Northeastern University (China)

About: Northeastern University (China) is a education organization based out in Shenyang, China. It is known for research contribution in the topics: Control theory & Microstructure. The organization has 36087 authors who have published 36125 publications receiving 426807 citations. The organization is also known as: Dōngběi Dàxué & Northeastern University (东北大学).

Flexible Pillared Graphene-Paper Electrodes for High-Performance Electrochemical Supercapacitors

Abstract: Flexible graphene paper (GP) pillared by carbon black (CB) nanoparticles using a simple vacuum filtration method is developed as a high-performance electrode material for supercapacitors. Through the introduction of CB nanoparticles as spacers, the self-restacking of graphene sheets during the filtration process is mitigated to a great extent. The pillared GP-based supercapacitors exhibit excellent electrochemical performances and cyclic stabilities compared with GP without the addition of CB nanoparticles. At a scan rate of 10 mV s −1 , the specific capacitance of the pillared GP is 138 F g −1 and 83.2 F g −1 with negligible 3.85% and 4.35% capacitance degradation after 2000 cycles in aqueous and organic electrolytes, respectively. At an extremely fast scan rate of 500 mV s −1 , the specific capacitance can reach 80 F g −1 in aqueous electrolyte. No binder is needed for assembling the supercapacitor cells and the pillared GP itself may serve as a current collector due to its intrinsic high electrical conductivity. The pillared GP has great potential in the development of promising flexible and ultralight-weight supercapacitors for electrochemical energy storage.

Deep Learning Based Approach for Bearing Fault Diagnosis

Abstract: Bearing is one of the most critical components in most electrical and power drives. Effective bearing fault diagnosis is important for keeping the electrical and power drives safe and operating normally. In the age of Internet of Things and Industrial 4.0, massive real-time data are collected from bearing health monitoring systems. Mechanical big data have the characteristics of large volume, diversity, and high velocity. There are two major problems in using the existing methods for bearing fault diagnosis with big data. The features are manually extracted relying on much prior knowledge about signal processing techniques and diagnostic expertise, and the used models have shallow architectures, limiting their capability in fault diagnosis. Effectively mining features from big data and accurately identifying the bearing health conditions with new advanced methods have become new issues. This paper presents a deep learning-based approach for bearing fault diagnosis. The presented approach preprocesses sensor signals using short-time Fourier transform (STFT). Based on a simple spectrum matrix obtained by STFT, an optimized deep learning structure, large memory storage retrieval (LAMSTAR) neural network, is built to diagnose the bearing faults. Acoustic emission signals acquired from a bearing test rig are used to validate the presented method. The validation results show the accurate classification performance on various bearing faults under different working conditions. The performance of the presented method is also compared with other effective bearing fault diagnosis methods reported in the literature. The comparison results have shown that the presented method gives much better diagnostic performance, even at relatively low rotating speeds.

Neural-Network-Based Event-Triggered Adaptive Control of Nonaffine Nonlinear Multiagent Systems With Dynamic Uncertainties

Abstract: This article addresses the adaptive event-triggered neural control problem for nonaffine pure-feedback nonlinear multiagent systems with dynamic disturbance, unmodeled dynamics, and dead-zone input. Radial basis function neural networks are applied to approximate the unknown nonlinear function. A dynamic signal is constructed to deal with the design difficulties in the unmodeled dynamics. Moreover, to reduce the communication burden, we propose an event-triggered strategy with a varying threshold. Based on the Lyapunov function method and adaptive neural control approach, a novel event-triggered control protocol is constructed, which realizes that the outputs of all followers converge to a neighborhood of the leader’s output and ensures that all signals are bounded in the closed-loop system. An illustrative simulation example is applied to verify the usefulness of the proposed algorithms.

A Long-Cycle-Life Self-Doped Polyaniline Cathode for Rechargeable Aqueous Zinc Batteries

Abstract: Rechargeable aqueous zinc batteries are promising energy-storage systems for grid applications. Highly conductive polyaniline (PANI) is a potential cathode, but it tends to deactivate in electrolytes with low acidity (i.e. pH >1) owing to deprotonation of the polymer. In this study, we synthesized a sulfo-self-doped PANI electrode by a facile electrochemical copolymerization process. The -SO3 - self-dopant functions as an internal proton reservoir to ensure a highly acidic local environment and facilitate the redox process in the weakly acidic ZnSO4 electrolyte. In a full zinc cell, the self-doped PANI cathode provided a high capacity of 180 mAh g-1 , excellent rate performance of 70 % capacity retention with a 50-fold current-density increase, and a long cycle life of over 2000 cycles with coulombic efficiency close to 100 %. Our study opens a door for the use of conducting polymers as cathode materials for high-performance rechargeable zinc batteries.

An Integrated Power Pack of Dye-Sensitized Solar Cell and Li Battery Based on Double-Sided TiO2 Nanotube Arrays

Abstract: We present a new approach to fabricate an integrated power pack by hybridizing energy harvest and storage processes. This power pack incorporates a series-wound dye-sensitized solar cell (DSSC) and a lithium ion battery (LIB) on the same Ti foil that has double-sided TiO(2) nanotube (NTs) arrays. The solar cell part is made of two different cosensitized tandem solar cells based on TiO(2) nanorod arrays (NRs) and NTs, respectively, which provide an open-circuit voltage of 3.39 V and a short-circuit current density of 1.01 mA/cm(2). The power pack can be charged to about 3 V in about 8 min, and the discharge capacity is about 38.89 μAh under the discharge density of 100 μA. The total energy conversion and storage efficiency for this system is 0.82%. Such an integrated power pack could serve as a power source for mobile electronics.