stability. Our study proposes a dual-modification approach to engineer a highly porous and redox-enriched NiFe-based metal organic framework (NiFe-MOF) through urea-assisted synthesis and in situ integration of MnO2. The final Mn/NiFeMOF-U hybrid shows a hierarchically porous structure with abundant faradaic sites that facilitate fast ion transport and enhanced multivalent redox coupling across Ni, Fe, and Mn centers. The results indicate surface-controlled charge storage dominated by reversible Ni2+/Ni3+, Fe2+/Fe3+, and Mn2+/Mn4+transitions. Furthermore, electron hopping and a robust electron cascade are identified as the main electron transfer pathways in this system. By pairing Mn/NiFeMOF-U with reduced graphene oxide (rGO), the assembled battery-supercapacitor hybrid (BSH) demonstrates an outstanding energy density of 1.7 mWh/cm2at 6.4 mW/cm2, while maintaining 91% capacitance retention and 99% Coulombic efficiency after 10,000 cycles. Overall, we believe this study not only presents a new strategy for designing redox-modulated MOF hybrids but also confirms their practical potential for next-generation electrochemical energy storage. © 2025 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
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