Advances in Mn-Based MOFs and Their Derivatives for High-Performance Supercapacitor.

As the most widely used metal material in supercapacitors, manganese (Mn)-based materials possess the merits of high theoretical capacitance, stable structure as well as environmental friendliness. However, due to poor conductivity and easy accumulation, the practical capacitance of Mn-based materials is far lower than that of theoretical value. Therefore, accurate structural adjustment and controllable strategies are urgently needed to optimize the electrochemical properties of Mn-based materials. Metal-organic frameworks (MOFs) are porous materials with high specific surface area (SSA), tunable pore size, and controllable structure. These features make them attractive as precursors or scaffold for the synthesis of metal-based materials and composites, which are important for electrochemical energy storage applications. Therefore, a timely and comprehensive review on the classification, design, preparation and application of Mn-based MOFs and their derivatives for supercapacitors has been given in this paper. The recent advancement of Mn-based MOFs and their derivatives applied in supercapacitor electrodes are particularly highlighted. Finally, the challenges faced by Mn-MOFs and their derivatives for supercapacitors are summarized, and strategies to further improve their performance are proposed. The aspiration is that this review will serve as a beneficial compass, guiding the logical creation of Mn-based MOFs and their derivatives in the future.

Recent advances in supramolecular self-assembly derived materials for high-performance supercapacitors.

The key preponderance of supramolecular self-assembly strategy is its ability to precisely assemble various functional units at the molecular level through non-covalent bonds to form multifunctional materials. Supramolecular materials have the merits of diverse functional groups, flexible structure, and unique self-healing properties, which make them of great value in the field of energy storage. This paper reviews the latest research progress of the supramolecular self-assembly strategy for the advanced electrode materials and electrolytes for supercapacitors, including supramolecular self-assembly for the preparation of high-performance carbon materials, metal-based materials and conductive polymer materials, and its beneficial effects on the performance of supercapacitors. The preparation of high performance supramolecular polymer electrolytes and their application in flexible wearable devices and high energy density supercapacitors are also discussed in detail. In addition, at the end of this paper, the challenges of the supramolecular self-assembly strategy are summarized and the development of supramolecular-derived materials for supercapacitors is prospected.

AF: An Association-Based Fusion Method for Multi-Modal Classification.

Multi-modal classification (MMC) aims to integrate the complementary information from different modalities to improve classification performance. Existing MMC methods can be grouped into two categories: traditional methods and deep learning-based methods. The traditional methods often implement fusion in a low-level original space. Besides, they mostly focus on the inter-modal fusion and neglect the intra-modal fusion. Thus, the representation capacity of fused features induced by them is insufficient. The deep learning-based methods implement the fusion in a high-level feature space where the associations among features are considered, while the whole process is implicit and the fused space lacks interpretability. Based on these observations, we propose a novel interpretative association-based fusion method for MMC, named AF. In AF, both the association information and the high-order information extracted from feature space are simultaneously encoded into a new feature space to help to train an MMC model in an explicit manner. Moreover, AF is a general fusion framework, and most existing MMC methods can be embedded into it to improve their performance. Finally, the effectiveness and the generality of AF are validated on 22 datasets, four typically traditional MMC methods adopting best modality, early, late and model fusion strategies and a deep learning-based MMC method.

Testing Gene-Gene Interactions Based on a Neighborhood Perspective in Genome-wide Association Studies.

Unexplained genetic variation that causes complex diseases is often induced by gene-gene interactions (GGIs). Gene-based methods are one of the current statistical methodologies for discovering GGIs in case-control genome-wide association studies that are not only powerful statistically, but also interpretable biologically. However, most approaches include assumptions about the form of GGIs, which results in poor statistical performance. As a result, we propose gene-based testing based on the maximal neighborhood coefficient (MNC) called gene-based gene-gene interaction through a maximal neighborhood coefficient (GBMNC). MNC is a metric for capturing a wide range of relationships between two random vectors with arbitrary, but not necessarily equal, dimensions. We established a statistic that leverages the difference in MNC in case and in control samples as an indication of the existence of GGIs, based on the assumption that the joint distribution of two genes in cases and controls should not be substantially different if there is no interaction between them. We then used a permutation-based statistical test to evaluate this statistic and calculate a statistical p-value to represent the significance of the interaction. Experimental results using both simulation and real data showed that our approach outperformed earlier methods for detecting GGIs.

Synthesis of three dimensional N&S co-doped rGO foam with high capacity and long cycling stability for supercapacitors.

Inspired by steaming bread, a novel three dimensional N and S co-doped reduced graphene oxide (3D NS-rGO) foam is fabricated via a gas foaming method similar to steaming bread procedure, in which (NH4)2S2O3 is selected as the foaming agent as well as N and S source. Such cross-linked 3D structure not only has the high specific surface area also enable more transport channels for electrons/ions transport. Furthermore, introducing of N and S-containing functional groups creates lattice defects in graphene, which provides more active sites where the Faradaic pseudocapacitance occurs. Consequently, the electrochemical test of 3D NS-rGO sample in a three-electrode system demonstrates a high specific capacity of 306.3 F g-1 at 1 A g-1, two times higher than that of rGO prepared at the same temperature. Moreover, 3D NS-rGO sample reveals the superb cycling stability with less than 2% capacitance loss after 10,000 cycles and it exhibits potential application for high performance supercapacitors.

Preparation of Lithium Titanate/Reduced Graphene Oxide Composites with Three-Dimensional "Fishnet-Like" Conductive Structure via a Gas-Foaming Method for High-Rate Lithium-Ion Batteries.

With use of ammonium chloride (NH4Cl) as the pore-forming agent, three-dimensional (3D) "fishnet-like" lithium titanate/reduced graphene oxide (LTO/G) composites with hierarchical porous structure are prepared via a gas-foaming method. Scanning electron microscopy and transmission electron microscopy images show that, in the composite prepared with the NH4Cl concentration of 1 mg mL-1 (1-LTO/G), LTO particles with sizes of 50-100 nm disperse homogeneously on the 3D "fishnet-like" graphene. The nitrogen-sorption analyses reveal the existence of micro-/mesopores, which is attributed to the introduction of NH4Cl into the gap between the graphene sheets that further decomposes into gases and produces hierarchical pores during the thermal treatment process. The loose and porous structure of 1-LTO/G composites enables the better penetration of electrolytes, providing more rapid diffusion channels for lithium ion. As a result, the 1-LTO/G electrode delivers an ultrahigh specific capacity of 176.6 mA h g-1 at a rate of 1 C. Even at 3 and 10 C, the specific capacity can reach 167.5 and 142.9 mA h g-1, respectively. Moreover, the 1-LTO/G electrode shows excellent cycle performance with 95.4% capacity retention at 10 C after 100 cycles. The results demonstrate that the LTO/G composite with these properties is one of the most promising anode materials for lithium-ion batteries.