人类免疫缺陷病毒整合酶二酮酸类抑制剂的三维药效团构建

应用遗传算法相似性程序(GASP), 以作用于I型人类免疫缺陷病毒(human immun-odeficiency virus type 1, HIV-1)整合酶(IN)的二酮酸类(diketoacids, DKAs)抑制剂构建药效团模型. 所选训练集分子均具有可靠的类药性特征及DKAs药效团特征. 尝试将抑制剂与药效团叠合后的构象和抑制剂与IN的对接构象进行叠合, 得到药效团模型与分子对接构象中IN残基的相对位置, 并基于抑制剂的药效团模型特征与周围IN氨基酸残基位置的匹配情况进行药效团特征的修改. 所得最优药效团由1个疏水特征、3对氢键特征和1个氢键供体特征组成. 该药效团的命中物质量(goodness of hit, GH)为0.56, 产出率(Y)达63.6%, 假阳性率(FP)为0.41%. 该药效团具有较好的置信度, 产出率较高而假阳性率较低, 可用于数据库搜索发现新的具有DKAs药效团特征的活性化合物, 也可为先导化合物的改造提供帮助.     

LiCl-Li2B4O7-H2O体系在298.15 K下热力学性质的等压研究

用等压法研究了298.15 K下LiCl-Li2B4O7-H2O体系在不同LiB4O7质量摩尔浓度时的等压平衡浓度,  水活度; 计算了LiCl和Li2B4O7混合盐溶液的渗透系数等热力学性质. 用298.15 K下的实验数据对Pitzer离子相互作用模型进行了参数化研究, 拟合求取了298.15 K下Pitzer离子相互作用参数, 用获得的参数计算了LiCl和Li2B4O7在LiCl-Li2B4O7-H2O体系中的活度系数. Pitzer模型计算的渗透系数值与实验结果一致.     

As(Ⅲ)和As(Ⅴ)与牛血清白蛋白的结合平衡研究

在模拟动物体生理条件下,研究As(Ⅲ)和As(V)与牛血清白蛋白(BSA)的相互作用.用氢化物发生-超低温捕集-原子吸收分光光度法测定平衡透析后As(Ⅲ)或As(V)的浓度,用Scatchard方法分别处理实验数据,确定结合部位和结合常数.发现当As(Ⅲ)浓度(cAs(Ⅲ)∶cBSA≤1∶1)较低时,在BSA中有1.3个强结合部位,结合常数为1.7×106,为强结合;当As(Ⅲ)的浓度(cAs(Ⅲ)∶cBSA≥2∶1)较高时,没有明显的特征结合点,表现为弱结合.而As(V)与BSA无任何结合作用.     

基于激光诱导击穿光谱(LIBS)的煤灰熔点快速检测

炉内结渣是影响火电机组和气化工艺可靠运行的关键因素之一,准确预测灰熔点可以提前调整炉膛出口温度以避免结渣。本论文采用激光诱导击穿光谱（LIBS）采集煤灰样中金属元素的光谱,分别建立煤灰中的金属元素的谱线强度与煤灰熔点的随机森林模型、支持向量机回归模型和线性回归模型,直接预测煤灰熔点温度。采用基于马氏距离(MD)的异常数据剔除算法和基于稀疏矩阵的基线估计与降噪算法（BEADS）,对粉煤灰样的全光谱数据进行了预处理。随机森林模型对粉煤灰熔点的预测平均相对误差（MRE）为54.74%,支持向量机回归模型的预测平均相对误差为60.08%,而线性回归模型的预测平均相对误差达到了9.78%。研究结果表明,线性回归模型对煤灰熔点的预测结果更准确。     

CFD-PBM approach for the gas-liquid flow in a nanobubble generator with honeycomb structure

In recent years, nanobubble technologies have drawn great attention due to their wide applications in many fields of science and technology. From previous studies, a kind of honeycomb structure for high efficiency nanobubble generation has been proposed. In this paper, the numerical simulations of bubbly flow in the honeycomb structure were performed by using a computational fluid dynamics–population balance model (CFD-PBM) coupled model. The numerical model was based on the Eulerian multiphase model and the population balance model (PBM) was used to calculate the bubble size distribution. The bubble size distributions in the honeycomb structure under different work conditions were predicted. Two different drag force models (Schiller-Naumann model and Tomiyama model) and two different aggregation models (Luo model and turbulent aggregation model) were investigated. Both two drag models gave similar prediction of bubble number density distribution at the outlet. The results obtained from Luo model had better reflection of the trend of number density distribution. The turbulence dissipation rate ε can be used to evaluate the nanobubble generating ability. The water tank was not included in the CFD model in this work. The bubbles in the water tank should be studied in the future.     

基于傅里叶变换红外光声光谱的包膜控释肥料聚合物膜疏水性的表征

以不饱和有机硅改性的丙烯酸酯为基材,在不同条件下制备了30种水基聚合物包膜控释肥料模型膜,测定了模型膜的溶胀度及模型膜的傅里叶变换红外光声光谱.分别以红外光声光谱和溶胀度为自变量和因变量进行简单相关分析,同时也构建了偏最小二乘模型,并利用该模型对模型膜的疏水性进行预测.结果表明,聚合物模型膜具有明显不同的疏水性,其红外光声光谱具有相似的吸收特征,但不同吸收带的相对强度发生明显改变;常用的简单相关分析无法实现模型膜疏水性的预测,表明模型膜的疏水性与谱区更多的特征峰相关;基于偏最小二乘法的多元校正分析具有很好的预测能力,模型的校正系数(R2)为0.9864,校正标准误差为0.70%,验证标准误差为1.92%.此模型可用于模型膜疏水性的预测.本方法样品用量少,测定快速,操作简便且可实现原位测定,为控释肥料包膜材料的研制提供新的手段.     

Use of 13C NMR spectrometric data to produce a predictive model of estrogen receptor binding activity

We have developed a spectroscopic data-activity relationship (SDAR) model based on 13C NMR spectral data for 30 estrogenic chemicals whose relative binding affinities (RBA) are available for the alpha (ERalpha) and beta (ERbeta) estrogen receptors. The SDAR models segregated the 30 compounds into strong and medium binding affinities. The SDAR model gave a leave-one-out (LOO) cross-validation of 90%. Two compounds that were classified incorrectly in the SDAR model were in the transition zone between classifications. Real and predicted 13C NMR chemical shifts were used with test compounds to evaluate the predictive behavior of the SDAR model. The 13C NMR SDAR model using predicted 13C NMR data for the test compounds provides a rapid, reliable, and simple way to screen whether a compound binds to the estrogen receptors.     

pePC-SAFT: Modeling of polyelectrolyte systems 2. Aqueous two-phase systems

This work considers aqueous two-phase systems (ATPS) containing one polymer-polyelectrolyte as well as one salt. To model the liquid-liquid equilibria (LLE) of these systems, the recently presented model pePC-SAFT has been employed. ATPS containing poly(acrylic acid) of different degrees of neutralization or poly(vinyl pyrrolidone), respectively, were considered. The binary interaction parameters used between water-poly(acrylic acid) and water-poly(vinyl pyrrolidone) were adjusted to vapor-liquid equilibrium (VLE) data of these systems. ATPS consisting of poly(vinyl pyrrolidone)-water-sodium sulfate were predicted as function of temperature as well as of molar mass of the polymer. For poly(acrylic acid) systems, ATPS were predicted as function of charge density (degree of neutralization) for different types of salt. For these calculations, the polyelectrolyte model parameters were determined from the non-charged polymer whereas the effect of increasing charge density has been purely predicted by the model. Using this approach, it is possible to predict the shrinking of the liquid-liquid equilibrium region with increasing charging of the polyelectrolyte.     

A new approach for modeling of RO membranes using MD-SF-PF model and CFD technique

The modified surface force-pore flow (MD-SF-PF) model is a successful and powerful model for prediction of reverse osmosis (RO) membrane separation. In the MD-SF-PF model, a suitable mass and momentum balances were made through the cylindrical micro porous of the membranes. A one-dimensional potential function in radial direction was used to attain the velocity profile. The obtained nonlinear equations were solved by orthogonal collocation method. In our model, extended MD-SF-PF (Ex-MD-SF-PF) model, the potential function of the MD-SF-PF model was developed as both radial and axial directions. Both Ex-MD-SF-PF and MD-SF-PF models were solved by finite volume and finite difference methods based on a computational fluid dynamics (CFD) technique. The profiles of velocity and concentration were achieved and thereby the values of separation factors and fluxes for both models were obtained and were compared accordingly. This approach affected only at the end of pore, mainly on the concentration profile and slightly on the velocity profile.     

取代嘧啶衍生物抗前列腺癌活性的3D-QSAR研究

运用比较分子力场分析(CoMFA)方法,建立18种取代嘧啶衍生物抗前列腺癌活性(pM)的三维定量构效关系。训练集中15个化合物用于建立预测模型,测试集13个化合物(含10号模板分子和新设计的9个分子)作为模型验证。建立的CoMFA模型的交叉验证系数(R_ev²)、非交叉验证系数(R²)分别为0.344、0.935,说明所建模型具有较强的鲁棒性和良好的预测能力。该模型中立体场、静电场贡献率依次为71.6%、28.6%。影响取代嘧啶衍生物抗前列腺癌活性的主要因素是取代基的疏水作用和空间位阻,其次是取代基的库仑力、氢键及配位作用。基于此研究结果,设计了9个新化合物,其抗前列腺癌活性有待医学实验验证。     

Theoretical Study on a 42 T Model of Beta Zeolite by Density Functional Theory Simulation

Density functional theory was applied to study the structure of Beta zeolite. A model cluster containing 41Si atoms, 1 Al atom, 70 O atoms and 29 H atoms was constructed. The model structures were optimized using the Becke＇s three-parameter hybrid method with the Lee-Yang-Parr correlation functional （B3LYP） and the 6-31G basis set applying the Gaussian03 program package. The NMR parameters were calculated to validate the rationality of the model. It was found that in the optimization models, all O-H bond lengths were in range of 0.984-0.985A^°, among which the model with O-H bond length of 0.98478A^° was more stable than the others. The ^1H and ^27Al chemical shifts of the most stable model were 4.03434 and 55.74 ppm, which were pretty consistent with Larry＇ s experimental data of 4.1 and 54 ppm. The relationship between other structure parameters and total relative electric energy has also been found. All the results exhibit that the 42 T （the total number of Si and Al atoms is 42） model has common properties of the standard of zeolite Beta.     

咪唑啉衍生物缓蚀剂的定量构效关系及分子设计

采用量子化学密度泛函理论(DFT)及线性回归分析方法, 对十一烷基咪唑啉衍生物缓蚀剂抗H2S、CO2腐蚀性能进行了定量构效关系(QSAR)研究. 通过回归分析, 筛选出了影响缓蚀剂缓蚀性能的主要因素, 建立了QSAR模型, 并使用留一法交叉验证对模型的稳定性及预测能力进行了分析. 结果表明, 电子转移参数△N、咪唑环上非氢原子静电荷之和∑Qring及分子极化率α对咪唑啉类缓蚀剂的缓蚀性能有很大的贡献, 所得模型的拟合相关系数(R2)和交叉验证相关系数(q2)分别为0.924 和0.917, 模型对此类缓蚀剂抗H2S、CO2腐蚀性能具有较好的预测效果. 应用QSAR研究结果进行了分子设计, 在理论上提出了一些具有较高抗H2S、CO2腐蚀性能的新型咪唑啉衍生物, 为实验工作者合成新型缓蚀剂提供理论参考.     

基于支持向量机的药物诱导磷脂质病预测模型

本文应用一种组合遗传算法和共轭梯度法的支持向量机(GA-CG-SVM)方法建立了药物诱导磷脂质病分类预测模型.首先对描述符进行了优化,选出了19个描述符用于模型的构建,所建模型对训练集的预测准确率为81.6%,对测试集的预测精度为87.5%,说明所建SVM分类模型不仅能正确预测训练集药物诱导的磷脂质病,也对其他化合物具...     

Spectroscopic ellipsometry of SU‐8 photoresist from 190 to 1680 nm (0.740–6.50 eV)

SU‐8 is an important, epoxy‐based, negative photoresist that can create high aspect ratio features. Spectroscopic ellipsometry (SE) is a nondestructive analytical technique that can be performed in the open air. In this study, reflection and transmission SE measurement data were combined to model the optical function of SU‐8 photoresist. The data were fit using three different models: (i) a B‐spline model, (ii) a four‐Gaussian oscillator model with an ultraviolet (UV) and an infrared (IR) pole, and (iii) a Cody–Lorentz model with three additional Gaussian oscillators. All three models successfully fit the data, where the B‐spline model showed the lowest mean squared error. In situ SE data were also collected and fitted to follow possible changes in the optical properties of the SU‐8 during its development. Time‐dependent density functional theory (TD‐DFT) modeling of a complete SU‐8 monomer is qualitatively and quantitatively consistent with the measured optical function.     

多氯萘的气相色谱保留指数与其结构参数的定量关系

在B3LYP/6-31G*水平上计算了76个多氯萘分子,将计算得到的结构参数和热力学参数作为理论描述符引入到与气相色谱保留指数(RI)相关的多元回归分析中,建立了拟合度高、物理意义明确、预测能力强的保留时间-结构参数的相关方程(模型Ⅰ)(r2=0.9957);再以氯原子的取代个数和相互位置作为理论描述符,得出另一模型(模型Ⅱ)(r2=0.9967)。找出了影响多氯萘保留时间的主要因素。     

(Liquid + liquid) equilibria of binary polymer solutions using a free-volume UNIQUAC-NRF model

In this work, a modified free-volume (FV) model based on the UNIQUAC-Nonrandom factor (UNIQUAC-NRF) model developed by Haghtalab and Asadollahi was proposed. While the combinatorial part of the proposed model for activity coefficient takes the same form as that of the entropic free-volume (entropic-FV) model, the residual part is similar to that of the UNIQUAC-NRF model. The proposed model, i.e., the FV-UNIQUAC-NRF model overcomes the main shortcoming of the original UNIQUAC-NRF model in predicting the lower critical solution temperature (LCST) for polymer solutions. The appearance of the LCST is believed to be attributed to the existence of the free volume differences between polymer and solvent molecules. Thus, the models without considering such differences fail to predict the LCST behavior of polymer solutions. The proposed model was applied to correlate the experimental data of (liquid + liquid) equilibria (LLE) for a number of binary polymer solutions at various temperatures. The values for the binary characteristic energy parameters for the proposed model and the FV-UNIQUAC model along with their average relative deviations from the experimental data were reported. It should be stated that the binary polymer solutions studied in this work were considered as monodisperse. The results obtained from the FV-UNIQUAC-NRF model were compared with those obtained from the FV-UNIQUAC model. The results of the proposed model show that the FV-UNIQUAC-NRF model can accurately correlate the experimental data for LLE of polymer solutions studied in this work. Also the error produced from the FV-UNIQUAC-NRF model show the slightly better accuracy in comparison with that from the FV-UNIQUAC model. The clear advantage of the proposed model, contrary to the original UNIQUAC-NRF model, is its capability in predicting the LCST for binary polymer solutions.     

Model Reduction in Emulsion Polymerization Using Hybrid First‐Principles/Artificial Neural Network Models

A first‐principles mathematical model for emulsion polymerization was reduced by using a hybrid mathematical model composed by artificial neural networks (ANN) and material balances. The goal was to have an accurate model that may be integrated fast enough to be used for online optimization purposes. In the reduced model the polymerization rate and the instantaneous weight‐average molecular weight were calculated by means of artificial neural networks. These ANNs were incorporated to first‐principles material balances. The accuracy of the reduced model under a wide range of conditions was assessed. Savings in computer time were achieved by using the reduced model, which makes it suitable for online optimization purposes.

Effect of the temperature on the cumulative weight‐average molecular weight: first principles mathematical model (—); (ANN2) and hybrid model predictions: (▵) 50 °C, (▪) 60 °C^(training), (▿) 70 °C^(validation), (•) 80 °C, (○) 90 °C.