Electrochemically Determining Electronic Structure of ZnO Quantum Dots with Different Surface Ligands

The electronic structure of quantum dots (QDs) including band edges and possible trap states is an important physical property for optoelectronic applications. The reliable determination of the energy levels of QDs remains a big challenge. Herein we employ cyclic voltammetry (CV) to determine the energy levels of three types of ZnO QDs with different surface ligands. Coupled with spectroscopic techniques, it is found that the onset potential of the first reductive wave is likely related to the conduction band edges while the first oxidative wave originates from the trap states. The determined specific energy levels in CV further demonstrates that the ZnO QDs without surface ligands mainly have oxygen interstitial defects whilst the ZnO QDs covered with ligands contain oxygen vacancies. The present electrochemical method offers a powerful and effective way to determine the energy levels of wide bandgap ZnO QDs, which will boost their device performance.     

Phosphoric-Acid Retention in High-Temperature Proton-Exchange Membranes

Great efforts have been conducted to develop high temperature proton exchange membrane fuel cell (HT-PEMFC) due to its features of enhanced electrocatalyst reactivity, simplified hydrothermal management system and high CO tolerance of catalysts, and remarkable progress has been achieved. However, the easy leaching of phosphoric acid (PA) from the membranes during operation limits its commercial scale-up in complicated environments. This concept here mainly focuses on the recent developments for mitigation of PA loss in PEMs. The probable mechanisms of PA loss are proposed. The approaches to improve PA retention for example via introduction of phosphonic acid by covalent bond, using ion-pairs interaction and siphoning effect, and blending with inorganic nanoparticles are described in detail. Among these strategies, the siphoning effect from the intrinsic microporous PEMs is the most efficient and enables the cell to operate flexibly within a broad temperature range. Therefore, this concept may provide new ideas for the scientists to retain PA, to improve the cell performance and expand the potential applications of PA doped PEMs at elevated humidity and wide temperature range.     

热解吸反压低温等离子体电离源质谱检测食品中的邻苯二甲酸酯EI北大核心CSCD

基于低温等离子体设计并实现了一套热解吸反压低温等离子体电离(TD-iLTPI)装置。TD-iLTPI装置由热解吸模块和电离源模块集成在一个π型四通管上组成,进样探针取得样品后插入热解吸模块使其气化,之后随载气进入电离区域被电离。iLTPI内部的针电极接交流电,外部的环形电极接地,电极连接方式与LTP相反。该文对热解吸反压低温等离子体电离源进行了参数的优化,并与三重四极杆质谱联用检测了12种代表性的邻苯二甲酸酯,同时考察了TD-iLTPI-MS的分析性能,并对含有邻苯二甲酸酯的实际样品进行了检测。结果表明,邻苯二甲酸丁苄酯(BBP)的标准曲线具有良好的线性相关系数(R;=0.9958),加标回收率为89.7%~116.8%,相对标准偏差为4.5%~8.2%,对邻苯二甲酸酯的检出限也远低于其他敞开式离子源。对白酒、果汁、面包、奶酪和黄油中的邻苯二甲酸酯进行了快速筛查,并定量检测了果汁中的邻苯二甲酸丁苄酯。     

W-translated Schubert divisors and transversal intersections

Science China Mathematics - We study the toric degeneration of Weyl group translated Schubert divisors of a partial flag variety $$F{\ell _{{n_1}, \ldots,{n_k};n}}$$ via Gelfand-Cetlin polytopes....     

基于快速神经网络架构搜索的鲁棒图像水印网络算法

为解决深度学习在图像水印算法中计算量大且模型冗余的问题，提高图像水印算法在抵抗噪声、旋转和剪裁等攻击时的鲁棒性，提出基于快速神经网络架构搜索（neural architecture search，NAS）的鲁棒图像水印网络算法。通过多项式分布学习快速神经网络架构搜索算法，在预设的搜索空间中搜索最优网络结构，进行图像水印的高效嵌入与鲁棒提取。首先，将子网络中线性连接的全卷积层设置为独立的神经单元结构，并参数化表示结构单元内节点的连接，预先设定结构单元内每个神经元操作的搜索空间；其次，在完成一个批次的数据集训练后，依据神经元操作中的被采样次数和平均损失函数值动态更新概率；最后，重新训练搜索完成的网络。水印网络模型的参数量较原始网络模型缩减了92%以上，大大缩短了模型训练时间。由于搜索得到的网络结构更为紧凑，本文算法具有较高的时间性能和较好的实验效果，在隐藏图像时，对空域信息的依赖比原始网络更少。对改进前后的2个网络进行了大量鲁棒性实验，对比发现，本文算法在CIFAR-10数据集上对抵抗椒盐噪声和旋转、移除像素行（列）等攻击优势显著；在ImageNet数据集上对抵抗椒盐高斯噪声、旋转、中值滤波、高斯滤波、JPEG压缩、裁剪等攻击优势显著，特别是对随机移除行（列）和椒盐噪声有较强的鲁棒性。     

An elementary proof for the representation theorem of analytic isotropic tensor functions of a second-order tensor

正following Cayley-Hamilton theorem:(1)where I1, I2, and I3denote the first, second, and third principal invariants of and N-dimensional space,(2)