Wuhan University of Technology

About: Wuhan University of Technology is a education organization based out in Wuhan, China. It is known for research contribution in the topics: Microstructure & Photocatalysis. The organization has 40384 authors who have published 36724 publications receiving 575695 citations. The organization is also known as: WUT.

MOF-derived 3D Fe-N-S co-doped carbon matrix/nanotube nanocomposites with advanced oxygen reduction activity and stability in both acidic and alkaline media

Abstract: MOF-derived carbon-based nanomaterials have attracted great attention due to the outstanding electrocatalytic performance, low-cost and super stability. To design an excellent catalyst, Fe, N and S codoped carbon matrix/carbon nanotube nanocomposites (Fe-N-S CNN) are prepared by pyrolysis of ZIF-8 impregnated with iron salt in this work. Benefiting from the synergistic effect of carbon matrix and nanotubes, abundant iron nitrides and thiophene-S active sites, the Fe-N-S CNN exhibits an excellent oxygen reduction reaction (ORR) performance with a half-wave potential of 0.91 V vs. RHE in alkaline conditions and 0.78 V vs. RHE in acidic conditions, while those of commercial Pt/C catalysts are 0.85 V vs. RHE and 0.795 V vs. RHE, respectively. Furthermore, Fe-N-S CNN as the cathode catalyst in a primary zinc-air battery shows a specific capacity of 700 mA h g−1.

Deep Anomaly Detection for Time-Series Data in Industrial IoT: A Communication-Efficient On-Device Federated Learning Approach

Abstract: Since edge device failures (i.e., anomalies) seriously affect the production of industrial products in Industrial IoT (IIoT), accurately and timely detecting anomalies are becoming increasingly important. Furthermore, data collected by the edge device contain massive user’s private data, which is challenging current detection approaches as user privacy has attracted more and more public concerns. With this focus, this article proposes a new communication-efficient on-device federated learning (FL)-based deep anomaly detection framework for sensing time-series data in IIoT. Specifically, we first introduce an FL framework to enable decentralized edge devices to collaboratively train an anomaly detection model, which can improve its generalization ability. Second, we propose an attention mechanism-based convolutional neural network-long short-term memory (AMCNN-LSTM) model to accurately detect anomalies. The AMCNN-LSTM model uses attention mechanism-based convolutional neural network units to capture important fine-grained features, thereby preventing memory loss and gradient dispersion problems. Furthermore, this model retains the advantages of the long short-term memory unit in predicting time-series data. Third, to adapt the proposed framework to the timeliness of industrial anomaly detection, we propose a gradient compression mechanism based on Top- ${k}$ selection to improve communication efficiency. Extensive experimental studies on four real-world data sets demonstrate that our framework accurately and timely detects anomalies and also reduces the communication overhead by 50% compared to the FL framework that does not use the gradient compression scheme.

Light-controlled propulsion, aggregation and separation of water-fuelled TiO2/Pt Janus submicromotors and their “on-the-fly” photocatalytic activities

Abstract: In this work, water-fuelled TiO2/Pt Janus submicromotors with light-controlled motions have been developed by utilizing the asymmetrical photocatalytic water redox reaction over TiO2/Pt Janus submicrospheres under UV irradiation. The motion state, speed, aggregation and separation behaviors of the TiO2/Pt Janus submicromotor can be reversibly, wirelessly and remotely controlled at will by regulating the “on/off” switch, intensity and pulsed/continuous irradiation mode of UV light. The motion of the water-fuelled TiO2/Pt Janus submicromotor is governed by light-induced self-electrophoresis under the local electrical field generated by the asymmetrical water oxidation and reduction reactions on its surface. The TiO2/Pt Janus submicromotors can interact with each other through the light-switchable electrostatic forces, and hence continuous and pulsed UV irradiation can make the TiO2/Pt Janus submicromotors aggregate and separate at will, respectively. Because of the enhanced mass exchange between the environment and active submicromotors, the separated TiO2/Pt Janus submicromotors powered by the pulsed UV irradiation show a much higher activity for the photocatalytic degradation of the organic dye than the aggregated TiO2/Pt submicromotors. The water-fuelled TiO2/Pt Janus submicromotors developed here have some outstanding advantages as “swimming” photocatalysts for organic pollutant remediation in the macro or microenvironment (microchannels and microwells in microchips) because of their small size, long-term stability, wirelessly controllable motion behaviors and long life span.