Improved electrochemical performance of silicon-carbon anodes by different conductive agents

Journal of Materials Science: Materials in Electronics - Silica/silicon/polyacrylonitrile (SiO2/Si/PAN) composites were prepared as active anode for lithium-ion batteries (LIBs) using a rational...     

Research and Application of Plunger Airlift Draining Gas Recovery Intelligent Control Technology in Shale Gas Horizontal Wells

Chemistry and Technology of Fuels and Oils - For shale gas wells, plunger lift is challenged by the difficulties like difficult tool tripping in and high operation risks due to the liquid phase...     

应用型人才培养模式下的材料科学基础实验课程改革

材料科学基础实验是我校高分子材料与工程专业必修的实验课程，是与材料科学基础课程理论知识相配套的应用实践课，本文结合我校应用型人才培养模式，针对目前开设的实验课程中的不足，通过对材料科学基础实验教学方法、实验类型以及考核方式等方面进行改革，提高学生学习兴趣、综合分析问题与实践创新能力，为培养社会发展需要的高质量应用型人才奠定基础。     

硼亲和材料的制备方法新进展

近年来，硼亲和技术由于选择性好、质谱兼容性高、操作简便等优点，成为分析化学、生物医学等领域的研究热点，广泛应用于儿茶酚、核苷、糖蛋白、糖肽等顺式二羟基生物分子的分离富集和检测。本文对近年来新型硼亲和材料的制备方法进行了评述，主要包括点击化学法、原子转移自由基聚合法、超支化聚合物修饰法、分子印迹法和整体合成法，以期为新型硼亲和材料的发展提供参考。     

Performance of a Degassing Cyclone with Main and Subsidiary Chambers

Based on the premise that large bubbles are removed in larger cyclones and small bubbles in smaller cyclones, a combined degassing cyclone with main and subsidiary chambers was designed to enhance liquid degassing. The pressure loss, liquid flow rate at the gas outlet, split ratio, gas flow rate at the liquid outlet, and degassing efficiency of the degassing cyclone were measured and calculated. Pressure loss correlations were established which relates the Euler number to the gas and liquid Reynolds numbers in the main chamber. Most cases exhibit a degassing efficiency greater than 0.998 when the liquid flow rate is more than 0.7 m³h^?1. The contours of pressure loss, split ratio, and degassing efficiency provide an effective guidance for designing a degassing cyclone.     

基于SVD和SSA-VMD降噪的轴承故障特征提取

针对强噪声背景下轴承故障特征提取困难的问题，提出一种基于奇异值分解和参数优化变分模态分解联合降噪的轴承故障特征提取方法(SSVMD)：首先，对原始信号进行奇异值分解(Singular Value Decomposition，SVD)处理，运用奇异值差分谱法选取有效奇异值并将原始信号重构得到初步降噪信号；其次，为防止故障信息丢失，将残余信号进行麻雀算法(Sparrow Search Algorithm，SSA)优化的变分模态分解(Variational Mode Decomposition，VMD）算法处理，得到最佳的模态个数K和惩罚参数α，选取峭度值最大、包络熵最小的IMF分量与初步降噪信号叠加得到最终降噪信号，并对信号进行包络分析；最后，通过仿真和试验数据分析得出，该方法能在信噪比很低的情况下降低噪声含量并提取轴承故障特征，为设备的状态监测和故障诊断提供理论依据。     

CDBN‑IKELM的轴承变工况故障诊断方法

针对现有方法在轴承变工况方面存在的诊断精度低、人工提取特征不充分等问题，提出了基于卷积深度置信网络（convolutional deep belief network，简称CDBN）与改进核极限学习机 （improved Kernel?based extreme learning machine，简称IKELM）的滚动轴承故障智能识别方法。首先，由卷积深度置信网络对原始信号内的故障特征进行深层自适应提取；其次，利用等距特征映射对提取的多维特征进行降维，去除冗余特征信息；然后，采用改进的核极限学习机对特征进行分类，使用粒子群（particle swarm optimization， 简称PSO）对模型重要参数进行优化，实现滚动轴承变工况下的故障识别；最后，将所提方法应用于不同工况下多种轴承故障的诊断。实验结果表明，该方法能够智能有效地识别变工况的轴承故障，诊断结果优于已有的智能故障诊断方法。