顶空固相微萃取气质联用分析伊犁翠雀花中挥发性成分

采用顶空固相微萃取气质联用(HS-SPME-GC-MS)法对伊犁翠雀花挥发性成分进行提取并鉴定,以色谱峰面积归一化法计算各成分的相对百分含量。结果表明,伊犁翠雀花中鉴定的挥发性成分为38个,其主要成分为(E)-2-庚烯醛(7.76%)、苯甲醛(7.62%)、正辛醇(7.16%)、3,5-辛二烯-2-酮(6.12%)、已醛(3.53%)、1,4-二甲氧基苯(3.27%)、壬醛(3.12%)、反-β-金合欢烯(2.92%)等,伊犁翠雀花的主要挥发性成分为醛类、芳香族、酮类、醇和萜类化合物。     

顶空固相微萃取–气相色谱–质谱法检测絮用纤维中有机挥发物

采用顶空固相微萃取–气相色谱–质谱联用法测定絮用纤维中甲苯、乙烯基环己烯、1,3-丁二烯等29种常见有机挥发物的含量。样品在120℃平衡20 min,然后固相微萃取30 min,在250℃的进样口温度下解吸10min。29种有机挥发物在检测范围内均有良好的线性,线性相关系数r≥0.99,检出限为0.000 5~0.016 mg/m~2。3个添加标水平下平均加标回收率为88.4%~113.4%,测量结果的相对标准偏差小于11%(n=6)。方法快速、灵敏,适用于絮用纤维中有机挥发物快速检测。     

结构随机分析的Monte Carlo加权残值法

本文提出一种结构随机分析的Monte Carlo加权残值法。文中建立了这种方法的基本列式,并通过静力挠度、固有频率和屈曲荷载等算例,表明本文方法理论简捷,计算工程量少,精度较高,是随机结构数值分析的有效方法。     

环空大挠度钻柱力学分析的动坐标迭代法及其简化应用

应用动坐标迭代法分析环空钻柱的边界待定问题与几何非线性受力变形问题。将摩擦引入平衡计算中，导出了三维分析模型。计算说明该方法和模型简便可靠，实际应用可以简化计算。     

顶空固相微萃取-气相色谱-质谱法测定茶叶中香叶醇

建立了一种应用顶空固相微萃取(HS-SPME)气相色谱质谱(GC-MS)联用分析测定茶叶中香叶醇(Geraniol)的方法。通过考察萃取头型号、茶水比、萃取温度、萃取时间、解吸附温度和解吸附时间等影响因素,确定最佳HS-SPME条件为:DVB/CAR/PDMS型号萃取头、茶水比1:6、萃取温度60℃、萃取时间60 min;气相色谱最佳解吸附条件为:进样口温度240℃、解吸附时间3 min。在优化条件下茶叶样品中的香叶醇得到较好的提取,GC-MS检测线性范围为0.08~16.50μg/g,检出限(S/N≥3)为9.42×10-3μg/g,空白基质加标回收率为89.8%~105.9%。在对24种茶样进行检测后,香叶醇含量范围为0.13~11.85μg/g,相对标准偏差为1.8%~9.7%。方法能满足茶叶样品中香叶醇分析测定的需要。     

静态顶空-气相色谱法测定养殖用水中11种氯苯类化合物

提出了静态顶空-气相色谱法测定养殖用水中11种氯苯类化合物的方法。取10mL含200g·L-1氯化钠的水样在20mL顶空瓶中于70℃振摇30min进行顶空进样的条件优化。选用DB-35MS毛细管气相色谱柱(30m×0.25mm,0.25μm)分离,电子捕获检测器检测,外标法或标准加入法定量。在优化条件下,二氯苯、三氯苯、四氯苯的线性范围分别为0.16~8μg·L-1,0.017 6~0.88μg·L-1,0.004~0.2μg·L-1,五氯苯和六氯苯的线性范围均为0.001~0.05μg·L-1。11种氯苯类化合物检出限(3S/N)为0.0002~0.04μg·L-1,应用此方法对养殖用水进行测定,回收率在86.0%~106%之间,测定值的相对标准偏差(n=5)在2.1%~5.8%之间。     

加权抽取模糊算法及其应用

本文提出了一种新型的加权抽取模糊逻辑推理模型;旨在既克服运用“ｍａｘ－ｍｉｎ”算子带来的信息丢失、封闭性、二义失效和全同失效而难于获得合理的结果的缺陷,又克服了传统加权模糊逻辑扬弃子结论之间逻辑关系的缺陷;达到了既考虑子结论的逻辑关系,又考虑了结论的相对重要程度且不丢失过多信息的目的。文中还给出了较详细的实验结果。     

从$Q_{k}(p,q)$空间到加权$\alpha-Bloch$空间的加权复合微分后置和前置算子

We study the boundedness and compactness of the weighted composition followed and proceeded by differentiation operators from Q_k（p,q）space to weighted α-Bloch space and little weighted α-Bloch space.Some necessary and sufficient conditions for the boundedness and compactness of these operators are given.     

Feasibility of similarity coefficient map for improving morphological evaluation of T2* weighted MRI for renal cancer

The purpose of this paper is to investigate the feasibility of using a similarity coefficient map(SCM) in improving the morphological evaluation of T2* weighted(T2*W) magnatic resonance imaging(MRI) for renal cancer.Simulation studies and in vivo 12-echo T2*W experiments for renal cancers were performed for this purpose.The results of the first simulation study suggest that an SCM can reveal small structures which are hard to distinguish from the background tissue in T2*W images and the corresponding T2* map.The capability of improving the morphological evaluation is likely due to the improvement in the signal-to-noise ratio(SNR) and the carrier-to-noise ratio(CNR) by using the SCM technique.Compared with T2* W images,an SCM can improve the SNR by a factor ranging from 1.87 to 2.47.Compared with T2* maps,an SCM can improve the SNR by a factor ranging from 3.85 to 33.31.Compared with T2*W images,an SCM can improve the CNR by a factor ranging from 2.09 to 2.43.Compared with T2* maps,an SCM can improve the CNR by a factor ranging from 1.94 to 8.14.For a given noise level,the improvements of the SNR and the CNR depend mainly on the original SNRs and CNRs in T2*W images,respectively.In vivo experiments confirmed the results of the first simulation study.The results of the second simulation study suggest that more echoes are used to generate the SCM,and higher SNRs and CNRs can be achieved in SCMs.In conclusion,an SCM can provide improved morphological evaluation of T2*W MR images for renal cancer by unveiling fine structures which are ambiguous or invisible in the corresponding T2*W MR images and T2* maps.Furthermore,in practical applications,for a fixed total sampling time,one should increase the number of echoes as much as possible to achieve SCMs with better SNRs and CNRs.     

Markov transition probability-based network from time series for characterizing experimental two-phase flow

We generate a directed weighted complex network by a method based on Markov transition probability to represent an experimental two-phase flow. We first systematically carry out gas-liquid two-phase flow experiments for measuring the time series of flow signals. Then we construct directed weighted complex networks from various time series in terms of a network generation method based on Markov transition probability. We find that the generated network inherits the main features of the time series in the network structure. In particular, the networks from time series with different dynamics exhibit distinct topological properties. Finally, we construct two-phase flow directed weighted networks from experimental signals and associate the dynamic behavior of gas-liquid two-phase flow with the topological statistics of the generated networks. The results suggest that the topological statistics of two-phase flow networks allow quantitative characterization of the dynamic flow behavior in the transitions among different gas-liquid flow patterns.