流动注射-化学发光法测定药物中氢溴酸右美沙芬

试验发现由氢溴酸右美沙芬(D-MPHB)与高锰酸钾在5.0 mmol·L-1硫酸介质中反应所产生的较弱的化学发光强度因一定量的亚硫酸钠的存在而明显增强,且其化学发光的相对强度与D-MPHB浓度在1.0×10-5～1.0×10-1·L-1范围内呈线性关系,检出限(3S/N)为3.5×10-6g·L-1,根据这些结果并应用流动注射技术,提出了FI-CL法测定药物中氢溴酸右美沙芬的方法.应用所提出的方法分析了一种氢溴酸右美沙芬滴鼻液,测得其中D-MPHB的含量与标准值相符.以此药物为基体对方法的精密度及回收率作了试验,测得其相对标准偏差(n=5)均小于1.5%,回收率在99.7%～102.8%之间.     

微纳光子结构中光子和激子相互作用

微纳光子结构中超强的光场局域给光和物质相互作用带来了新的研究机遇.通过设计光学模式,微纳结构中的光子和激子可以实现可逆或者不可逆的能量交换作用.本文综述了我们近年来在微纳结构,尤其是表面等离激元及其复合结构中光子和激子在强弱耦合区域的系列研究工作,如高效可调谐及方向性的单光子发射,利用电磁真空构造增强光子和激子的耦合等.这些工作为微纳尺度上光和物质作用提供了新的物理内容,在芯片上量子信息过程及可扩展的量子网络构建中有潜在应用.     

基于熵学习机的恒星光谱分类（英文）

数据挖掘被广泛应用于恒星光谱分类。为了提高传统光谱分类方法性能,提出熵学习机(Entropybased Learning Machine,ELM)。在该方法中,熵用来刻画分类的不确定性。为了得到理想的分类结果,分类的不确定性应最小,基于此,可得ELM的最优化问题。ELM在处理二分类问题和稀有光谱发现等方面具有一定优势。SDSS中K型、F型、G型恒星光谱数据集上的比较实验表明:ELM在进行恒星光谱分类时,其分类性能优于k近邻(k Nearest Neighbor)和支持向量机(Support Vector Machine)等传统分类方法。     

Enhancing coupling coefficient in a hybrid nanotoroid–nanowire system

Enhancing light–matter interaction in cavity quantum electrodynamics has aroused widespread interests in on-chip quantum information processing. Here, we propose a hybrid nanotoroid–nanowire system to enhance photon–exciton interaction. A nanoscale gap is formed by placing a dielectric nanowire close to a dielectric nanotoroid, where the coupling coefficient between photon and emitter can achieve 5.55 times of that without nanogap. Meanwhile, the cavity loss and spontaneous emission of the emitter will remain at a small value to guarantee the realization of strong coupling. The method might hold promise for the research of nanophotonics,quantum optics, and novel optical devices.     

基于磁性分离-硫化铜纳米粒子标记的DNA流动注射化学发光检测

本文基于磁性粒子(MB)良好的分离、富集能力,研究了硫化铜纳米粒子标记的流动注射-化学发光(FI-CL)DNA检测体系.通过硫化铜标记的探针1与目标DNA及连有磁球的探针2形成三明治结构,实现对目标DNA的捕获、分离与标记;通过其溶解释放出CuS标记颗粒的铜离子,引起化学发光信号增强,实现了目标DNA序列的定性定量检测.该方法对完全互补单链DNA(ssDNA)检测的线性范围为1.0×10-11～1.6×10-9 mol/L,检出限为3.0×10-12 mol/L,对1.0×10-9 mol/L目标DNA测定的相对标准偏差为3.2%(n=11),对目标碱基序列具有良好的识别能力.     

利用基于互信息的不平衡分类方法识别稀有光谱

从海量恒星光谱中发现稀有光谱是天文学研究的重要课题之一。与一般光谱相比,稀有光谱数量较少,因此,传统分类方法无法正常工作。究其原因是这些方法不仅在分类决策时并未对稀有光谱予以更多关注,而且只关注分类的准确率。鉴于此,在总结当前分类方法的基础上,深入分析互信息与决策树之间的关系,提出基于互信息的代价缺失决策树。SDSS DR8中K型、F型、G型以及M型恒星光谱上的比较实验表明,与传统分类方法相比,所提方法能够较好地完成稀有光谱识别的任务。     

LAMOST medium-resolution spectroscopic survey of binarity and exotic star (LAMOST-MRS-B): Observation strategy and target selection

LAMOST-MRS-B is one of the sub-surveys of LAMOST medium-resolution ($R\sim7500$) spectroscopic survey. It aims at studying the statistical properties (e.g., binary fraction, orbital period distribution, mass ratio distribution) of binary stars and exotic stars. We intend to observe about 30000 stars ($10 {\rm mag}\leq G < 14.5$ mag) with at least 10 visits in five years. We first planned to observe 25 plates around the galactic plane in 2018. Then the plates were reduced to 12 in 2019 because of the limitation of observation. At the same time, two new plates located at the high galactic latitude were added to explore binary properties influenced by the different environments. In this survey project, we set the identified exotic and low-metallicity stars with the highest observation priorities. For the rest of the selected stars, we gave the higher priority to the relatively brighter stars in order to obtain high quality spectra as many as possible. Spectra of 49129 stars have been obtained in LAMOST-MRS-B field and released in DR8, of which 28828 and 3375 stars have been visited more than twice and ten times with $\rm SNR \geq 10$, respectively. Most of the sources are B-, A-, and F-type stars with $\rm -0.6< [Fe/H] < 0.4 $ dex. We also obtain 347 identified variable and exotic stars and about 250 stars with $\rm [Fe/H] <-1$ dex. We measure radial velocities (RVs) by using 892233 spectra of the stars. The uncertainties of RV achieve about $1$ ${\rm km \cdot s^{-1}}$and $10$ ${\rm km \cdot s^{-1}}$ for 95% of late- and early-type stars, respectively. The datasets presented in this paper are available at http://www.doi.org/10.57760/sciencedb.j00113.00035.