Lithium (Li) is known for excellent theoretical specific capacity and most negative electrochemical potential, while still restricted by the irregular lithium dendrites and safety risks in practical applications of lithium metal batteries (LMBs) due to thermal runaway. Herein, a fluorinated‐polyimide (F‐PI)‐based composite nanofibrous separator containing poly(vinylidene fluoride) (PVDF) component, which is designed as PVDF/F‐PI, is developed via a facile electrospinning strategy for wide‐temperature LMBs. First, abundant polar trifluoromethyl (–CF3) groups in F‐PI create an electronegative environment to facilitate rapid Li‐ions (Li+) transport. Meanwhile, the PVDF component, acting as both the physical linker between F‐PI nanofibers and the regulator of homogenized pore size, simultaneously improves the mechanical properties and homogenizes the Li+ flux on the electrode surface. Therefore, a steady circulation of 2400 h is achieved for the symmetric cell using PVDF/F‐PI separator, which still displays a stable cycle life with a low voltage polarization of 15 mV in 1000 h even under 60 °C. Therefore, the fluorinated‐PI‐based composite nanofibrous separator with high ionic conductivity and uniform pore structure offers a practical method for design of functionalized separators in wide‐temperature LMB applications.

Empathetic dialogue is a human-like behavior that requires the perception of both affective factors (e.g., emotion status) and cognitive factors (e.g., cause of the emotion). Besides concerning emotion status in early work, the latest approaches study emotion causes in empathetic dialogue. These approaches focus on understanding and duplicating emotion causes in the context to show empathy for the speaker. However, instead of only repeating the contextual causes, the real empathic response often demonstrate a logical and emotion-centered transition from the causes in the context to those in the responses. In this work, we propose an emotion cause transition graph to explicitly model the natural transition of emotion causes between two adjacent turns in empathetic dialogue. With this graph, the concept words of the emotion causes in the next turn can be predicted and used by a specifically designed conceptaware decoder to generate the empathic response. Automatic and human experimental results on the benchmark dataset demonstrate that our method produces more empathetic, coherent, informative, and specific responses than existing models.

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Towards human-like dialogue systems, current emotional dialogue approaches jointly model emotion and semantics with a unified neural network. This strategy tends to generate safe responses due to the mutual restriction between emotion and semantics, and requires the rare large-scale emotion-annotated dialogue corpus. Inspired by the "think twice" behavior in human intelligent dialogue, we propose a two-stage conversational agent for the generation of emotional dialogue. Firstly, a dialogue model trained without the emotion-annotated dialogue corpus generates a prototype response that meets the contextual semantics. Secondly, the first-stage prototype is modified by a controllable emotion refiner with the empathy hypothesis. Experimental results on the DailyDialog and Empathet-icDialogues datasets demonstrate that the proposed conversational agent outperforms the compared models in the emotion generation and maintains the semantic performance in the automatic and human evaluations.