以天然植物为模板合成高度有序的多级复合孔材料

筛选出两种孔径较大且孔结构规则的植物为大孔模板, 以嵌段共聚物为介孔相模板成功合成了两种具有连续的骨架和贯通的大孔孔道(大孔孔径大于40 μm, 其孔壁为介孔相)的高度有序多级复合孔材料. 对该复合孔材料进行了水热稳定性研究. 用扫描电子显微镜(SEM)、粉末X射线衍射仪(XRD)、高分辨率透射电镜(HRTEM)以及N₂吸附-脱附等测试手段对合成的样品进行了表征. 结果表明, 合成的产物是孔道相互贯通的多级有序复合孔硅材料, 具有较好的水热稳定性. 采用此合成方法可精确地复制大孔植物模板.     

仿生分级结构NiO纳米线/纳米纤维的可控制备及光催化性能

采用静电纺丝技术和"自下而上"的溶液相自组装技术, 制备了具有仿生主次分级结构的三维NiO纳米线/纳米纤维. 采用扫描电子显微镜(SEM)、 X射线衍射仪(XRD)和比表面积分析仪分别对其形貌、 晶型和孔结构进行了表征. 以水体中的有色染料亚甲基蓝为模型污染物, 研究了分级结构NiO纳米线/纳米纤维的光催化性能. 结果表明, 在180 min内, 以分级结构NiO为催化剂时, 亚甲基蓝的降解率达到了97%, 分别是以纳米纤维和纳米粒子为催化剂时的1.24倍和2.16倍.     

A fast parallel clustering algorithm for molecular simulation trajectories

We implemented a GPU‐powered parallel k‐centers algorithm to perform clustering on the conformations of molecular dynamics (MD) simulations. The algorithm is up to two orders of magnitude faster than the CPU implementation. We tested our algorithm on four protein MD simulation datasets ranging from the small Alanine Dipeptide to a 370‐residue Maltose Binding Protein (MBP). It is capable of grouping 250,000 conformations of the MBP into 4000 clusters within 40 seconds. To achieve this, we effectively parallelized the code on the GPU and utilize the triangle inequality of metric spaces. Furthermore, the algorithm's running time is linear with respect to the number of cluster centers. In addition, we found the triangle inequality to be less effective in higher dimensions and provide a mathematical rationale. Finally, using Alanine Dipeptide as an example, we show a strong correlation between cluster populations resulting from the k‐centers algorithm and the underlying density. © 2012 Wiley Periodicals, Inc.     

QUANTIS: Data quality assessment tool by clustering analysis

Automatically generated kinetic networks are ideally validated against a large set of accurate, reproducible, and easy-to-model experimental data. However, although this might seem simple, it proves to be quite challenging. QUANTIS, a publicly available Python package, is specifically developed to evaluate both the precision and accuracy of experimental data and to ensure a uniform, quick processing, and storage strategy that enables automated comparison of developed kinetic models. The precision is investigated with two clustering techniques, PCA and t-SNE, whereas the accuracy is probed with checks for the conservation laws. First, the developed tool processes, evaluates, and stores experimental yield data automatically. All data belonging to a given experiment, both unprocessed and processed, are stored in the form of an HDF5 container. The demonstration of QUANTIS on three different pyrolysis cases showed that it can help in identifying and overcoming instabilities in experimental datasets, reduce mass and molar balance closure discrepancies, and, by evaluating the visualized correlation matrices, increase understanding in the underlying reaction pathways. Inclusion of all experimental data in the HDF5 file makes it possible to automate simulating the experiment with CHEMKIN. Because of the employed InChI string identifiers for molecules, it is possible to automate the comparison experiment/simulation. QUANTIS and the concepts demonstrated therein is a potentially useful tool for data quality assessment, kinetic model validation, and refinement.     

氮化硼纳米管-纳米片分级结构的制备及光学和吸附特性

将五硼酸铵、 氨硼烷络合物和氧化镁混合, 球磨均匀后, 在1200 ℃及0.6 L/min流动氨气保护条件下退火6 h, 即可在氧化铝基片上收集到白色毛状产物. 采用X射线衍射(XRD), 红外光谱(FTIR)、 扫描电子显微镜(SEM)、 透射电子显微镜(TEM)、 拉曼光谱(Raman)、 紫外-可见吸收光谱(UV-Vis)和荧光光谱(PL)对产物进行了表征. 结果表明, 样品呈一维线状分级结构, 长度大于5 mm, 中间为竹节状空心结构, 内部管径为50~350 nm, 外径范围为200~800 nm. 分级结构表面负载了大量氮化硼(BN)纳米薄片, 单个薄片厚度约为13 nm. 薄片弯曲褶皱, 相互交织, 构成1个氮化硼片层, 其厚度约为50~200 nm. UV-Vis和PL光谱测试结果表明, 氮化硼纳米管(BNNT)分级结构在紫外光材料领域具有一定的应用潜力, 且对亚甲基蓝具有良好的吸附能力(7 min即可吸附71%, 107 min时可吸附96%). 对比实验结果表明, BNNT的生长机理遵循气-液-固相(VLS)模型, 而表面负载的超薄BN片的生长机理遵循气-固相(VS)模型.     

静电纺丝制备多级结构TiO_2微纳米纤维

通过对共轴电纺内流体溶液浓度和流速的调控得到4种具有不同内部结构的TiO₂微纳米纤维, 即具有“微孔”、“囊泡”、“竹节”和“管”状结构的TiO₂微纳米纤维, 实现了对纤维内部结构的有效控制, 对于新型仿生中空纤维的研究具有重要意义.     

Polypeptide folding on a conformational‐space network: Dependence of network topology on the structural discretization procedure

Mapping the conformational space of a polypeptide onto a network of conformational states involves a number of subjective choices, mostly in relation to the definition of conformation and its discrete nature in a network framework. Here, we evaluate the robustness of the topology of conformational‐space networks derived from Molecular Dynamics (MD) simulations with respect to the use of different discretization (clustering) methods, variation of their parameters, simulation length and analysis time‐step, and removing high‐frequency motions from the coordinate trajectories. In addition, we investigate the extent to which polypeptide dynamics can be reproduced on the resulting networks when assuming Markovian behavior. The analysis is based on eight 500 ns and eight 400 ns MD simulations in explicit water of two 10‐residue peptides. Three clustering algorithms were used, two of them based on the pair‐wise root‐mean‐square difference between structures and one on dihedral‐angle patterns. A short characteristic path length and a power‐law behavior of the probability distribution of the node degree are obtained irrespective of the clustering method or the value of any of the tested parameters. The average cliquishness is consistently one or two orders of magnitude larger than that of a random realization of a network of corresponding size and connectivity. The cliquishness as function of node degree and the kinetic properties of the networks are found to be most dependent on clustering method and/or parameters. Although Markovian simulations on the networks reproduce cluster populations accurately, their kinetic properties most often differ from those observed in the MD simulations. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2010     

中成药质量稳定性的原子吸收光谱鉴别分析

对全国各地用原子吸收光谱仪测定10批次大活络丸中12种无机元素及建立的指纹图谱,尝试用成分数据分析法、向量相似法和模糊聚类法建立全国无机元素的指纹图谱和中药质量控制标准方法,获得较满意的结果。研究表明,建立无机元素指纹图谱控制中成药中无机元素的质量,具有很好的实用性和可操作性,用于全国各地不同厂家生产的大活络丸无机元素的质量控制是切实可行的。     

蛋白质折叠类型的分类建模与识别

蛋白质的氨基酸序列如何决定空间结构是当今生命科学研究中的核心问题之一. 折叠类型反映了蛋白质核心结构的拓扑模式, 折叠识别是蛋白质序列-结构研究的重要内容. 我们以占Astral 1.65序列数据库中α, β和α/β三类蛋白质总量41.8%的36个无法独立建模的折叠类型为研究对象, 选取其中序列一致性小于25%的样本作为训练集, 以均方根偏差(RMSD)为指标分别进行系统聚类, 生成若干折叠子类, 并对各子类建立基于多结构比对算法(MUSTANG)结构比对的概形隐马尔科夫模型(profile-HMM). 将Astral 1.65中序列一致性小于95%的9505个样本作为检验集, 36个折叠类型的平均识别敏感性为90%, 特异性为99%, 马修斯相关系数(MCC)为0.95. 结果表明: 对于成员较多, 无法建立统一模型的折叠类型, 基于RMSD的系统分类建模均可实现较高准确率的识别, 为蛋白质折叠识别拓展了新的方法和思路, 为进一步研究奠定了基础.     

纹党参与白条党参红外光谱的SIMCA聚类鉴别方法研究

以纹党参和白条党参的红外光谱为聚类分析的对象,研究了红外光谱结合SIMCA聚类分析法对纹党参和白条党参进行识别与分类的可行性.选取400 ～2 000 cm~(-1)范围内的光谱,通过基线补偿(Offset)和散射校正(MSC)等预处理后,采用SIMCA聚类分析法建立识别模型.结果表明,所建模型对纹党参和白条党参的识别率分别达92%和96%,拒绝率均为100%.用盲样对所建模型进行了测试,测试结果全部正确.该法可实现对纹党参和白条党参的快速鉴别.