Experimental investigation of electrode cycle performance and electrochemical kinetic performance under stress loading

Lithium-ion batteries suffer from mechano–electrochemical coupling problems that directly determine the battery life. In this paper, we investigate the electrode electrochemical performance under stress conditions, where seven tensile/compressive stresses are designed and loaded on electrodes, thereby decoupling mechanics and electrochemistry through incremental stress loads. Four types of multi-group electrochemical tests under tensile/compressive stress loading and normal package loading are performed to quantitatively characterize the effects of tensile stress and compressive stress on cycle performance and the kinetic performance of a silicon composite electrode. Experiments show that a tensile stress improves the electrochemical performance of a silicon composite electrode, exhibiting increased specific capacity and capacity retention rate, reduced energy dissipation rate and impedances, enhanced reactivity, accelerated ion/electron migration and diffusion, and reduced polarization. Contrarily, a compressive stress has the opposite effect, inhibiting the electrochemical performance. The stress effect is nonlinear, and a more obvious suppression via compressive stress is observed than an enhancement via tensile stress. For example, a tensile stress of 675 k Pa increases diffusion coefficient by 32.5%, while a compressive stress reduces it by 35%. Based on the experimental results, the stress regulation mechanism is analyzed. Tensile stress loads increase the pores of the electrode material microstructure, providing more deformation spaces and ion/electron transport channels. This relieves contact compressive stress, strengthens diffusion/reaction, and reduces the degree of damage and energy dissipation. Thus, the essence of stress enhancement is that it improves and optimizes diffusion, reaction and stress in the microstructure of electrode material as well as their interactions via physical morphology.     

脑自发性神经振荡低频振幅表征脑功能网络静息态信息流

已有研究表明,任务诱发的局部脑区激活可通过全脑功能网络中脑区间的神经活动信息流（activity flow,AF）预测,然而静息态下自发脑神经振荡与AF的关系仍不清楚。本文旨在研究静息态自发神经活动是否也反映脑区间在功能连接(functional connectivity,FC)路径上的信息传播。用来自千人脑功能连接组计划中的197名健康被试的静息态功能磁共振成像（RS-fMRI）数据,计算全脑160个感兴趣区的自发性神经振荡低频振幅（amplitude of low-frequency fluctuation,ALFF）,采用相关性分析和多元回归分析2种方法计算脑区间FC;基于AF模型,通过ALFF与FC的加权和估计汇聚到目标脑区的AF,利用Pearson相关性分析在全脑层面和默认网络脑区层面ALFF与AF之间的空间相关性。结果表明,在全脑层面和默认网络（default-mode network,DMN）脑区层面,AF与ALFF的分布模式显著相关;用多元回归改进FC估计可改善预测效果。低频振幅不仅体现脑区局部的神经振荡和功能情况,同时也反映自发性脑神经活动通过FC通道在脑区间进行信息交互。