912 resultados para HPLC Profiling
Resumo:
The conduction-band offset Delta E-C has been determined for a molecular beam epitaxy grown GaAs/In0.2Ga0.8As single quantum-well structure, by measuring the capacitance-voltage (C - V) profiling, taking into account a correction for the interface charge density, and the capacitance transient resulting from thermal emission of carriers from the quantum well, respectively. We found that Delta E-C = 0.227 eV, corresponding to about 89% Delta E-g, from the C - V profiling; and Delta E-C = 0.229eV, corresponding to about 89.9% Delta E-g, from the deep-level transient spectroscopy (DLTS) technique. The results suggest that the conduction-band discontinuity Delta E-C obtained from the C-V profiling is in good agreement with that obtained from the DLTS technique. (C) 1998 American Institute of Physics.
Resumo:
手性是自然界的一种普遍现象,生命现象离不开手性。手性物质在有机化学、药物化学、生物化学以及功能材料等领域显示出诱人的应用前景。外消旋体的拆分是手性研究的重要基础工作。高效液相色谱手性固定相法(HPLC-CSP)在对映体化合物的分离分析和制备方面表现出独特的优势。本论文以旋光性联萘类聚合物和纤维素类衍生物涂敷的手性固定相进行分析级和半制备级色谱拆分,研究和探讨了它们对外消旋化合物的手性识别能力。1. 纤维素类聚合物的合成 将微晶纤维素与相应的酰氯或异氰酸酯反应获得四种纤维系类衍生物,纤维素三苯甲酸酯(CTB)、纤维素三苯基氨基甲酸酯(CTPC)、纤维素三(3,5-二甲基苯基氨基甲酸酯)(CDMPC)、纤维素三联萘甲酸酯(CTBPT)。红外、核磁、元素分析证明原料纤维素已酯化完全。2. 旋光性聚合物手性固定相及HPLC手性柱的制备采用聚合物涂敷硅胶方法制备了四种纤维素类、三种联萘聚酰胺手性固定相和二种半制备级纤维素手性固定相,匀浆法装柱。研究了不同的涂敷液对色谱柱效的影响。3. 旋光性联萘聚酰胺手性柱拆分能力的探讨 在三种由(S)-联萘聚酰胺涂敷的色谱柱上,以多种流动相体系对多种外消旋化合物进行手性折分试验。4. 纤维素类手性分析柱拆分能力的研究 对手性柱进行塔板数和稳定性测试及拆分能力研究。对一些外消旋化合物实现了手性拆分。由实验结果可以看出,以CDMPC涂敷的手性柱对多种外消旋化合(包括药物)具有手性拆分能力,具分离度较高。5. 半制备级拆分 对三种外消旋药物在纤维素半制备色谱柱上进行了半制备级拆分。我们在分析型色谱柱进行了流动相条件的选择,将优化后的分析条件直接放大到半制备色谱中,不仅节省了模索条件的时间,而且可以节省大量的流动相,预计一天内所能达到的对外消旋体最大拆分量可达克级以上。6. 进行了HPLC-UV-旋光仪的联用检测研究,实现了在线流动过程中旋光曲线的绘制。
Resumo:
蛋黄中含有大量磷脂,其中磷脂酰胆碱(PC)和磷脂酰乙醇胺(PE)最为丰富。本研究采用高效液相色谱法(HPLC)与基质辅助激光解吸电离飞行时间质谱(MALDI-TOF MS)联用技术分析了蛋黄中磷脂粗提物。将从蛋黄中提取的多种磷脂通过HPLC预先分离,收集各组分后分别进行MALDI-TOF MS分析得到比较清晰的质谱图。通过质谱图解析确定了蛋黄中磷脂酰胆碱、神经鞘磷脂(SM)的脂肪酸组成。
Resumo:
Reversed-phase high performance liquid chromatography (RP-HPLC) was employed to develop predictive models for fish bioconcentration factors (BCF) of organic compounds. Estimation of BCF from RP-HPLC retention parameters on octadecyl-bonded silica gel (ODS), cyanopropyl-bonded silica gel (CN), and phenyl-bonded silica gel (Ph) columns were investigated. The results show that, for a set of compounds belonging to different chemical classes, the CN stationary phase is the best one among the three columns and better than n-octanol/water model for BCF estimation. A multi-column RP-HPLC model, using the retention parameters on the CN and Ph columns as the variables of multiple linear regression equations, was further evaluated to estimate BCF of organic compounds belonging to different chemical classes, and the results show that the multi-column RP-HPLC model is better than that of any single RP-HPLC column for BCF estimation.