电化学免疫传感器测定牛奶中的青霉素

利用共价键合法,将新亚甲蓝(NMB)与辣根过氧化酶(HRP)标记的青霉素多克隆抗体(Ab*)修饰于玻碳电极表面,制成电流型免疫传感器;考察了该传感器的电化学行为和对H2O2的电化学响应.结果表明,NMB作为介体能有效地传递电子,传感器对H2O2具有很好的催化作用.用此传感器对牛奶中的青霉素进行检测,其线性范围为0.25～3.00ng/mL (R=0.984),检出限为0.298ng/mL.     

一种蓝光发射星型有机分子的合成及光电性质

采用分子设计的思想,通过多步反应合成了一种新型的以N原子为中心的蓝光发射星型有机小分子。用1H NMR,MS和元素分析进行了表征,研究了化合物的热稳定性和真空镀膜膜层的光致发光性质,用循环伏安法测定了其电化学性能。结果表明,这种合成的有机化合物光致发光性能优良(量子效率达到87%),热稳定性好,可作为制作有机电致发光器件的候选材料。     

Theoretical Study of Reaction Mechanism of 1-Propenyl Radical with NO

The reaction system of 1-propenyl radical with NO is an ideal model for studying the intermolecular and intramolecular reactions of complex organic free radicals containing C=C double bonds. On the basis of the full optimization of all species with the Gaussian 98 package at the B3LYP/6-311++G** level, the reaction mechanism was elucidated extensively using the vibrational mode analysis. There are seven reaction pathways and five sets of small molecule end products: CH2O+CH3CN, CH2CHCN+H2O, CH3CHO+HCN, CH3CHO+HNC, and CH3CCH+HNO. The channel of C3H5￠+NO→ IM1→TS1→IM2→TS2→IM3→TS3→CH3CHO+HCN is thermodynamically most favorable.     

人工智能助力当代化学研究

以机器学习为代表的人工智能在当代的科学研究中正在发挥越来越重要的作用.不同于传统的计算机程序,机器学习人工智能可以通过对大量数据的反复分析和自身模型的优化,即"学习"过程,从而在大量的数据中寻找客观事物的相互联系,形成具有更好预测和决策能力的新模型,做出合理的判断.化学研究的特点恰恰是机器学习人工智能的强项.化学研究经常要面对十分复杂的物质体系和实验过程,从而很难通过化学物理原理进行精准的分析和判断.人工智能可以挖掘化学实验中产生的海量实验数据的相关性,帮助化学家做出合理分析预测,大大加速化学研发过程.本文介绍了当代人工智能方法及用其解决化学问题基本原理,并通过具体案例展示了人工智能辅助解决不同化学研发问题的方法以及对应的机器学习算法.将人工智能运用在化学科学的尝试正处于蓬勃上升期,人工智能已经初步展示出对化学研究的强大助力,希望本文能帮助更多的国内的化学工作者了解和运用这一有力的工具.     

具有高稳定性以及光催化产氢活性的奇数环状异金属钛氧纳米团簇

利用太阳能是解决当前能源危机和环境问题的有效途径.二氧化钛是一种稳定性高、环境友好的新型光催化剂.近年来,具有精确结构信息的晶态钛氧团簇(PTCs)作为TiO2的分子结构模型受到广泛关注.目前大多数PTCs含有烷氧基,在空气中易发生水解.这在很大程度上限制了对其光催化性能的研究.在无机钛氧簇的外围修饰大量的共轭有机配体...     

Point Cloud Geometry Compression Based on Multi-Layer Residual Structure

Point cloud data are extensively used in various applications, such as autonomous driving and augmented reality since it can provide both detailed and realistic depictions of 3D scenes or objects. Meanwhile, 3D point clouds generally occupy a large amount of storage space that is a big burden for efficient communication. However, it is difficult to efficiently compress such sparse, disordered, non-uniform and high dimensional data. Therefore, this work proposes a novel deep-learning framework for point cloud geometric compression based on an autoencoder architecture. Specifically, a multi-layer residual module is designed on a sparse convolution-based autoencoders that progressively down-samples the input point clouds and reconstructs the point clouds in a hierarchically way. It effectively constrains the accuracy of the sampling process at the encoder side, which significantly preserves the feature information with a decrease in the data volume. Compared with the state-of-the-art geometry-based point cloud compression (G-PCC) schemes, our approach obtains more than 70–90% BD-Rate gain on an object point cloud dataset and achieves a better point cloud reconstruction quality. Additionally, compared to the state-of-the-art PCGCv2, we achieve an average gain of about 10% in BD-Rate.     

Molecular Beam Epitaxy Growth and Electronic Structures of Monolayer GdTe_3

GdTe_3 is a layered antiferromagnetic(AFM) metal with charge density wave(CDW).We grew monolayer(ML)GdTe_3 on graphene/6H-SiC(0001) substrates by molecular beam epitaxy.The electronic and magnetic structures are studied by scanning tunneling microscopy/spectroscopy,quasi-particle interference(QPI) and first-principles calculations.Strong evidence of CDW persisting at the two-dimensional(2D) limit is found.Band dispersions and partially gapped energy bands near the Fermi surface are revealed by the QPI patterns.By density functional theory +U calculations,AFM order with stripe pattern is found to be the magnetic ground state for ML GdTe_(3).These results provide fundamental understanding and pave the way for further investigation of GdTe_3 at the 2D limit.     

Locally periodically poled LNOI ridge waveguide for second harmonic generation

Periodically poled lithium niobate on insulator[LNOI]ridge waveguides are desirable for high-efficiency nonlinear frequency conversions,and the fabrication proc...     

以系列三维立体投影实验为例谈大学物理实验中的创新型人才培养

创新能力是本世纪的核心竞争力,其关键是创新型人才的培养,这为理工类高校的实验教学提出了新的挑战.目前高校中的以学生为主体,教师为主导的大学生创新实验项目的实施是解决此挑战的有效途径之一.以笔者承担的系列三维立体投影物理实验的大学生创新实验项目为例,探讨了大学物理实验中的创新型人才培养模式.通过循序渐进的实验过程,引导学生自主搭建实验装置和编写控制程序,有效培养了学生的创新和实践能力,为高校实验教学改革提供了参考.