Conversion‐type cathodes such as metal fluorides, especially FeF2 and FeF3, are potential candidates to replace intercalation cathodes for the next generation of lithium ion batteries. However, the application of iron fluorides is impeded by their poor electronic conductivity, iron/fluorine dissolution, and unstable cathode electrolyte interfaces (CEIs). A facile route to fabricate a mechanical strong electrode with hierarchical electron pathways for FeF2 nanoparticles is reported here. The FeF2/Li cell demonstrates remarkable cycle performances with a capacity of 300 mAh g−1 after a record long 4500 cycles at 1C. Meanwhile, a record stable high area capacity of over 6 mAh cm−2 is achieved. Furthermore, ultra‐high rate capabilities at 20C and 6C for electrodes with low and high mass loading, respectively, are attained. Advanced electron microscopy reveals the formation of stable CEIs. The results demonstrate that the construction of viable electronic connections and favorable CEIs are the key to boost the electrochemical performances of FeF2 cathode.

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Emotions are usually evoked in humans by images. Recently, extensive research efforts have been dedicated to understanding the emotions of images. In this chapter, we aim to introduce image emotion analysis (IEA) from a computational perspective with the focus on summarizing recent advances and suggesting future directions. We begin with commonly used emotion representation models from psychology. We then define the key computational problems that the researchers have been trying to solve and provide supervised frameworks that are generally used for different IEA tasks. After the introduction of major challenges in IEA, we present some representative methods on emotion feature extraction, supervised classifier learning, and domain adaptation. Furthermore, we introduce available datasets for evaluation and summarize some main results. Finally, we discuss some open questions and future directions that researchers can pursue.

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