Optimization of sodium-ion battery cathode performance by nickel-based Prussian blue epitaxial growth with iron-based Prussian blue core-shell structure

IF 1.3 4区 化学 Q4 ELECTROCHEMISTRY International Journal of Electrochemical Science Pub Date : 2024-10-09 DOI:10.1016/j.ijoes.2024.100828
Dasen Shen , Junli Pan , Na Wang , Shouqiang Huang , Weiqiao Liu , Binglong Zhu , Hongying Lv
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Abstract

To address the structural defects and poor electrical conductivity of conventional Prussian blue analogues (PBAs) samples, in this study, nickel hexacyanoferrate (NiHCF) and nickel hexacyanoferrate epitaxially grown with iron hexacyanoferrate (Ni@FeHCF-X) series of samples were successfully synthesized by co-precipitation combined with epitaxial growth technique, and their structural and electrochemical properties were systematically characterized. X-ray powder diffractometer (XRD) results show that all the samples maintain a cubic crystal system structure, and the diffraction peaks of the (220) and (400) crystal planes are shifted to a lower angle with the epitaxial growth of ferric hexacyanoferrate (FeHCF). The successful embedding of Fe was confirmed by inductively coupled plasma (ICP) measurement data. Scanning electron microscope (SEM) images show that the samples are in a cubic morphology, and the surfaces gradually become rounded from sharp to rounded with the increase of the Fe content. High-resolution transmission electron microscope (HRTEM) and energy-dispersive spectrometer (EDS) analyses confirm the successful construction of the core-shell structure and the homogeneous distribution of the elements. Electrochemical testing showed that the Ni@FeHCF-X samples exhibited higher specific capacities and superior rate performance compared to NiHCF, particularly the Ni@FeHCF-4 sample, which delivered a high specific capacity of 73.4 mAh·g⁻¹ at a current density of 100 mA·g⁻¹, significantly outperforming the 38.2 mAh·g⁻¹ achieved by NiHCF. Additionally, the capacity retention rate of Ni@FeHCF-4 reached 42.16 %. Electrochemical impedance spectroscopy (EIS) further confirmed the reduced charge transfer resistance and enhanced reversibility of the electrochemical reactions in the Ni@FeHCF-X samples. These results demonstrate that Ni@FeHCF-4 significantly enhances the electrochemical performance of the samples in aqueous sodium-ion battery, providing new insights into the design of cathode materials for sodium-ion batteries.
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通过镍基普鲁士蓝外延生长与铁基普鲁士蓝核壳结构优化钠离子电池阴极性能
针对传统普鲁士蓝类似物(PBAs)样品结构缺陷和导电性差的问题,本研究采用共沉淀结合外延生长技术成功合成了六氰基铁酸镍(NiHCF)和六氰基铁酸镍外延生长(Ni@FeHCF-X)系列样品,并对其结构和电化学性能进行了系统表征。X 射线粉末衍射仪(XRD)结果表明,所有样品均保持立方晶系结构,随着六氰铁(FeHCF)的外延生长,(220)和(400)晶面的衍射峰向低角度移动。电感耦合等离子体 (ICP) 测量数据证实了铁的成功嵌入。扫描电子显微镜(SEM)图像显示,样品呈立方体形态,随着铁含量的增加,表面由尖锐逐渐变圆。高分辨率透射电子显微镜(HRTEM)和能量色散光谱仪(EDS)分析证实了核壳结构的成功构建和元素的均匀分布。电化学测试表明,与 NiHCF 相比,Ni@FeHCF-X 样品具有更高的比容量和更优越的速率性能,尤其是 Ni@FeHCF-4 样品,在 100 mA-g-¹ 的电流密度下,比容量高达 73.4 mAh-g-¹,明显优于 NiHCF 的 38.2 mAh-g-¹。此外,Ni@FeHCF-4 的容量保持率达到了 42.16%。电化学阻抗谱(EIS)进一步证实,Ni@FeHCF-X 样品的电荷转移电阻降低,电化学反应的可逆性增强。这些结果表明,Ni@FeHCF-4 能显著提高样品在水性钠离子电池中的电化学性能,为钠离子电池阴极材料的设计提供了新的思路。
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来源期刊
CiteScore
3.00
自引率
20.00%
发文量
714
审稿时长
2.6 months
期刊介绍: International Journal of Electrochemical Science is a peer-reviewed, open access journal that publishes original research articles, short communications as well as review articles in all areas of electrochemistry: Scope - Theoretical and Computational Electrochemistry - Processes on Electrodes - Electroanalytical Chemistry and Sensor Science - Corrosion - Electrochemical Energy Conversion and Storage - Electrochemical Engineering - Coatings - Electrochemical Synthesis - Bioelectrochemistry - Molecular Electrochemistry
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Editorial Board Front Matter1:Full Title Page Retraction notice to ‘Electrochemical behavior of salbutamol, clenbuterol, ractopamine and albuterol at CNTs/GCE’ [Int. J. Electrochem. Sci. 17/5 (2022) 220567] Retraction notice to “Fe–Co co-doped 1D@2D carbon-based composite as an efficient catalyst for Zn-air batteries” [Int. J. Electrochem. Sci., 19 (2024) 100766] Robust lithium-ion battery state of health estimation based on recursive feature elimination-deep Bidirectional long short-term memory model using partial charging data
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