Identification of micro-scale calorimetric devices

Micro-calorimetric devices using Si-based sensors are very useful for the study of gas–solid reactions, in which very low mass of reactants are necessary. But in fact the consequence of using flat detectors is an increase of the uncertainty in the measured energy. In this work a calorimetric gas sensor based on Xensor chip is analysed studying the local x–y contributions of dissipation to the sensitivity related to the value in the centre. We study also the effects of the gas-flow on the sensitivity, comparing the results obtained with two Xensor type chips. Finally we carry out a deeper analysis of the x–y effects on the calorimetric detector for dissipations in the reactant shell extremely close to the detector surface to visualize the link between the power density distribution and the output signal.     

聚醚醚酮微结构及反应特性的量子化学研究

利用AM1对聚醚醚酮重复单元进行条件优化,得出了此分子的最优构型,明确了聚醚醚酮分子链的微观构象,并在此构象基础上进行量子化学计算,给出了健序、净电荷、前线轨道等信息,从理论上研究了聚醚醚酮的磺化反应及热分解反应的一些特性.     

Systematic optimization of micellar electrokinetic chromatographic separation of flavonoids

Summary A simple and rapid systematic optimization scheme was described for the micellar electrokinetic chromatographic separation of a group of flavonoids. The scheme employed an interpretative optimization approach to predict the optimum conditions for the separation of a group of flavonoids by micellar electrokinetic chromatography. By performing a set of nine pre-planned experiments conducted over the maximum working range for the system, global optimum separation conditions could be determined. To validate the optimization procedure, additional experiments were performed using the optimum experimental conditions derived from the optimization scheme. The results showed that satisfactory separation of all the peaks could be obtained. In addition, the application of the method in micropreparative micellar electrokinetic chromatography of the flavonoids was demonstrated.     

杂合型全局优化法优化水分子团簇结构

基于遗传算法、快速模拟退火及共轭梯度方法提出了一种快速的杂合型全局优化方法(fast hybrid global optimization algorithm, FHGOA),并将这一方法应用于TIP3P和TIPS2模型水分子团簇(H2O)n结构的优化.在进行TIP3P模型水分子团簇结构的优化过程中,发现了能量比文献值更低的团簇结构,且执行效率有较大提高.把该方法应用到优化TIPS2模型的水分子团簇,发现最优结构和采用TTM2-F模型优化的水分子团簇结构在n < 17时完全相同,为全表面结构;而在n=17、19、22时为单中心水分子笼状结构;在n=25、27时为双中心水分子笼状结构.说明随着团簇中水分子个数的增加,采用TIPS2和TTM2-F势能函数优化的团簇最优结构有相同的变化趋势.     

Simplex-optimization with a new criterion. Applications to dual-column ion chromatography

A new optimization criterion for chromatography is presented. It was designed to evaluate the quality of chromatograms in a way similar to human estimation. Selectivity, efficiency, and time of analysis as well as the number of peaks were taken into account. The criterion has been tested in comparison with expert estimations and some modern criteria. A better agreement of the new criterion with human estimation of chromatographic quality compared to some literature criteria is shown. A number of successful optimization runs has been performed using the criterion together with simplex optimization.     

A combination of modified particle swarm optimization algorithm and support vector machine for gene selection and tumor classification

In the analysis of gene expression profiles, the number of tissue samples with genes expression levels available is usually small compared with the number of genes. This can lead either to possible overfitting or even to a complete failure in analysis of microarray data. The selection of genes that are really indicative of the tissue classification concerned is becoming one of the key steps in microarray studies. In the present paper, we have combined the modified discrete particle swarm optimization (PSO) and support vector machines (SVM) for tumor classification. The modified discrete PSO is applied to select genes, while SVM is used as the classifier or the evaluator. The proposed approach is used to the microarray data of 22 normal and 40 colon tumor tissues and showed good prediction performance. It has been demonstrated that the modified PSO is a useful tool for gene selection and mining high dimension data.     

The predictive accuracy for estimating infinite dilution activity coefficients by γ∞-based UNIFAC

The predictive accuracy for estimating infinite dilution activity coefficients by a modification of the UNIFAC method wherein the group interaction parameters were based on only data (referred to as -based UNIFAC) has been studied. Estimates and measured values were compared for six prototypical solutes in a series of homologous n-alkanes, l-alcohols and alkanenitrile solvents. Despite the fact that the interaction parameters were derived using only data, this approach still gave serious errors due to several inherent problems in the original UNIFAC model. Its performance is sometimes even poorer than that of the original UNIFAC method. For example for nitromethane in alcohols and p-dioxane in nitriles values predicted by the -based UNIFAC are essentially zero. The large errors for these systems are most likely due to inaccurate interaction parameters in the -based UNIFAC method.     

Nanostructural Approach to Proton Ordering in Gas Hydrate Cages

A nanostructural approach to analysis of proton ordering in gas hydrate cages has been worked out within the framework of the topological model of strong and weak H-bonds. The approach involves rejection of the periodic boundary conditions, decomposition of the H-bond net into spherical layers, and two-dimensional drawing of the structure of spherical (spheroidal) fragments in the form of conjugate Schlegel diagrams. To analyze proton ordering in the spherical fragments composed of gas hydrate voids, we used the simulated annealing procedure and the correlation extension method proposed earlier.     

不依赖于统计模型的手性毛细管电泳分离多指标同时优化

考虑到手性毛细管电泳分离机理的复杂性，提出了不依赖于统计模型的二进制编码遗传算法多目标优化策略。根据实验参数的多少及其重要性，采用均匀设计安排完成初步实验。并用Derringer功效函数作为多指标同时优化的评价指标，以取得较高的分离度和较快分离速度；通过对实验参数给予编码，转化为二进制字符串，根据得到的总功效函数值大小进行遗传算法操作，产生新的一组实验条件。重复上述过程直到得到最佳分离条件为止。此方法成功地应用于华法令对映体的毛细管电泳分离条件优化。     

Fully quantum mechanical energy optimization for protein-ligand structure

We present a quantum mechanical approach to study protein-ligand binding structure with application to a Adipocyte lipid-binding protein complexed with Propanoic Acid. The present approach employs a recently develop molecular fractionation with a conjugate caps (MFCC) method to compute protein-ligand interaction energy and performs energy optimization using the quasi-Newton method. The MFCC method enables us to compute fully quantum mechanical ab initio protein-ligand interaction energy and its gradients that are used in energy minimization. This quantum optimization approach is applied to study the Adipocyte lipid-binding protein complexed with Propanoic Acid system, a complex system consisting of a 2057-atom protein and a 10-atom ligand. The MFCC calculation is carried out at the Hartree-Fock level with a 3-21G basis set. The quantum optimized structure of this complex is in good agreement with the experimental crystal structure. The quantum energy calculation is implemented in a parallel program that dramatically speeds up the MFCC calculation for the protein-ligand system. Similarly good agreement between MFCC optimized structure and the experimental structure is also obtained for the streptavidin-biotin complex. Due to heavy computational cost, the quantum energy minimization is carried out in a six-dimensional space that corresponds to the rigid-body protein-ligand interaction.