高性能LiFePO4/碳纳米笼锂离子电池正极材料

以具有高比表面积、分级孔结构和优良导电性的碳纳米笼（CNCs）为载体,制得了粒子尺寸为10～25 nm且高度分散的LiFePO₄/CNCs复合物.以LiFePO₄/CNCs复合物作为锂离子电池的正极材料,在0.1 C倍率下首次放电比容量达到163 mAh·g^-1,15 C和30 C倍率下的放电比容量可达96和75 mAh·g^-1;在15 C倍率下循环200圈后,其放电比容量仍保持在92 mAh·g^-1,显著优于LiFePO₄/CNTs复合物.这些结果表明,LiFePO₄/CNCs复合物具有优异的倍率性能和循环稳定性,是一种性能优良的锂离子电池正极材料,其性能源自CNCs载体的高比表面积、分级孔结构和优异导电性以及LiFePO₄颗粒的纳米化和高结晶度.     

大尺寸固体氧化物燃料电池的电极过程解析方法

电化学阻抗谱(Electrochemical Impedance Spectroscopy,EIS)作为一种原位/非原位的电化学表征技术,在固体氧化物燃料电池(Solid Oxide Fuel Cell,SOFC)尤其是小尺寸电池的研究中得到了广泛应用,而工业大尺寸电池的EIS研究较少且大多基于小尺寸电池的研究结果。本文对工业尺寸(10 cm × 10 cm)阳极支撑平板式SOFC搭建了EIS测试系统,并改变电池运行温度、阳极/阴极气体组分,对该电池进行了系统的EIS测试,而后采用不基于先验假设的弛豫时间分布法(Distribution of Relaxation Times,DRT)对EIS数据进行解析。通过比较分析不同条件下的DRT结果,揭示了DRT中各特征峰与电池中具体电极过程的对应关系。与小尺寸电池相比,由于大尺寸电池的有效面积较大且入口流量较小,气体转化过程在大尺寸电池中不容忽视。本文通过解析EIS实现了对工业大尺寸SOFC单电池中各项电极过程的分辨,该方法及结果能够进一步应用于SOFC原位表征、在线监测以及衰减机理等相关研究。     

采用响应面回归的汽车多属性声品质预测方法*

单一主观评价分数无法准确描述人耳对声品质的多属性偏好特征。该文在多属性声品质试验数据的基础上,以愉悦度、平顺度和驾驶乐趣的主观评价分数为因变量,通过相关分析筛选出响度、尖锐度和A计权声压级3个主要自变量,引入响应面回归方法,分别建立因变量与自变量之间的预测模型,通过与多元线性模型和BP神经网络模型对比验证了精度。最后,建立多属性主观评价分数之间的量化映射模型。该研究可为多属性汽车声品质的优化控制提供参考。     

Measurement and simulation of partial discharge in oil-paper insulation under the combined AC–DC voltage

Depending on unique operation characteristics for voltage applied on valve side winding of the converter transformers, it is extraordinarily significant to study the partial discharge (PD) behavior with oil-paper insulation under combined AC–DC voltage. Therefore, this paper investigated PD inception characteristics by pulse current methods with needle-plate electrode system under combined AC–DC voltage. Furthermore, 3D electric field distributions versus combined AC–DC voltage in different ratios were calculated by Finite Element Analysis (FEA). An experimental conclusion was drew that AC partial discharge inception voltage (PDIV) in pure oil would decrease linearly with the DC component increasing but the inception voltage with oil-paper insulation appeared to be independent of DC voltage and dependent of AC voltage. And 3D electric field distribution deduced from simulation provided a supplementary proof on the experimental results. Moreover, high speed photography was used to capture emitted light produced by discharge, estimate streamer velocity (1.8 km/s) and record streamer initiation and propagation process in oil gap. Previous studies have shown that the prebreakdown phenomena involving the generation and propagation of vapor channels through the oil could be divided into a three-stage process.     

Quasi-Monte Carlo Sampling for Solving Partial Differential Equations by Deep Neural Networks

Solving partial differential equations in high dimensions by deep neural networks has brought significant attentions in recent years. In many scenarios, the loss function is defined as an integral over a high-dimensional domain. Monte-Carlo method, together with a deep neural network, is used to overcome the curse of dimensionality, while classical methods fail. Often, a neural network outperforms classical numerical methods in terms of both accuracy and efficiency. In this paper, we propose to use quasi-Monte Carlo sampling, instead of Monte-Carlo method to approximate the loss function. To demonstrate the idea, we conduct numerical experiments in the framework of deep Ritz method. For the same accuracy requirement, it is observed that quasi-Monte Carlo sampling reduces the size of training data set by more than two orders of magnitude compared to that of Monte-Carlo method. Under some assumptions, we can prove that quasi-Monte Carlo sampling together with the deep neural network generates a convergent series with rate proportional to the approximation accuracy of quasi-Monte Carlo method for numerical integration. Numerically the fitted convergence rate is a bit smaller, but the proposed approach always outperforms Monte Carlo method.     

Synthesized high-silica hierarchical porous ZSM-5 and optimization of its reaction conditions in benzene alkylation with methanol

41.4% yield of xylene was obtained over high-silica hierarchical ZSM-5 catalyst by optimizing reaction conditions of benzene alkylation with methanol.     

Preparation of magnetic Fe3O4/P (GMA‐DVB)‐PEI/Pd highly efficient catalyst with core‐shell structure

In this paper, a simple route for palladium (Pd) nanoparticles attached to the surface of hollow magnetic Fe₃O₄/P (GMA‐DVB)‐polyethyleneimine (PEI) microspheres was established. Due to the large amount of imidogen groups and tertiary amine groups presenting in the PEI, Pd²⁺ ions could be anchored to the support by complexation with a polyfunctional organic ligand. Thereafter, a magnetic Pd catalyst having a high loading amount and good dispersibility was obtained by reducing Pd²⁺ ions. Afterwards, the prepared catalyst was characterized by TEM, SEM, FTIR, XRD, TGA, VSM, and UV–vis in detail. Ultimately, their catalytic activity was evaluated using the reduction of 4‐nitrophenol (4‐NP). Research showed that the Fe₃O₄/P (GMA‐DVB)‐PEI/Pd catalyst possessed high catalytic performances for the reduction of 4‐NP with a conversion rate of 98.43% within 540 s. Furthermore, the catalyst could be easily recovered and reused at least for nine successive cycles.     

Hydrothermal and plasma nitrided electrospun carbon nanofibers for amperometric sensing of hydrogen peroxide

Microchimica Acta - A fluorometric method is presented for sensitive deternination of microRNA. It is making use of carbon dots (C-dots) loaded with a DNA probe as fluorophore and MnO2 nanosheets...