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Hao, Wei

Publications and source records attributed to Hao, Wei.

Elucidating the mechanism underlying the augmented capacity of MoO 2 as an anode material in Li-ion batteries

Transition-metal oxide anode materials have been observed to possess an intriguing surplus of capacity beyond the expected values based on conversion reaction. However, the mechanisms behind this phenomenon have remained contentious and elusive. Here, this study focuses on synthesized nanosized molybdenum dioxide and its electrochemical performance as an anode material for Li-ion batteries. Our findings reveal a substantial increase in capacity upon cycling, achieving approximately 1688 mA h g -1 , nearly double the theoretical capacity, after 700 cycles at a 1C rate. To elucidate the mechanisms underlying this augmented capacity, a comprehensive analysis employing in situ and ex situ X-ray diffraction, X-ray absorption spectroscopy, scanning electron microscopy, and transmission electron microscopy was conducted at various stages of the Li-ion cell cycling. Our results indicate that no conversion reaction occurs during the initial discharge phase, with Li 2 O and Mo remaining undetected. Instead, Li 0.98 MoO 2 is generated upon lithiation. Further materials characterization employing electron energy loss spectroscopy and energy-dispersive X-ray spectroscopy on the cycled electrode suggests the potential formation of a metallic Li-rich layer at the interface of the Li-ion intercalated phase subsequent to the formation of Li 0.98 MoO 2 , contributing to the surplus Li storage. Moreover, electrochemical impedance spectroscopy coupled with ex situ SEM and TEM analyses reveals that alterations in particle size and morphology, along with changes in the solid electrolyte interphase (SEI) resistance, are instrumental in the capacity variation observed upon cycling.

25 ENERGY STORAGE↗

Multi defect detection and analysis of electron microscopy images with deep learning

Electron microscopy is widely used to explore defects in crystal structures, but human detecting of defects is often time-consuming, error-prone, and unreliable, and is not scalable to large numbers of images or real-time analysis. In this work, we discuss the application of machine learning approaches to find the location and geometry of different defect clusters in irradiated steels. We show that a deep learning based Faster R-CNN analysis system has a performance comparable to human analysis with relatively small training data sets. Furthermore, this study proves the promising ability to apply deep learning to assist the development of automated microscopy data analysis even when multiple features are present and paves the way for fast, scalable, and reliable analysis systems for massive amounts of modern electron microscopy data.

36 MATERIALS SCIENCE↗