葡萄糖基-β-环糊精作为手性选择剂对苯磺酸氨氯地平的毛细管电泳手性拆分

以葡萄糖基-β-环糊精(Glu-β-CD)为手性选择剂,用毛细管区带电泳法对手性药物苯磺酸氨氯地平进行了拆分研究.考察了缓冲液的pH值、缓冲液浓度、缓冲液体系组成、Glu-β-CD的浓度及电压等对分离的影响,并对3批市售左旋苯磺酸氨氯地平片(施慧达)进行光学纯度检查.结果表明,在背景电解质为含20 mmol/L Glu-β-CD的200 mmol/L乙酸-三乙醇胺(HAc-TEA)(pH 4.0)体系,电压25 kV,温度20 ℃,检测波长214 nm的条件下,苯磺酸氨氯地平可以得到良好分离,分离度为4.0.     

近红外光谱模型检测苯磺酸氨氯地平片中有效含量的研究

以12种苯磺酸氨氯地平片为研究对象,以高效液相色谱(HPLC)法准确测定出苯磺酸氨氯地平片中苯磺酸氨氯地平的有效含量为标准,利用近红外光谱技术,建立了苯磺酸氨氯地平片的近红外定量分析模型。结果表明,利用二阶导数谱图,在4070.89～4248.69cm~(-1)、4316.83～4656.56cm~(-1)、4749.74～5449.81cm~(-1)、7315.64～6942.53cm~(-1)等四个波段下,得到的样品粉末定量分析模型最优。模型的预测值与HPLC测定值的绝对误差为-0.12%～0.18%,相对误差为-3.51%～5.41%。建立了一种快速检测苯磺酸氨氯地平片含量的方法。     

光致变色二芳烯掩膜及在近场光存储中的应用

合成了在超分辨率近场结构(Super-RENS)存储方式中适合做掩膜层和记录层材料的两种全氟二芳烯,旋涂法制备了实验存储盘片,并进行了近场存储实验.初步的实验表明,光致变色二芳烯掩膜层可以缩小聚焦激光的直径,得到小尺寸记录信息.掩膜层中二芳烯的浓度,对近场存储结果影响很大,存在一个最佳浓度使记录光斑的尺寸减少到最小.     

苯磺酸氨氯地平对映体在Chiralcel OJ柱上的色谱保留及拆分特性

在Chiralcel OJ手性柱上对苯磺酸氨氯地平对映体进行分离研究.考察了在正己烷流动相体系中,不同碳数、不同立体结构的醇类添加剂以及不同醇含量对分离结果的影响,同时还考察了流速及柱温对分离的影响.并通过色谱保留及热力学行为研究对药物手性识别机理进行了探讨.结果表明,在正己烷-异丙醇(90:10,体积比)为流动相,流...     

分子印迹-基质固相分散-高效液相色谱法测定血液中苯磺酸氨氯地平

采用分子印迹-基质固相分散-高效液相色谱法(MI-MSPD-HPLC)测定血液中痕量苯磺酸氨氯地平。制备苯磺酸氨氯地平分子印迹聚合物,以此为基质固相分散剂与样品混合均匀,风干后装柱,用甲醇洗脱苯磺酸氨氯地平。洗脱液在Inertsil DOS-SP分离柱上分离,以甲醇-乙腈-水(70+22+8)混合液为流动相进行洗脱,检测波长为237nm。氨氯地平的线性范围为1.7~20.0μg·L-1,检出限(3S/N)为0.5μg·L-1,测定下限(10S/N)为1.7μg·L-1。对空白血液样品进行加标回收试验,回收率在91.0%~109%之间,测定值的相对标准偏差(n=5)在3.7%~4.8%之间。方法可用于测定血液中的苯磺酸氨氯地平。     

平板双分子层脂膜的制备及锌离子跨该膜的输运过程

制备了氧化胆固醇 卵磷脂(脑磷脂)平板双分子层脂膜,研究了膜配方对双分子层脂膜的稳定性和离子通透性的影响,得到了最佳制膜工艺,建立了锌离子跨卵 (脑 )磷脂膜的吸附 -扩散模型,其计算值与实验值基本吻合.     

毛细管电泳拆分苯磺酸氨氯地平及机理的探讨

采用羟丙基-β-环糊精(HP-β—CD)作为手性选择试剂对外消旋的苯磺酸氨氯地平进行了拆分,研究了环糊精种类,浓度,缓冲液的pH值以及添加剂对分离的影响,结果表明以羟丙基-β-环糊精为手性选择剂,短链的阳离子表面活性剂四乙基氯化铵为电渗流改性剂可以使苯磺酸氨氯地平实现基线分离,对拆分的机理进行了探讨。     

双极界面聚合物膜燃料电池Ⅱ:阴极催化层结构优化

以薄层亲水电极或者厚层憎水电极作为双极燃料电池(BPFC)阴极,系统考察了薄层亲水阴极中季铵化聚砜(QAPSF)含量、厚层憎水电极中聚四氟乙烯(PTFE)含量对电池性能的影响.结果表明,采用薄层亲水阴极时,催化层中QAPSF的最佳含量是20 wt%,室温下BPFC的最大输出功率达到186.1 m W/cm2.采用厚层憎水电极时,催化层中PTFE的合适含量是20 wt%,40℃时BPFC的最大输出功率达到461.5 m W/cm2.由于碱性阴极对排水的需求较高,厚层憎水电极相较于薄层亲水电极在BPFC中更有优势.     

表面水热碳层对磁性NiFe2O4八面体光催化活性的影响

采用葡萄糖水热碳化法合成了一系列碳层包覆的NiFe₂O₄核壳八面体(NiFe₂O₄@C). 通过调控葡萄糖的含量可以有效控制NiFe₂O₄表面包覆的碳层厚度. 利用X射线衍射(XRD)、 拉曼光谱(Roman)、 X射线光电子能谱(XPS)、 扫描电子显微镜(SEM)、 透射电子显微镜(TEM)和紫外-可见漫反射光谱(UV-Vis DRS)等对NiFe₂O₄@C的组成、 结构、 形貌和光学性能进行了表征. 考察了表面水热碳层对NiFe₂O₄光催化降解亚甲基蓝(MB)性能的影响. 结果表明, NiFe₂O₄的光催化活性很大程度上依赖于在其表面包覆的碳层厚度, 碳层厚度为5.5 nm的NiFe₂O₄@C-3展现了最佳的光催化活性. 荧光光谱(PL)、 瞬态光电流和电化学阻抗谱(EIS)表征结果证明, NiFe₂O₄@C的光催化性能的提升归因于在NiFe₂O₄核和碳壳之间形成了异质结, 有效地促进了光生载流子的传输和分离效率. NiFe₂O₄@C复合材料展现了较好的稳定性和可回收性, 在污水处理方面有很大的应用潜力.     

K_2CO_3活化糖蜜基多孔炭的制备及双电层储能性能

以生物质糖蜜为原料,K_2CO_3为活化剂制备了糖蜜基多孔炭.K_2CO_3的使用改善了传统活化剂KOH对设备腐蚀的问题,避免了传统活化剂ZnCl_2可能引发的致毒性.分析了活化条件对产率的影响.采用扫描电子显微镜(SEM)、X射线衍射(XRD)、N_2吸附-脱附分析(BET)和傅里叶变换红外光谱(FTIR)表征了糖蜜基多孔炭,结果表明其为石墨化层堆结构,表面富含羟基、羧基、酯基或醚基等官能团,具有丰富的孔结构,比表面积可达1219 m~2/g,并证实了800℃为最佳的活化温度.电化学测试结果表明,糖蜜基多孔炭具有优良的双电层储能性能.     

Determination of amlodipine besylate by adsorptive square-wave anodic stripping voltammetry on glassy carbon electrode in tablets and biological fluids

The adsorptive and electrochemical behavior of amlodipine besylate on a glassy carbon electrode were explored in Britton-Robinson buffer solution by using cyclic and square-wave voltammetry. Cyclic voltammetric studies indicated the oxidation of amlodipine besylate at the electrode surface through a single two-electron irreversible step and fundamentally controlled by adsorption. The solution conditions and instrumental parameters were optimized for the determination of the authentic drug by adsorptive square-wave stripping voltammetry. Amlodipine besylate gave a sensitive adsorptive oxidation peak at 0.510 V (versus Ag/AgCl). The oxidation peak was used to determine amlodipine besylate in range 4.0×10⁻⁸ to 2.0×10⁻⁶ with a detection limit of 1.4×10⁻⁸ M. The procedure was successfully applied for the assay of amlodipine besylate in tablets (Norvasc)^®. The percentage recoveries were in agreement with those obtained by the reference method. Applicability to assay the drug in urine and serum samples was illustrated. The mean percentage recoveries were 96.31±1.18 and 96.98±1.17, respectively. The proposd method used for monotoring clinically relevant concntrations of drug in human urine and serum.     

近红外光谱无损定量分析吡嗪酰胺片

近红外光谱;径向基神经网络;吡嗪酰胺;定量分析     

Development of a rapid process monitoring method for dry-coated tableting process by using near-infrared spectroscopy

A nondestructive transmittance near-infrared (NIR) method for detecting off-centered cores in dry-coated (DC) tablets was developed as a monitoring system in the DC tableting process. Caffeine anhydrate was used as a core active pharmaceutical ingredient (API), and DC tablets were made by the direct compression method. NIR spectra were obtained from these intact DC tablets using the transmittance method. The reference assay was performed with HPLC. Calibration models were generated by partial least squares (PLS) regression and principal component regression (PCR) utilizing external validations. Hierarchical cluster analysis (HCA) of the results confirmed that NIR spectroscopy correctly detected off-centered cores in DC tablets. We formulated and used the Centering Index (CI) to evaluate the precision of core alignment and generated an NIR calibration model that could correctly predict this index. The principal component (PC) 1 loading vector of the final calibration model indicated that it could specifically detect the misalignment of tablet cores. The model also had good linearity and accuracy. The CIs of unknown sample tablets predicted by the final calibration model and those calculated through the HPLC analysis were closely parallel with each other. These results demonstrate the validity of the final calibration model and the utility of the transmittance NIR spectroscopic method developed in this study as a monitoring system in DC tableting process.     

Radial basis function neural networks in non-destructive determination of compound aspirin tablets on NIR spectroscopy

The application of the second most popular artificial neural networks (ANNs), namely, the radial basis function (RBF) networks, has been developed for quantitative analysis of drugs during the last decade. In this paper, the two components (aspirin and phenacetin) were simultaneously determined in compound aspirin tablets by using near-infrared (NIR) spectroscopy and RBF networks. The total database was randomly divided into a training set (50) and a testing set (17). Different preprocessing methods (standard normal variate (SNV), multiplicative scatter correction (MSC), first-derivative and second-derivative) were applied to two sets of NIR spectra of compound aspirin tablets with different concentrations of two active components and compared each other. After that, the performance of RBF learning algorithm adopted the nearest neighbor clustering algorithm (NNCA) and the criterion for selection used a cross-validation technique. Results show that using RBF networks to quantificationally analyze tablets is reliable, and the best RBF model was obtained by first-derivative spectra.     

Study of spatial distribution for the active ingredient in ibuprofen tablet based on near-infrared micro-imaging technology

The NIR micro-images of ibuprofen tablets were collected in this research.Compare correlation imaging and principal component analysis(PC A) with histogram were applied to acquire the spatial distribution of ibuprofen granule.The result indicated that a similar distribution trend can be acquired by both of the two methods mentioned above;the information of PC2 results from ibuprofen mainly since the correlation coefficient between PC2 loading vector and the NIR spectrum of ibuprofen is 0.9930.The result of PCA indicated that the information of PC2 results from ibuprofen mainly for both the low and the high content of ibuprofen in the tablets.The correlation coefficient between the data of the two PC2 loading vectors of the low and the high content of ibuprofen in the tablets is 0.9998,which indicates that the result of PCA is stable and reliable.     

Near-infrared determination of active substance content in intact low-dosage tablets

Near-infrared (NIR) spectroscopy can be applied to determine the active substance content of tablets. Its great advantage lies in the minimal sample preparation required, which helps to reduce the potential for error. The aim of this study is to show the feasibility of this method on low-dosage tablets. The influence of various spectral pretreatments [standard normal variate (SNV), multiplicative scatter correction (MSC), second derivative (D2), orthogonal signal correction (OSC), separately and combined] and regression methods on prediction error are compared. Partial least square (PLS) regression provided better prediction than principal component regression (PCR). SNV was applied to the first data set and SNV and a second derivative to the second set to maximise model accuracy for quantifying the active substance of intact pharmaceutical products using diffuse reflectance NIR. The models yielded standard errors of prediction (SEP) of 0.1768 and 0.0682 mg for the two products. The experiments were conducted with two low-dosage pharmaceutical forms and results of NIR predictions were comparable to currently approved methods. Diffuse reflectance NIR has the potential to become a reliable and robust quality control method for determining active tablet content.     

Application of transmission near-infrared spectroscopy to uniformity of content testing of intact steroid tablets

Transmission near-infrared (NIR) spectroscopy was used for the rapid and non-destructive determination of the content of a hormone steroid in single intact tablets. Tablets produced for clinical trial purposes containing 5, 10, 15, 20 and 30 mg (2.94, 5.88, 8.82, 11.76 and 17.64% m/m, respectively) were used to develop calibration models without the need to specially prepare any out of specification tablets. Reference values for the individual tablets used in the NIR calibration models and test set were measured by reversed-phase high performance liquid chromatography (HPLC). Partial least squares regression using standard normal variate transformed second-derivative spectra over the range 800 to 1040 nm gave the optimum calibration model with a standard error of calibration of 0.52 mg per tablet. Measurements of an independent test set gave comparable results (standard error of prediction 0.31 mg per tablet). Measurement errors for a single tablet (RSD < 2.5% for a given active level) were sufficiently small to allow the procedure to be applied to pharmacopoeial uniformity of content testing of batches of these tablets and permitted the non-destructive testing of 30 tablets in under 20 min as compared to 6 h by HPLC.     

Calibration in non-linear NIR spectroscopy using principal component artificial neural networks

Near-infrared (NIR) spectroscopy was used in simultaneous, non-destructive analysis of antipyriine and caffeine citrate tablets. Principal component artificial neural networks (PC-ANNs) were used to construct models for the analytes, using the testing set for external validation. Four pretreated spectra, namely, first-derivative, second-derivative, standard normal variate (SNV) and multiplicative scatter correction (MSC) spectra led to simplified and more robust models than conventional spectra. In PC-ANNs models, the spectra data were analyzed by principal component analysis (PCA) firstly. Then the scores of the principal compounds (PCs) were chosen as input nodes for input layer instead of the spectra data. The artificial neural networks (ANNs) models using the spectra data as input nodes were also established, which were compared with the PC-ANNs models. The result shows the SNV model of PC-ANNs multivariate calibration has the lowest training error and predicting error. The concept of the degree of approximation was introduced and performed as the selective criterion of the optimum network parameters.     

Comparison of near infrared and microwave resonance sensors for at-line moisture determination in powders and tablets

In this paper we demonstrate the feasibility of replacing KF for water content testing in bulk powders and tablets with at-line near infrared (NIR) or microwave resonance (MR) methods. Accurate NIR and MR prediction models were developed with a minimalistic approach to calibration. The NIR method can accurately predict water content in bulk powders in the range of 0.5-5% w/w. Results from this method were compared to a MR method. We demonstrated excellent agreement of both NIR and MR methods for powders vs. the reference KF method. These methods are applicable to in-process control or quality control environments. One of the aims of this study was to determine if a calibration developed for a particular product could be used to predict the water content of another product (with related composition) but containing a different active pharmaceutical ingredient (API). We demonstrated that, contrary to the NIR method, a general MR method can be used to predict water content in two different types of blends. Finally, we demonstrated that a MR method can be developed for at-line moisture determination in tablets.     

NIR analysis of pharmaceutical samples without reference data: improving the calibration

Using an appropriate set of samples to construct the calibration set is crucial with a view to ensuring accurate multivariate calibration of NIR spectroscopic data. In this work, we developed and optimized a new methodology for incorporating physical variability in pharmaceutical production based on the NIR spectrum for the process. Such a spectrum contains the spectral changes caused by each treatment applied to the component mixture during the production process. The proposed methodology involves adding a set of process spectra (viz. difference spectra between those for production tablets and a laboratory mixture of identical nominal composition) to the set of laboratory samples, which span the wanted concentration range, in order to construct a calibration set incorporating all physical changes undergone by the samples in each step of the production process. The best calibration model among those tested was selected by establishing the influence of spectral pretreatments used to obtain the process spectrum and construct the calibration models, and also by determining the multiplying factor m to be applied to the process spectra in order to ensure incorporation of all variability sources into the calibration model. The specific samples to be included in the calibration set were selected by principal component analysis (PCA). To this end, the new methodology for constructing calibration sets for determining the Active Principle Ingredients (API) and excipients was applied to Irbesartan tablets and validated by application to the API and excipients of paracetamol tablets. The proposed methodology provides simple, robust calibration models for determining the different components of a pharmaceutical formulation.