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Porous carbon nanosheets with high specific surface area have become the most promising electrode materials for supercapacitor, but their high pore volume leads to relatively low density and poor volumetric capacitance. In this work, strutted nitrogen doped hierarchical porous carbon nanosheets (SNPCNS), featuring three-dimensional nonaggregated architecture braced by struts have successfully been scalably synthesized via a novel calcium d -gluconate-exploding technique. The pyrolysis temperature and duration, and the mass ratio of calcium d -gluconate and urea formaldehyde resin are regulated for optimizing the specific surface area, pore volume and capacitive performance of SNPCNS. The optimized SNPCNS displays high specific surface area (539 m2 g−1), rich surface heteroatoms (8.1 at.% for N) and high density (1.11 g cm−3). Therefore, the supercapacitor assembled by SNPCNS electrodes presents very high gravimetric/volumetric capacitances of 286 F g−1/317 F cm−3 (in 6 M KOH) and 355 F g−1/394 F cm−3 (in 1 M H2SO4). Importantly, high gravimetric/volumetric energy densities of 40.5 W h kg−1/44.9 W h L−1 (in ionic liquid) are achieved, which are superior to those of previously reported carbon nanosheets based symmetric supercapacitors. This work provides a new strategy for the mass and low-cost production of high-performance porous carbon nanosheets for energy storage.

The exploration of biomass-derived hierarchically porous carbon is of great significance to supercapacitors (SCs). Herein, the N and O co-doped hierarchically porous carbon (N-O-HPC) derived from waste tea leaves was prepared by a facile dual-templating (ZnCl2 and Mg5(OH)2(CO3)4) approach. When utilized as an electrode for SCs, the as-prepared N-O-HPC exhibits a large specific capacitance of 321 F g−1 at 1 A g−1. Moreover, the assembled N-O-HPC//N-O-HPC symmetric SCs exhibit a wide voltage window (1.8 V), a high energy density (28.01 Wh kg-1 at 450 W kg-1) and a long cycle life after 10000 cycles (90.7%). These results demonstrate that as-prepared N-O-HPC exhibits a great potential for SCs, highlighting the significance of biomass derived carbon materials.

Stance detection on social media aims to identify the stance of social media users toward a topic or claim, which can provide powerful information for various downstream tasks. Many existing stance detection approaches neglect to model the deep semantic representation information in tweets and do not explore aggregating the hierarchical features among words, thus degrading performance. To address these issues, this article proposes a novel deep learning approach P retrained E mbeddings for Stance Detection with H ierarchical C apsule N etwork (PE-HCN) without complicated preprocessing. Specifically, PE-HCN first adopts a pretrained language model and then uses a related textual entailment task for fine-tuning to obtain the deep textual representations of tweets. The PE-HCN approach extends the dynamic routing scheme to cope with these deep textual representations by utilizing primary capsules for routing the information among words in each tweet and applying secondary capsules to transmit the aggregated features to each category capsule accordingly. Moreover, to improve the confidences of the category capsules, we design an adaptive feedback mechanism to dynamically strengthen the routing signals. Through experiments on three benchmark datasets, compared with the state-of-the-art baselines, the extensive results exhibit that PE-HCN achieves competitive improvements of up to 6.32%, 2.09%, and 1.8%, respectively.

Pursuing active, low-cost, and stable electrode materials with superior rate capability and long-life cycling performances for lithium-ion batteries remains a big challenge. In this study, a carbon-coated shuttle-like Fe2O3/Fe1−xS heterostructure is synthesized by simply annealing Fe-based metal–organic frameworks (MIL-88(Fe)) as precursors and sublimed sulfur. Carbon-coated Fe2O3/Fe1−xS displays a unique structure with ultrafine Fe2O3/Fe1−xS nanoparticles distributed in the hollow and porous carbon matrix, which offers a large specific surface area and fast charge transfer ability, and alleviates the volume change upon cycling. When evaluated as an anode material for lithium-ion batteries, it exhibits an ultra-high specific capacity of 1200 mA h g−1 at 0.1 A g−1, and superior high rate capability with a capacity of 345 mA h g−1 at a very high current density of 5.0 A g−1 owing to its high electrical conductivity and enhanced pseudocapacitive contribution from surface effects. The current strategy is promising to synthesize the carbon-coated porous structure from metal–organic frameworks for next-generation energy-storage applications.

In this work, we demonstrate a novel strategy for producing a porous carbon hollow rod (PCHR) via simple carbonization of magnesium gluconate. The mass ratios of PCHR/KOH are a key factor in direct...

Utilizing renewable and abundant biomass waste to prepare hierarchically porous carbons (HPC) materials with large surface area and good electrical conductivity have received considerable attention recently. In this work, a novel 3D HPC was prepared by a low-cost and facile hydrothermal process, carbonization, and chemical activation from Huiyuan® fruit grain orange (FGO) with expired deadline for the first time. The obtained HPC exhibits a high specific surface area of 2149 m2 g−1 and facilitated charge transfer ability offered by its hierarchical structure. When used as electrodes for supercapacitor, HPC displays a maximum specific capacitance of 452.7 F g−1 at 1 A g−1 in 1 M H2SO4, which is one of the highest values reported for biomass-derived carbons. Remarkably, the all-solid-state symmetric supercapacitor based on HPC-4 exhibits a high energy density of 14.1 Wh kg−1 at a power density of 140 W kg−1 and outstanding cycling stability (90% capacitance retention after 6000 charge/discharge cycles at 10 A g−1). HPC-4 should be a promising electrode material for all-solid-state supercapacitor application.

Topic models have been widely utilized in Topic Detection and Tracking tasks, which aim to detect, track, and describe topics from a stream of broadcast news reports. However, most existing topic models neglect semantic or syntactic information and lack readable topic descriptions. To exploit semantic and syntactic information, Language Models (LMs) have been applied in many supervised NLP tasks. However, there are still no extensions of LMs for unsupervised topic clustering. Moreover, it is difficult to employ general LMs (e.g., BERT) to produce readable topic summaries due to the mismatch between the pretraining method and the summarization task. In this paper, noticing the similarity between content and summary, first we propose a Language Model-based Topic Model (LMTM) for Topic Clustering by using an LM to generate a deep contextualized word representation. Then, a new method of training a Topic Summarization Model is introduced, where it is not only able to produce brief topic summaries but also used as an LM in LMTM for topic clustering. Empirical evaluations of two different datasets show that the proposed LMTM method achieves better performance over four baselines for JC, FMI, precision, recall and F1-score. Additionally, the generated readable and reasonable summaries also validate the rationality of our model components.

Novel N, O and P co-doped honeycomb-like hierarchically porous carbon (N-O-P-HHPC) materials with a large specific surface area from Sophora japonica were prepared via a one-step activation and carbonization method and used as an electrode for supercapacitors. The results indicate that as-prepared N-P-HHPC with a large specific surface area (2068.9 m2 g−1) and N (1.5 atomic%), O (8.4 atomic%) and P (0.4 atomic%) co-doping has a high specific capacitance of 386 F g−1 at 1 A g−1. Moreover, a 1.8 V symmetrical SC was assembled from the N-O-P-HHPC-3 electrode using 1 M Na2SO4 gel electrolyte, presenting a high energy density (28.4 W h kg−1 at 449.9 W kg−1) and a long life cycling stability with only 7.3% capacitance loss after 10 000 cycles. Furthermore, the coin-type symmetrical SC using EMIMBF4 as electrolyte presents an ultrahigh energy density (80.8 W h kg−1 at 1500 W kg−1). When the two coin-type symmetrical SCs are connected in series, eight red light-emitting diodes (LEDs) and a small display screen can be powered. These results demonstrate as-prepared N, O and P co-doped HHPC is a considerable candidate as a carbon electrode for energy storage devices.

Zinc bromine redox flow battery (ZBFB) has been paid attention since it has been considered as an important part of new energy storage technology. This paper introduces the working principle and main components of zinc bromine flow battery, makes analysis on their technical features and the development process of zinc bromine battery was reviewed, and emphasizes on the three main components of zinc bromine battery, and summarizes the materials and applications of electrolyte, membrane and electrode. At the same time, the solution to the technical problems of zinc bromine flow battery is also briefly analyzed. Finally, the future development of zinc bromine battery system is prospected.

The stability of hydrophobic surface has an important influence on the application of superhydrophobic function. The destruction of hydrophobic micro-nano structures on the material surface is the main factor leading to the loss of superhydrophobic property. In order to improve the corrosion resistance of superhydrophobic surface, Ni-Co-BN nanocomposite coatings with superhydrophobic property were prepared on 45 steel by two-step jet electrodeposition. The surface morphology, water contact angle, and corrosion resistance of the samples were measured and characterized by scanning electron microscope, surface contact angle measuring instrument, and electrochemical workstation. The results of electrochemical analysis show that the superhydrophobic property improved the corrosion resistance of Ni-Co-BN nanocomposite coating. The enhanced corrosion resistance is of great significance to the integrity of the microstructure and the durability of the superhydrophobic function.

Biomass-based hierarchically porous carbon is green and eco-friendly, which exhibits a wide potential for energy storage due to its large specific surface area and multiheteroatoms codoping. Herein...

Hierarchically porous carbon (HPC) material based on environmental friendliness biomass has spurred much attention, due to its high surface area and porous structure. Herein, three-dimensional (3D) N,O co-doped HPC (N–O-HPC) was prepared by using a one-step fabrication process of simultaneously carbonizing and activating soybean dregs and used as an electrode for supercapacitors (SCs). The obtained N–O-HPC with 4.8 at% N and 6.1 at% O exhibits a pretty small charge transfer resistance (0.05 Ω) and a large specific capacitance (408 F g−1 at 1 A g−1), due to its 3D hierarchically porous framework structure with extremely large specific surface area (1688 m2 g−1). Moreover, a symmetrical SC assembled with the HPC electrode exhibits an amazingly high energy density (22 W h kg−1 at 450 W kg−1) and a stable long cycling life with only 6% capacitance loss after 5000 cycles in 1 M Na2SO4 solution. This work provides a facile, green, and low-cost way to prepare electrode materials for SCs.

The exploration of new-family flexible carbon architectures is significant with regard to flexible electrochemical capacitors. Herein we show the carbonization of core-shell structured metal-organic frameworks (MOFs) (i.e., ZIF-8@ZIF-67) on a waste biomass of cosmetic cotton to prepare flexible nitrogen-doped carbon heteroarchitectures (hetero-fNCs) that possess high supercapacitive properties, including a high energy density of 30 Wh kg−1 at 1440 W kg−1 and good capacitance retention of 89.5% after 5000 cycles. Our elaborately designed hetero-fNCs demonstrate multiple advantages: (i) The outer-layer ZIF-67-derived mesoporous carbons show abundant mesopores, providing abundant ion-diffusion pathways for mass transport and rich graphite microstructures, improving electrical conductivity for electron transfer; (ii) the inter-layer ZIF-8-derived microporous carbons possess a mass of micropores for more charge storage; (iii) the inner-layer cosmetic cotton-derived flexible carbons afford good mechanical flexibility, guaranteeing performance stability during use; and (iv) the plentiful nitrogen dopants within all carbon components increase the capacitance through their pseudocapacitive contribution. As a consequence, the flexible supercapacitor based on hetero-fNCs exhibits high energy and power densities, as well as good bending stability, highlighting the significance of MOFs and flexible biomass materials.

Superhydrophobic coatings have been developed widely, because of their self-cleaning, anti-fouling, and anti-icing properties. However, there are critical challenges in terms of poor durability in harsh environments. Herein, we report an effective and simple strategy for large-scale fabrication of durable superhydrophobic composite coating, based on polydimethylsiloxane filled with fluoroalkyl silane functionalized cerium dioxide. The obtained composite coating showed satisfactory water repellency with water contact angle of 161 ± 2° and sliding angle of 4 ± 1°, mainly attributed to the construction of multilevel micro/nano scale structure. Such coatings possessed outstanding chemical stability, self-cleaning property, and adhesion strength. Notably, the coating was UV resistant and mechanically robust, which could also retain good superhydrophobicity after 240 h of UV irradiation, 30 abrasion cycles, and scratching, and could be applied to a variety of substrate surfaces. In addition, according to electrochemical test results, the superhydrophobic composite coating showed excellent corrosion barrier and active corrosion inhibition effect, due to synergy between trapped air films and cerium dioxide. These multiple key properties integrated into our superhydrophobic composite coating are expected to provide a wide range of applications in various extreme outdoor environments.

A novel frequency scanning based direction of arrival (DOA) estimation scheme is proposed. Angular superresolution algorithm based on maximum likelihood (ML) is addressed in frequency scanning reflector antenna (FSRA). the model of FSRA manifold vector is constructed. Maximum likelihood (ML) method, traditionally used in array signal processing, is extended to the novel frequency scanning system. A frequency scanning reflector antenna (FSRA) operating on 0.3THz frequency band was experimented, based on which the DOA estimation system is constructed. The resolution of the frequency scanning ML algorithm can be obtained on the level of half of the 3dB beamwidth by the proof-of-principle experiments in 0.3THz band.

This paper focuses on the problems existing in intrusion detection using neural network, including redundant information, large amount of data, long-time training, easy to fall into the local optimal. An intrusion detection method using deep belief network (DBN) and probabilistic neural network (PNN) is proposed. First, the raw data are converted to low-dimensional data while retaining the essential attributes of the raw data by using the nonlinear learning ability of DBN. Second, to obtain the best learning performance, particle swarm optimization algorithm is used to optimize the number of hidden-layer nodes per layer. Next, PNN is used to classify the low-dimensional data. Finally, the KDD CUP 1999 dataset is employed to test the performance of the method mentioned above. The experiment result shows that the method performs better than the traditional PNN, PCA-PNN and unoptimized DBN-PNN