Preparation and Properties of Poly（ether ether ketone）/Carbon Nanotube Modified Polypropylene Janus Composite Separator北大核心CSCD

Lithium-ion batteries （LIBs）have attracted wide attention because of their broad prospects in electric vehicles. However,the safety problems and low multiplier performance of the commercial polyolefin separator limit their further development,due to the poor dimensional thermal stability and low electrolyte absorption rate. Poly（ether ether ketone）（PEEK）and carbon nanotube（CNT）are compounded to coat on polypropylene （PP） to prepare Janus composite separator （PP@C） through the phase inversion method. PP@C composite separator does not deform at 180 ℃ for 0. 5 h（only slight deformation）,illustrating excellent thermal stability. The electrolyte absorption rate of PP@C2 is 193. 8%. The electrolyte uptake rate of the PP@C2 composite membrane was 193. 8%,which was 64. 5% higher than the PP membrane,showing a superduper electrolyte permeability. As a result,the specific discharge capacity of LIBs assembled with PP@C2 composite separator is 157. 6 mA·h/g at 0. 2 C and 129. 8 mA·h/g at 2 C,showing good rate performance with the capacity recovery rate of more than 99%. This might be attributed to the ultra-high thermal stability of PEEK,the good affinity of the electrolyte,the high conductivity of CNT,as well as the uniform dispersion of Li+ ,so that the separator can have excellent electrochemical performance while improving safety. © 2022, Science Press (China). All rights reserved.     

小波神经网络模型预测二氧化碳+水溶液体系界面张力

二氧化碳+水溶液体系界面张力(IFT)是影响地层中气水两相运移特性的重要参数之一,对二氧化碳捕集、埋存至关重要.为了快速准确地确定二氧化碳+水溶液体系IFT,对已有IFT实验结果进行了统计整理,得到了1 677组样本数据,考虑了压力,温度,气体中甲烷、氮气含量,水溶液中一价阳离子(Na+,K+)浓度、二价阳离子(Ca2+,Mg2+)浓度6个因素对IFT的影响,建立了小波神经网络（WNN）预测模型对二氧化碳+水溶液体系IFT进行预测.模拟结果表明,随机选取839组数据作为训练集样本,得到的小波神经网络结构为6-16-1,该模型预测IFT的平均绝对误差(M_MAE)、平均相对误差(M_MARE)、方差(M_MSE)和相关度(R2)分别为1.23 mN/m,3.30%,2.30 mN2/m2,0.988.与最新提出的多元拟合模型和BP神经网络模型对比结果表明,小波神经网络模型预测精度最高.