We are the first to examine the role of graphene host structure/chemistry in plating-stripping in lithium metal anodes employed for lithium metal batteries (LMBs). Structural and chemical defects are bad since highly defective graphene promotes unstable solid electrolyte interphase (SEI) growth. This consumes the FEC additive in the carbonate electrolyte and is correlated with rapid decay in CE and formation of filament-like Li dendrites. A unique flow-aided sonication exfoliation method is employed to synthesize "defect-free" graphene (df-G), allowing for a direct performance comparison with conventional reduced graphene oxide (r-GO). At cycle 1, the r-GO is better electrochemically wetted by Li than df-G, indicating that initially it is more lithiophilic. With cycling, the nucleation overpotential with r-GO becomes higher than with df-G, indicating less facile plating reactions. The df-G yields state-of-the-art electrochemical performance, with the post cycled metal surface being relatively-smooth and dendrite-free. Conversely, r-GO templates have CE rapidly degrade from the onset, with extensive dendrites after cycling. Severe SEI growth and associated FEC depletion with r-This article is protected by copyright. All rights reserved. 2GO are further confirmed by electrochemical impedance analysis (EIS) and surface science methods (XPS). We provide a new design rule for Li metal templates: An ideal host must be non-catalytic towards SEI formation per se.

Finding suitable electrode materials for alkali‐metal‐ion storage is vital to the next‐generation energy‐storage technologies. Polyantimonic acid (PAA, H2Sb2O6 · nH2O), having pentavalent antimony species and an interconnected tunnel‐like pyrochlore crystal framework, is a promising high‐capacity energy‐storage material. Fabricating electrochemically reversible PAA electrode materials for alkali‐metal‐ion storage is a challenge and has never been reported due to the extremely poor intrinsic electronic conductivity of PAA associated with the highest oxidation state Sb(V). Combining nanostructure engineering with a conductive‐network construction strategy, here is reported a facile one‐pot synthesis protocol for crafting uniform internal‐void‐containing PAA nano‐octahedra in a composite with nitrogen‐doped reduced graphene oxide nanosheets (PAA⊂N‐RGO), and for the first time, realizing the reversible storage of both Li+ and K+ ions in PAA⊂N‐RGO. Such an architecture, as validated by theoretical calculations and ex/in situ experiments, not only fully takes advantage of the large‐sized tunnel transport pathways (0.37 nm2) of PAA for fast solid‐phase ionic diffusion but also leads to exponentially increased electrical conductivity (3.3 S cm−1 in PAA⊂N‐RGO vs 4.8 × 10−10 S cm−1 in bare‐PAA) and yields an inside‐out buffer function for accommodating volume expansion. Compared to electrochemically irreversible bare‐PAA, PAA⊂N‐RGO manifests reversible conversion‐alloying of Sb(V) toward fast and durable Li+‐ and K+‐ion storage.

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Novel three-dimensional hierarchical flower-like TiO2/carbon (TiO2/C) nanostructures were in-situ synthesized via a solvothermal method involving calcination of organic precursor under inert atmosphere. The composite films comprised of P (VDF-HFP) and as-prepared hierarchical flower-like TiO2/C were fabricated by a solution casting and hot-pressing approach. The results reveal that loading the fillers with a small amount of carbon is an effective way to improve the dielectric constant and suppress the dielectric loss. In addition, TiO2/C particles with higher carbon contents exhibit superiority in promoting the dielectric constants of composites when compared with their noncarbon counterparts. For instance, the highest dielectric constant (330.6) of the TiO2/C composites is 10 times over that of noncarbon-TiO2-filled ones at the same filler volume fraction, and 32 times over that of pristine P (VDF-HFP). The enhancement in the dielectric constant can be attributed to the formation of a large network, which is composed of local micro-capacitors with carbon particles as electrodes and TiO2 as the dielectric in between.

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In this article, we provide a feasible method to prepare high-dielectric-constant hydrophobic composite films, which show potential application as the insulator layer in electrowetting devices.

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A variety of psychological scales are utilized at present as the most important basis for clinical diagnosis of mood disorders. An experienced psychiatrist assesses and diagnoses mood disorders based on clinical symptoms and relevant assessment scores. This symptom based clinical criterion is limited by the psychiatrist's experience. In practice, it is difficult to distinguish the patients with bipolar disorder with depression episode (bipolar depression, BD) from those with major depressive disorder (MDD). The functional near-infrared spectroscopy (fNIRS) technology is commonly used to perceive the emotions of a human. It measures the hemodynamic parameters of the brain, which correlate with cerebral activation. Here, we propose a machine learning classification method based on deep neural network for the brain activations of mood disorders. Large time scale connectivity is determined using an attention long short term memory neural network and short-time feature information are considered using the InceptionTime neural network in this method. Our combined method is referred to as AttentionLSTM-InceptionTime (ALSTMIT). We collected fNIRS data of 36 MDD patients and 48 BD patients who were in the depressed state. All the patients were monitored by fNIRS during conducting the verbal fluency task (VFT). We trained the model with the ALSTMIT network. The algorithm can distinguish the two types of patients effectively: the average accuracy of classification on the test set can reach 96.2% stably. The classification can provide an objective diagnosis tool for clinicians, and this algorithm may be critical for the early detection and precise treatment for the patients with mood disorders.