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Ceramic–polymer composite electrolytes (CPEs) are being explored to achieve both high ionic conductivity and mechanical flexibility. Here, we show that, by incorporating 10 wt % (3 vol %) mixed-sized fillers of Li7La3Zr2O12 (LLZO) doped with Nb/Al, the room-temperature ionic conductivity of a polyvinylidene fluoride (PVDF)–LiClO4-based composite can be as high as 2.6 × 10–4 S/cm, which is 1 order of magnitude higher than that with nano- or micrometer-sized LLZO particles as fillers. The CPE also shows a high lithium-ion transference number of 0.682, a stable and low Li/CPE interfacial resistance, and good mechanical properties favorable for all-solid-state lithium-ion battery applications. X-ray photoelectron spectroscopy and Raman analysis demonstrate that the LLZO fillers of all sizes interact with PVDF and LiClO4. High packing density (i.e., lower porosity) and long conducting pathways are believed responsible for the excellent performance of the composite electrolyte filled with mixed-sized ionically conducting ceramic particles.

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Large brain imaging databases contain a wealth of information on brain organization in the populations they target, and on individual variability. While such databases have been used to study group-level features of populations directly, they are currently underutilized as a resource to inform single-subject analysis. Here, we propose leveraging the information contained in large functional magnetic resonance imaging (fMRI) databases by establishing population priors to employ in an empirical Bayesian framework. We focus on estimation of brain networks as source signals in independent component analysis (ICA). We formulate a hierarchical "template" ICA model where source signals-including known population brain networks and subject-specific signals-are represented as latent variables. For estimation, we derive an expectation maximization (EM) algorithm having an explicit solution. However, as this solution is computationally intractable, we also consider an approximate subspace algorithm and a faster two-stage approach. Through extensive simulation studies, we assess performance of both methods and compare with dual regression, a popular but ad-hoc method. The two proposed algorithms have similar performance, and both dramatically outperform dual regression. We also conduct a reliability study utilizing the Human Connectome Project and find that template ICA achieves substantially better performance than dual regression, achieving 75-250% higher intra-subject reliability.

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