Lithium plating is commonly observed in anodes charged at fast rates, and can lead to capacity loss and battery safety issues. The increased risk of plating has been attributed to transport limitations, and architectured electrodes may reduce plating risk. However, while theoretical studies have shown that reaction non-uniformity arises due to interplay of transport limitations, anode open circuit voltage behavior and reaction kinetics, its effect on lithium plating has not been studied. We use analytic and numerical simulations to predict onset of plating in graphite anode half-cells at high C-rates and demonstrate how anodes with layered porosities can delay plating. Simplified analytical models identify trends for plating onset and predictions are validated against numerical models. A calibrated numerical model of graphite demonstrates qualitative agreement with analytical model predictions. This reaction inhomogeneity mechanism occurs in the absence of lithium ion depletion, indicating that these mechanisms may contribute to capacity loss independently or simultaneously. A bilayer model of graphite exhibits delayed plating onset, and an optimization procedure is presented. This theoretical work presents quantitative and mechanistic insight on how reaction inhomogenity affects lithium metal plating onset and can be used as a guide to engineer anodes resistant to lithium plating.

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In this work, an optofluidics based micro-photocatalytic fuel cell with a membrane-free and air-breathing mode was proposed to greatly enhance the cell performance. The incorporation of the optofluidic technology into a photocatalytic fuel cell not only enlarges the specific illumination and reaction area but also enhances the photon and mass transfer, which eventually boosts the photocatalytic reaction rate. Our results show that this new photocatalytic fuel cell yields a much higher performance in converting organics into electricity. A maximum power density of 0.58 mW cm(-2) was achieved. The degradation performance of this new optofluidic micro-photocatalytic fuel cell was also evaluated and the maximum degradation efficiency reached 83.9%. In short, the optofluidic micro-photocatalytic fuel cell developed in this work shows promising potential for simultaneously degrading organic pollutants and generating electricity.

Prostate cancer is a typical malignant disease with a high incidence and a poor prognosis. Doxorubicin hydrochloride (DOX·HCl) is one of the most effective agents in the treatment of prostate cancer, but severe side effects and metastasis after its treatment impose restrictions on its application. Herein, a combination of genistein (GEN) and doxorubicin-loaded polypeptide nanoparticles (DOX-NPs) is constructed for the treatment of prostate cancer. The DOX-NPs can reduce the side effects caused by free DOX·HCl and produce a relatively low level of intracellular reactive oxygen species (ROS)-induced oxidative damage, while GEN, an inhibitor of the oxidative DNA repair enzyme apurinic/apyrimidinic endonuclease1 (APE1), can further amplify the ROS-induced oxidative damage by downregulating the intracellular expression of APE1 and reducing oxidative DNA repair in the prostate cancer cells. Because high levels of ROS-induced oxidative damage can prevent the distant metastasis of tumor cells, the distant metastasis of malignant prostate cancer cells is significantly inhibited by the combination of genistein and DOX-NPs with amplified oxidative damage.

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The tendency of Li plating at the surface of thick graphite electrodes greatly limits its application in electrical vehicle (EV) batteries for fast charging applications. To address this concern, we...

Sparsity-inducing regularization problems are ubiquitous in machine learning applications, ranging from feature selection to model compression. In this paper, we present a novel stochastic method -Orthant Based Proximal Stochastic Gradient Method (OBProx-SG) -to solve perhaps the most popular instance, i.e., the 1-regularized problem. The OBProx-SG method contains two steps: (i) a proximal stochastic gradient step to predict a support cover of the solution; and (ii) an orthant step to aggressively enhance the sparsity level via orthant face projection. Compared to the state-of-the-art methods, e.g., Prox-SG, RDA and Prox-SVRG, the OBProx-SG not only converges to the global optimal solutions (in convex scenario) or the stationary points (in non-convex scenario), but also promotes the sparsity of the solutions substantially. Particularly, on a large number of convex problems, OBProx-SG outperforms the existing methods comprehensively in the aspect of sparsity exploration and objective values. Moreover, the experiments on non-convex deep neural networks, e.g., MobileNetV1 and ResNet18, further demonstrate its superiority by achieving the solutions of much higher sparsity without sacrificing generalization accuracy.