The effects of oxygen in spinel oxide Li1+xTi2-xO4-δ thin films

Electron energy loss spectroscopy database synthesis and automation of core-loss edge recognition by deep-learning neural networks

The ionization edges encoded in the electron energy loss spectroscopy (EELS) spectra enable advanced material analysis including composition analyses and elemental quantifications. The development of the parallel EELS instrument and fast, sensitive detectors have greatly improved the acquisition speed of EELS spectra. However, the traditional way of core-loss edge recognition is experience based and human labor dependent, which limits the processing speed. So far, the low signal-noise ratio and the low jump ratio of the core-loss edges on the raw EELS spectra have been challenging for the automation of edge recognition. In this work, a convolutional-bidirectional long short-term memory neural network (CNN-BiLSTM) is proposed to automate the detection and elemental identification of core-loss edges from raw spectra. An EELS spectral database is synthesized by using our forward model to assist in the training and validation of the neural network. To make the synthesized spectra resemble the real spectra, we collected a large library of experimentally acquired EELS core edges. In synthesize the training library, the edges are modeled by fitting the multi-Gaussian model to the real edges from experiments, and the noise and instrumental imperfectness are simulated and added. The well-trained CNN-BiLSTM network is tested against both the simulated spectra and real spectra collected from experiments. The high accuracy of the network, 94.9%, proves that, without complicated preprocessing of the raw spectra, the proposed CNN-BiLSTM network achieves the automation of core-loss edge recognition for EELS spectra with high accuracy.

Physical Vapor Deposition in Solid-State Battery Development: From Materials to Devices

This review discusses the contribution of physical vapor deposition (PVD) processes to the development of electrochemical energy storage systems with emphasis on solid-state batteries. A brief overview of different PVD technologies and details highlighting the utility of PVD for the fabrication and characterization of individual battery materials are provided. In this context, the key methods that have been developed for the fabrication of solid electrolytes and active electrode materials with well-defined properties are described, and demonstrations of how these techniques facilitate the in-depth understanding of fundamental material properties and interfacial phenomena as well as the development of new materials are provided. Beyond the discussion of single components and interfaces, the progress on the device scale is also presented. State-of-the-art solid-state batteries, both academic and commercial types, are assessed in view of energy and power density as well as long-term stability. Finally, recent efforts to improve the power and energy density through the development of 3D-structured cells and the investigation of bulk cells are discussed.