Tailoring Pore Structure and Morphologies in Covalent Organic Frameworks for Xe/Kr Capture and Separation

As a rising star among porous solid materials, covalent organic frameworks(COFs) with excellent properties including but not limit to facilely controllable structure, high porosity, and multi-chemical functionality represent significant potential for efficient ¹²⁷Xe/⁸⁵Kr capture and separation. In this study, through tuning the length of the organic ligands, two-dimensional(2D) COF mate-rials with identical connection group but different pore properties, denoted as ATFG-COF and TpPa-COF with AA-stacking model and TpBD-COF with AB-stacking model were synthesized and tested for Kr and Xe adsorption for the first time. Adsorption measurements indicate that the narrower pore apertures and higher porosity are conducive for COF materials to capture Xe and Kr. Furthermore, the Henry's constant, isosteric heat of adsorption(Q_st), and ideal adsorbed solution theory(IAST) selectivity of ATFG-COF, the pore size of which is closest to the kinetic diameter of the Xe atom(0.41 nm) among 2D COF materials, were carried out based on the single component sorption isotherms. The results illustrate that the high isosteric heat values of Xe/Kr adsorption on ATFG-COF are 25 and 16 kJ/mol at room temperature, respectively. Henry's law predicts that the selectivity factor of Xe to Kr is 6.07, consistent with the adsorption selectivity(ca. 6) calculated based on the IAST.     

Genetically engineered materials: Proteins and beyond

Information-rich molecules provide opportunities for evolution. Genetically engineered materials are superior in that their properties are coded within genetic sequences and could be fine-tuned. In this review, we elaborate the concept of genetically engineered materials(GEMs) using examples ranging from engineered protein materials to engineered living materials. Proteinbased materials are the materials of choice by nature. Recent progress in protein engineering has led to opportunities to tune...     

五种郁金无机元素比较及样品元素间相关性分析

中药含有丰富的无机元素,特别是人体必需的无机元素含量较高。在样品中加入混酸,再用微波消解技术消化样品,采用火焰原子吸收光谱法对五种不同品种郁金（温郁金、姜黄、广西莪术、蓬莪术和川郁金）所含有的八种具有重要生理功能的无机元素——钾、钙、钠、镁、锌、铁、铜和锰的含量进行了测定。结果显示郁金类中药材含有相近的元素成分,不同来源的品种在个别元素含量上存在差异。基于各样品所含无机元素采用聚类分析进行相关性分析,药典所收载四种郁金（温郁金、姜黄、广西莪术和蓬莪术）聚为一类,与非正品川郁金有所区别,该聚类结果与依据形态特征的传统分类学结果一致。测定结果可为郁金中无机元素与其药效的相关性提供科学依据。     

蔬菜中硒总量及形态的氢化物发生-原子荧光光谱测定方法

采用氢化物发生原子荧光光谱法(HG-AFS)测定了几种蔬菜中的总硒含量,并测定了其中无机硒和有机硒的相对含量。实验优化了仪器测量参数,考察了载流盐酸和硼氢化钾浓度、样品介质盐酸浓度和残留硝酸浓度及干扰元素对测定的影响。结果表明：几种蔬菜中硒的含量都较低,而草菇的硒含量达到0.151 μg·g-1。此外,蔬菜中硒主要以有机硒的形态存在。方法的检出限为0.35 ng·g-1,加标回收率为97.6%～101%。实验方法操作简便、灵敏度高,结果令人满意。     

An Improvement to Conformer-Based Model for High-Accuracy Speech Feature Extraction and Learning

Owing to the loss of effective information and incomplete feature extraction caused by the convolution and pooling operations in a convolution subsampling network, the accuracy and speed of current speech processing architectures based on the conformer model are influenced because the shallow features of speech signals are not completely extracted. To solve these problems, in this study, we researched a method that used a capsule network to improve the accuracy of feature extraction in a conformer-based model, and then, we proposed a new end-to-end model architecture for speech recognition. First, to improve the accuracy of speech feature extraction, a capsule network with a dynamic routing mechanism was introduced into the conformer model; thus, the structural information in speech was preserved, and it was input to the conformer blocks via sequestered vectors; the learning ability of the conformed-based model was significantly enhanced using dynamic weight updating. Second, a residual network was added to the capsule blocks, thus, the mapping ability of our model was improved and the training difficulty was reduced. Furthermore, the bi-transformer model was adopted in the decoding network to promote the consistency of the hypotheses in different directions through bidirectional modeling. Finally, the effectiveness and robustness of the proposed model were verified against different types of recognition models by performing multiple sets of experiments. The experimental results demonstrated that our speech recognition model achieved a lower word error rate without a language model because of the higher accuracy of speech feature extraction and learning using our model architecture with a capsule network. Furthermore, our model architecture benefited from the advantage of the capsule network and the conformer encoder, and also has potential for other speech-related applications.     

Cloning,Expression, Purification,and Characterization of β-Galactosidase from Bifidobacterium longum and Bifidobacterium pseudocatenulatum

Expression and purification of β-galactosidases derived from Bifidobacterium provide a new resource for efficient lactose hydrolysis and lactose intolerance alleviation. Here, we cloned and expressed two β-galactosidases derived from Bifidobacterium. The optimal pH for BLGLB1 was 5.5, and the optimal temperature was 45 °C, at which the enzyme activity of BLGLB1 was higher than that of commercial enzyme E (300 ± 3.6 U/mg) under its optimal conditions, reaching 2200 ± 15 U/mg. The optimal pH and temperature for BPGLB1 were 6.0 and 45 °C, respectively, and the enzyme activity (0.58 ± 0.03 U/mg) under optimum conditions was significantly lower than that of BLGLB1. The structures of the two β-galactosidase were similar, with all known key sites conserved. When o-nitrophenyl-β-D-galactoside (oNPG) was used as an enzyme reaction substrate, the maximum reaction velocity (V_max) for BLGLB1 and BPGLB1 was 3700 ± 100 U/mg and 1.1 ± 0.1 U/mg, respectively. The kinetic constant (K_m) of BLGLB1 and BPGLB1 was 1.9 ± 0.1 and 1.3 ± 0.3 mmol/L, respectively. The respective catalytic constant (k_cat) of BLGLB1 and BPGLB1 was 1700 ± 40 s⁻¹ and 0.5 ± 0.02 s⁻¹, respectively; the respective k_cat/K_m value of BLGLB1 and BPGLB1 was 870 L/(mmol∙s) and 0.36 L/(mmol∙s), respectively. The K_m, k_cat and V_max values of BLGLB1 were superior to those of earlier reported β-galactosidase derived from Bifidobacterium. Overall, BLGLB1 has potential application in the food industry.     

四氨羰甲基对-叔丁基杯[4]芳烃的合成及聚集行为

对-叔丁基杯[4]芳烃与溴乙酸乙酯反应,得到锥式、1,2-交替、1,3-交替和部分锥式构象的四取代衍生物;将锥式、1,2-交替和1,3-交替构象的四乙酸乙酯取代衍生物在氨的甲醇溶液中进行氨解得到相应的酰胺产物.通过核磁研究比较了氨解产物的自聚集情况.锥式构象四氨羰甲基对-叔丁基杯[4]芳烃分子在氯仿溶剂中的分子间作用力很弱,容易溶剂化,溶剂中核磁谱图不能观察到分子聚集;而1,2-交替构象的四氨羰甲基对-叔丁基杯[4]芳烃分子间存在相当强的作用力,在氯仿中的溶解性较差,核磁谱图表明分子在氯仿溶剂中有分子聚集的现象.1,3-交替构象的四氨羰甲基对-叔丁基杯[4]芳烃分子可以形成一定的聚集体,在高浓度时是聚集体的形式;在浓度较低或者加入酸予以破坏时,溶液中是以单体的形式存在.     

电子束蒸发制备ZnO薄膜及其晶体结构和电学性质

采用电子束蒸发法制备了ZnO薄膜。研究了退火温度及衬底对薄膜的结晶状况以及电学性质的影响．X射线衍射测试结果表明。相同蒸发条件下制备的ZnO薄膜，硅(100)衬底上薄膜晶粒尺寸为73nm。结晶情况明显好于陶瓷和玻璃衬底上的薄膜．退火之后，各种衬底上薄膜的结晶情况相对未退火时都有明显好转；400℃退火时。薄膜逐渐结晶；600℃退火时，ZnO薄膜体现良好的择优生长的趋势，晶粒长大，晶化程度提高，大部分晶粒发生了织构．在400℃退火后。掺入Al2O3的薄膜和未掺杂的ZnO薄膜的电阻率下降了一个多数量级，但掺入MgO的薄膜电阻率变大。这是由于MgO掺杂起到了补偿作用，掺入MgO有可能实现ZnO薄膜的P型掺杂．     

基于函数调用图的静态数据分配

在许多嵌入式设计中,数据存储器是稀缺资源.如何基于静态分配方案,用最少的内存空间来存储程序数据成为嵌入式设计中一个非常重要的问题.如果两个函数之间不存在直接或者间接的调用关系的话,这两个函数的局部变量的生命期就没有重叠.这样的两个函数的局部变量可以共用存储空间而不会影响程序的正确性.基于这个思路,本文提出一种基于函数调用图的拓扑排序的最优静态分配算法(TBA)并证明了该分配算法的最优性.该分配算法通过静态分析技术,构建函数调用图(FCG),基于FCG的拓扑排序,计算每个函数的私有数据段的全局地址以及段内每个符号的全局地址,并根据重定位表更新对这些符号的引用.实验结果表明该算法优于前人提出的启发式算法.     

关联规则增量式更新的改进算法

关联规则是数据挖掘中的重要研究内容之一，对在事务数据库不变，只对最小支持度和最小可信度进行改变的情况下，针对关联规则的维护问题，设计了一个增量式更新的改进算法AIUA。