Nanjing University of Science and Technology

About: Nanjing University of Science and Technology is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Control theory & Catalysis. The organization has 31581 authors who have published 36390 publications receiving 525474 citations. The organization is also known as: Nánjīng Lǐgōng Dàxué & Nánlǐgōng.

From graphene to metal oxide nanolamellas: a phenomenon of morphology transmission.

Abstract: Single-layer-graphene and few-layer-graphene structures have been predicted to have high specific surface area. Recent research has focused largely on utilizing the intriguing morphology of graphene as building blocks or substrates, keeping the structure undisturbed. Relatively little attention has been paid to explore the framework substitution of graphene. Here, we report a procedure for morphology transmission from graphene to metal oxide nanolamellas by in situ replacement with the framework of graphene. Our approach involves using graphene sheets as the starting reagent, thereby transmitting the morphology of layered structure from graphene to as-prepared metal oxides. The heteroconfiguration of as-prepared MnO2 could play a role in preventing microstructure degradation in the electrochemical cycling process, bestowing MnO2 nanolamellas an excellent electrochemical stability as a supercapacitor electrode. It is worth mentioning that this methodology is readily adaptable to fabricating MnO2, Co3O4, an...

Influence of gradient structure volume fraction on the mechanical properties of pure copper

Abstract: This paper reports the influence of gradient structure volume fraction on the tensile mechanical behaviors of pure copper processed by surface mechanical attrition treatment at cryogenic temperature. Superior combinations of tensile strength and ductility are observed in a certain volume fraction, in which strain hardening uprising after yielding is also observed. The gradient structure produces a synergetic strengthening and extra work hardening. These findings suggest the existence of an optimum volume fraction of gradient structure for the best mechanical properties.

Ultrathin Bismuth Nanosheets for Stable Na-Ion Batteries: Clarification of Structure and Phase Transition by in Situ Observation

Abstract: Bismuth has garnered tremendous interest for Na-ion batteries (NIBs) due to potentially high volumetric capacity. Yet, the bismuth upon sodiation/desodiation experiencing structure and phase transitions remains unclear, which sets a challenge for accessing nanotechnology and nanofabrication to achieve its applicability. Here, we use in situ transmission electron microscopy to disclose the structure and phase transitions of layered bismuth (few-layer bismuth nanosheets) during Na+ intercalation and alloying processes. Multistep phase transitions from Bi → NaBi → c-Na3Bi (cubic) → h-Na3Bi (hexagonal) are clearly identified, during which the Na+ migration from interlayer to in-plane evokes the structure transition from ABCABC stacking type of c-Na3Bi to ABABAB stacking type of h-Na3Bi. It is found that the metastable c-Na3Bi devotes to buffer the dramatic structure changes from thermodynamic stable h-Na3Bi, which unveils the origin of volume expansion for bismuth and has important consequences for 2D in-plan...

Cost-Driven Off-Loading for DNN-Based Applications Over Cloud, Edge, and End Devices

Abstract: Currently, deep neural networks (DNNs) have achieved a great success in various applications. Traditional deployment for DNNs in the cloud may incur a prohibitively serious delay in transferring input data from the end devices to the cloud. To address this problem, the hybrid computing environments, consisting of the cloud, edge, and end devices, are adopted to offload DNN layers by combining the larger layers (more amount of data) in the cloud and the smaller layers (less amount of data) at the edge and end devices. A key issue in hybrid computing environments is how to minimize the system cost while accomplishing the offloaded layers with their deadline constraints. In this article, a self-adaptive discrete particle swarm optimization (PSO) algorithm using the genetic algorithm (GA) operators is proposed to reduce the system cost caused by data transmission and layer execution. This approach considers the characteristics of DNNs partitioning and layers off-loading over the cloud, edge, and end devices. The mutation operator and crossover operator of GA are adopted to avert the premature convergence of PSO, which distinctly reduces the system cost through enhanced population diversity of PSO. The proposed off-loading strategy is compared with benchmark solutions, and the results show that our strategy can effectively reduce the system cost of off-loading for DNN-based applications over the cloud, edge and end devices relative to the benchmarks.