Mixed solvent-assisted synthesis of high mass loading amorphous NiCo-MOF as a promising electrode material for supercapacitors.

The pursuit of high mass loading metal-organic framework (MOF) materials via a simple method is crucial to achieve high-performance supercapacitors. Herein, an amorphous NiCo-MOF material with a high mass loading of up to 10.3 mg cm-2 was successfully prepared using a mixed solvent system of ethanol and water. In addition, by adjusting the volume ratio of ethanol to water, amorphous NiCo-MOFs with three different morphologies including nanospheres, nanopores, and ultra-thick plates were obtained. It was found that the different solvent systems not only affected the growth rate of MOFs, but also controlled their nucleation rate by changing the coordination environment of the metal ions, and thus achieved morphology and mass loading regulation, thereby influencing their energy storage behavior. Notably, the optimum NiCo-MOF exhibited the superior specific capacitance of up to 9.7 F cm-2 (941.8 F g-1) at a current density of 5 mA cm-2 and high-rate capability of 71.1% even at 20 mA cm-2. Moreover, the corresponding assembled solid-state supercapacitor exhibited an excellent energy density of 0.65 mW h cm-2 at a power density of 2 mW cm-2 and capacity retention of 84.7% after 8000 cycles at 30 mA cm-2. Overall, this work proposes a feasible and effective strategy to achieve high mass loading NiCo-MOFs, impacting their ultimate electrochemical performance, which can possibly be further extended to other MOFs with superior capacitance.

Electrochemically activated 3D Mn doped NiCo hydroxide electrode materials toward high-performance supercapacitors.

Metal doping and electrochemical reconstruction had been demonstrated to play a significant role in the preparation of advanced electrode materials, which is helpful to achieve high-performance supercapacitors. However, there was no report about the combination of two technologies to construct electrode materials and their applications in supercapacitors. Herein, a rational Mn doped NiCo sulfide compound with open structure composed of 2D ultra-thin nanosheets was designed via a Mn doping route. In order to further improve the energy storage performance of the resulted product, we adopted a simple electrochemical activation strategy to reconstruct it. It was found that the reconstructed sample not only exhibited an irreversible evolution of structure (from 2D sheet to 3D channel), but also the phase transformation (from metal sulfide to metal hydroxide). Benefiting from the stable 3D curved structure with numerous channels, multitudinous charge transfer provided by numerous valence states of metals and copious active sites by low crystalline state, the in-situ self-reconstructed sample exhibited superior capacitance. In details, the optimized product delivered excellent specific capacitance of 1462C g-1 (3655F/g) at 1 A g-1 and high rate capability of 66 % even at 5 A g-1. Moreover, the corresponding assembled asymmetric supercapacitor exhibited an excellent energy density of 141.8 Wh kg-1 at a power density of 850.1 W kg-1, and the capacitance retention rate was 96.6 % even after 5000 cycles, which was distinctly superior than thoseofthe previous similarmaterialsreported. In a word, this work provided a feasible and effective strategy to construct 3D Mn doped NiCo hydroxide electrode materials toward high-performance supercapacitors.

From Whole to Part: Reference-Based Representation for Clustering Categorical Data.

Dissimilarity measures play a crucial role in clustering and, are directly related to the performance of clustering algorithms. However, effectively measuring the dissimilarity is not easy, especially for categorical data. The main difficulty of the dissimilarity measurement for categorical data is that its representation lacks a clear space structure. Therefore, the space structure-based representation has been proposed to provide the categorical data with a clear linear representation space. This representation improves the clustering performance obviously but only applies to small data sets because its dimensionality increases rapidly with the size of the data set. In this paper, we investigate the possibility of reducing the dimensionality of the space structure-based representation while maintaining the same representation ability. A lightweight representation scheme is proposed by taking a set of representative objects as the reference system (called the reference set) to position other objects in the Euclidean space. Moreover, a preclustering-based strategy is designed to select an appropriate reference set quickly. Finally, the representation scheme together with the k -means algorithm provides an efficient method to cluster the categorical data. The theoretical and the experimental analysis shows that the proposed method outperforms state-of-the-art methods in terms of both accuracy and efficiency.