2026-10-13T20:01:04Z https://www.scidb.cn/oai

10.57760/sciencedb.Partic.00034 2024-10-11T18:16:57Z

2024-10-11 Novel self-assembled porous yolk-shell NiO nanospheres with excellent electrochromic performance for smart windows doi:10.57760/sciencedb.partic.00034 en With the rapid development of optoelectronics, electrochromic (EC) materials (ECMs) with the advantages of low power consumption, easy viewing, high contrast ratio, etc. have attached more and more attention from the fields of smart windows, electronic billboards, emerging wearable and portable electronics, and other next-generation displays. Nickel oxide (NiO) is a promising candidate for high-performance ECMs because of its neutral-colored states and low cost. However, NiO-based ECMs still face the problem of slow switching speed due to their low electrical conductivity and small lattice spacing. Metal-organic frameworks (MOFs) are promising candidates to fabricate hollow and porous transition metal oxides (TMOs) with high ion transport efficiency, excellent specific capacitance, and electrochemical activities. In this work, porous yolk-shell NiO nanospheres (PYS–NiO NSs) were synthesized via a solvothermal and subsequent calcination process of Ni-MOF, which exhibited outstanding EC performance. Because the large specific surface areas and hollow porous nanostructures were conducive to ionic transport, PYS-NiO NSs exhibited a fast coloring/bleaching speed (3.6/3.9 s per one coloring/bleaching cycle) and excellent cycling stability (82% of capacity retention after 3000 cycles). These superior EC properties indicated that the PYS-NiO NSs was a promising candidate for high-performance EC devices. This work provides a new and feasible strategy for the efficient preparation of TMOs ECMs with good EC performance, especially fast switching speed. Electrochromic; Nickel oxide; Metal-organic frameworks; Hollow structure; Smart windows Baoshun Wang Ya Huang Siming Zhao Run Li Di Gao Hairong Jiang Rufan Zhang PUBLIC https://creativecommons.org/licenses/by/4.0/ dataset http://www.doi.org/https://doi.org/10.1016/j.partic.2023.03.007 Science Data Bank