An Elastomeric Lithium-Conducting Interlayer for High-Performance LATP-Based Lithium Metal Batteries.

In response to the critical challenges of interfacial impedance and volumetric changes in Li(1+x)AlxTi(2­x)(PO4)3 (LATP)-based lithium metal batteries, an elastomeric lithium-conducting interlayer fabricates from fluorinated hydrogenated nitrile butadiene rubber (F-HNBR) matrix is introduced herein. Owing to the vulcanization, vapor-phase fluorination, and plasticization processes, the lithium-conducting interlayer exhibits a high elasticity of 423%, exceptional fatigue resistance (10 000 compression cycles), superior ionic conductivity of 6.3 × 10-4 S cm-1, and favorable lithiophilicity, rendering it an ideal buffer layer. By integrating the F-HNBR interlayer, the LATP-based lithium symmetric cells demonstrate an extended cycle life of up to 1600 h at 0.1 mA cm-2 and can also endure deep charge/discharge cycles (0.5 mAh cm-2) for the same duration. Furthermore, the corresponding lithium metal full cells achieve 500 cycles at 0.5 C with 98.3% capacity retention and enable a high-mass-loading cathode of 11.1 mg cm-2 to operate at room temperature.

High-Performance Quasi-Solid-State Lithium-Sulfur Battery with a Controllably Solidified Cathode-Electrolyte Interface.

Lithium-sulfur batteries are considered a promising "beyond Li-ion" energy storage technology. Currently, the practical realization of Li-S batteries is plagued by rapid electrochemical failure of S cathodes due to aggravated polysulfide dissolution and shuttle in the conventional liquid ether-based electrolytes. A gel polymer electrolyte obtained by in situ polymerization of liquid electrolyte solvent at the cathode-electrolyte interface has been proven an effective strategy to prevent polysulfide shuttle. However, notably reduced polysulfide solubility in the gel electrolyte leads to enrichment of poorly conductive sulfide species, which hinders charge migration across the interface and therefore accounts for retarded polysulfide conversion and a low capacity/energy output of batteries. Here, we show that thioacetamide, as a cathode additive, inhibits interfacial polymerization of ether molecules while assisting dissolution of polysulfides and Li2S at the cathode/electrolyte interface. In this way, a layer of liquid, sulfide-soluble electrolyte is preserved between the highly gelled electrolyte and the S particle surface, avoiding interfacial sulfide accumulation and improving polysulfide conversion kinetics. A Li-S battery with the controllably solidified interface demonstrates, without adding other performance-boosting agents or catalysts, a high reversible capacity, a long cycle life, and a favorable rate performance, showing promises for the next-generation storage applications.

Analysis of Gut Microbiota in Patients with Exacerbated Symptoms of Schizophrenia following Therapy with Amisulpride: A Pilot Study.

Evidence is mounting that the gut microbiome is related to the underlying pathogenesis of schizophrenia. However, effects of amisulpride on gut microbiota are poorly defined. This study was aimed at analyzing cytokines and fecal microbiota in patients with exacerbated symptoms of schizophrenia treated with amisulpride during four weeks of their hospital stay. In the present study, feces collected from patients with schizophrenia were analyzed using 16S rRNA pyrosequencing and bioinformatic analyses to ascertain gut microbiome composition and fasting peripheral blood cytokines. We found that patients undergoing treatment of schizophrenia with amisulpride had distinct changes in gut microbial composition at the genus level, increased levels of short-chain fatty acid-producing bacteria (Dorea and Butyricicoccus), and reduced levels of pathogenic bacteria (Actinomyces and Porphyromonas), but the level of Desulfovibrio was still high. We also found a significant downregulation of butanoate metabolism based on functional analysis of the microbiome. After treatment, elevated levels of interleukin- (IL-) 4 and decreased levels of IL-6 were found. Our findings extend prior work and suggest a possible pharmacological mechanism of amisulpride treatment for schizophrenia, which acts via mediation of the gut microbiome.

Diagnosis of Schizophrenia Based on Deep Learning Using fMRI.

Schizophrenia is a brain disease that frequently occurs in young people. Early diagnosis and treatment can reduce family burdens and reduce social costs. There is no objective evaluation index for schizophrenia. In order to improve the classification effect of traditional classification methods on magnetic resonance data, a method of classification of functional magnetic resonance imaging data is proposed in conjunction with the convolutional neural network algorithm. We take functional magnetic resonance imaging (fMRI) data for schizophrenia as an example, to extract effective time series from preprocessed fMRI data, and perform correlation analysis on regions of interest, using transfer learning and VGG16 net, and the functional connection between schizophrenia and healthy controls is classified. Experimental results show that the classification accuracy of fMRI based on VGG16 is up to 84.3%. On the one hand, it can improve the early diagnosis of schizophrenia, and on the other hand, it can solve the classification problem of small samples and high-dimensional data and effectively improve the generalization ability of deep learning models.