Conventional polymer/ceramic composite solid-state electrolytes (CPEs) have limitations in inhibiting lithium dendrite growth and fail to meet the contradictory requirements of anodes and cathodes. Herein, an asymmetrical poly­(vinylidene fluoride) (PVDF)–PbZr x Ti1–x O3 (PZT) CPE was prepared. The CPE incorporates high dielectric PZT nanoparticles, which enrich a dense thin layer on the anode side, making their dipole ends strongly electronegative. This attracts lithium ions (Li+) at the PVDF–PZT interface to transport through dipolar channels and promotes the dissociation of lithium salts into free Li+. Consequently, the CPE enables homogeneous lithium plating and suppresses dendrite growth. Meanwhile, the PVDF-enriched region at the cathode side ensures intermediate contact with positive active materials. Therefore, Li/PVDF-PZT CPE/Li symmetrical cells exhibit a stable cycling performance exceeding 1900 h at 0.1 mA cm–2 at 25 °C, outperforming Li/PVDF solid-state electrolyte/Li cells that fail after 120 h. The LiNi0.8Co0.1Mo0.1O2/PVDF–PZT CPE/Li cells show low interfacial impedances and maintain stable cycling performance for 500 cycles with a capacity retention of 86.2% at 0.5 C and 25 °C. This study introduces a strategy utilizing dielectric ceramics to construct dipolar channels, providing a uniform Li+ transport mechanism and inhibiting dendrite growth.

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In many sensor network applications, it is essential to get the data distribution of the attribute value over the network. Such data distribution can be got through clustering, which partitions the network into contiguous regions, each of which contains sensor nodes of a range of similar readings. This paper proposes a method named Distributed, Hierarchical Clustering (DHC) for online data analysis and mining in senior networks. Different from the acquisition and aggregation of raw sensory data, DHC clusters sensor nodes based on their current data values as well as their geographical proximity, and computes a summary for each cluster. Furthermore, these clusters, together with their summaries, are produced in a distributed, bottom-up manner. The resulting hierarchy of clusters and their summaries facilitates interactive data exploration at multiple resolutions. It can also be used to improve the efficiency of data-centric routing and query processing in sensor networks. We also design and evaluate the maintenance mechanisms for DHC to make it be able to work on evolving data. Our simulation results on real world datasets as well as synthetic datasets show the effectiveness and efficiency of our approach.