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http://hdl.handle.net/1942/48619| Title: | Folded nanocrystalline-stacked WO3 for efficient ammonium-ion storage | Authors: | DONG, Ximan Li, Pei Chen, Xinyue Chu, Yue Zubtsovskii, Aleksandr Zhang , Chuyan Lu, Ziyang LI, Changli LIU, Shuang Hoffmann, Renee S. Huang, Nan Schonherr, Holger Yang, Quan-Hong Jiang , Xin YANG, Nianjun |
Issue Date: | 2026 | Publisher: | ELSEVIER | Source: | Energy storage materials, 86 (Art N° 104932) | Abstract: | Ammonium-ion hybrid supercapacitors (AIHSs) hold great promises for high-rate energy storage, yet their performance is often restricted by sluggish ion transport and the structural instability of electrode materials. Here, we present a rapid and scalable electrodeposition strategy operated at an unusually high overpotential (-2.5 V) to fabricate amorphous tungsten oxide (a-WO3) with nanocrystallites irregularly stacked. The asdeposited a-WO3 also features abundant oxygen vacancies and surface oxygen-containing functional groups, which serve as additional NH4+ adsorption sites and enhance redox activity and ion diffusion kinetics. Benefiting from these synergistic effects, the a-WO3 electrode delivers a high areal capacitance of 2783 mF cm-2, excellent rate capability, and superior cycling stability. When coupled with a polyaniline (PANI) cathode, the resulting AIHS achieves an impressive energy density of 620 mu Wh cm-2. This work demonstrates a powerful strategy for engineering defect-rich amorphous nanomaterials toward next-generation ammonium-ion storage technologies. | Notes: | Yang, NJ (corresponding author), Hasselt Univ, Dept Chem, Agoralaan 1, B-3590 Diepenbeek, Belgium.; Jiang, X (corresponding author), Univ Siegen, Inst Mat Engn, Paul Bonatz Str 9-11, D-57076 Siegen, Germany. nianjun.yang@uhasselt.be; jiang@lot.mb.uni-siegen.de |
Keywords: | Ammonium-ion storage;Tungsten oxide;Non-metal charge carriers;Folded nanocrystalline-stacked structure | Document URI: | http://hdl.handle.net/1942/48619 | ISSN: | 2405-8297 | e-ISSN: | 2405-8289 | DOI: | 10.1016/j.ensm.2026.104932 | ISI #: | 001683614000001 | Rights: | 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies | Category: | A1 | Type: | Journal Contribution |
| Appears in Collections: | Research publications |
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| main.pdf Restricted Access | Published version | 7 MB | Adobe PDF | View/Open Request a copy |
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