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• Defect engineering is employed to exploit sulfur vacancies on surface of MoS 2 nanoflakes. • Random generated sulfur vacancies improve the composite uniformity and binding of MoS 2 /SnO 2. • As anode materials in LIB, MoS 2 /SnO 2 composite can deliver a capacity up to 800 mAh g−1 at 1 A g−1. • A high cycling stability of 580 mAh g−1 after 1000 cycles at 1 A g−1 is retained. Defect engineering, which is a concept applied to exploit the potential of a given material fully, is also considered when designing materials for energy-related applications. In general, defect engineering focuses on the direct chemical or physical changes caused by defects through varied mechanisms. In this study, sulfur vacancies on the surface of molybdenum disulfide (MoS 2) nanoflakes are created easily via hydrogen-etching and are employed to improve the composite uniformity and binding condition with tin dioxide (SnO 2), which is strongly related to electrochemical performance. When the as-prepared MoS 2 -SnO 2 is used as the anode material in a lithium-ion battery (LIB), it delivers a peak capacity of 800 mAh g−1, while retaining a capacity of 580 mAh g−1 in the 1000th cycle at a current density of 1 A g−1. The vacancy-assisted compositing method we propose is demonstrated to be an effective and controllable synthetic technology and is expected to work in an analogous way for preparing MoS 2 composites with other promising oxide anode candidates (e.g., Fe 3 O 4 , CoO, or MnO) in LIB applications. [ABSTRACT FROM AUTHOR]

During air bending of sheet metals, the correction of punch stroke for springback control is always implemented through repeated trial bending until achieving the forming accuracy of bending parts. In this study, a modelling method for correction of punch stroke is presented for guiding trial bending based on a data-driven technique. Firstly, the big data for the model are mainly generated from a large number of finite element simulations, considering many variables, e.g., material parameters, dimensions of V-dies and blanks, and processing parameters. Based on the big data, two punch stroke correction models are developed via neural network and dimensional analysis, respectively. The analytic comparison shows that the neural network model is more suitable for guiding trial bending of sheet metals than the dimensional analysis model, which has mechanical significance. The actual trial bending tests prove that the neural-network-based punch stroke correction model presents great versatility and accuracy in the guidance of trial bending, leading to a reduction in the number of trial bends and an improvement in the production efficiency of air bending. [ABSTRACT FROM AUTHOR]