高效液相色谱法同时测定化妆品中的7种香豆素

基于香豆素类化合物在化妆品中的使用情况及其对人体健康的危害,采用C18色谱柱作为分析柱,通过优化试验条件建立了一种可准确、简便测定化妆品中多种香豆素类化合物的高效液相色谱法。试验结果表明,7种香豆素类化合物的保留时间和峰面积的相对标准偏差分别小于0.1%和2%,且检出限均低于40μg/L;通过样品加标试验,得到7种目标化合物的回收率为80%~94%。所建立方法具有快速、简便、准确、灵敏的特点,是测定化妆品中多种香豆素类化合物的有效方法。     

Towards a Better Prediction of Cell Settling on Nanostructure Arrays—Simple Means to Complicated Ends

Vertical arrays of nanostructures (NSs) are emerging as promising platforms for probing and manipulating live mammalian cells. The broad range of applications requires different types of interfaces, but cell settling on NS arrays is not yet fully controlled and understood. Cells are both seen to deform completely into NS arrays and to stay suspended like tiny fakirs, which have hitherto been explained with differences in NS spacing or density. Here, a better understanding of this phenomenon is provided by using a model that takes into account the extreme membrane deformation needed for a cell to settle into a NS array. It is shown that, in addition to the NS density, cell settling depends strongly on the dimensions of the single NS, and that the settling can be predicted for a given NS array geometry. The predictive power of the model is confirmed by experiments and good agreement with cases from the literature. Furthermore, the influence of cell‐related parameters is evaluated theoretically and a generic method of tuning cell settling through surface coating is demonstrated experimentally. These findings allow a more rational design of NS arrays for the numerous exciting biological applications where the mode of cell settling is crucial.     

A practical reliability assessment approach and its application for pile-stabilized slopes using FORM and support vector machine

Bulletin of Engineering Geology and the Environment - Many uncertainties exist in pile-stabilized slopes which make their design substantially complicated. In this paper, a first-order reliability...     

A Student's t-based density peaks clustering with superpixel segmentation (tDPCSS) method for image color clustering

Image color clustering is a basic technique in image processing and computer vision, which is often applied in image segmentation, color transfer, contrast enhancement, object detection, skin color capture, and so forth. Various clustering algorithms have been employed for image color clustering in recent years. However, most of the algorithms require a large amount of memory or a predetermined number of clusters. In addition, some of the existing algorithms are sensitive to the parameter configurations. In order to tackle the above problems, we propose an image color clustering method named Student's t-based density peaks clustering with superpixel segmentation (tDPCSS), which can automatically obtain clustering results, without requiring a large amount of memory, and is not dependent on the parameters of the algorithm or the number of clusters. In tDPCSS, superpixels are obtained based on automatic and constrained simple non-iterative clustering, to automatically decrease the image data volume. A Student's t kernel function and a cluster center selection method are adopted to eliminate the dependence of the density peak clustering on parameters and the number of clusters, respectively. The experiments undertaken in this study confirmed that the proposed approach outperforms k-means, fuzzy c-means, mean-shift clustering, and density peak clustering with superpixel segmentation in the accuracy of the cluster centers and the validity of the clustering results.     

Surface Modification by a Flexible Tool. 1. Plastic Surface Deformation and Simultaneous Coating Application by Rotating Wire Brushes

The machining of metal surfaces and the application of functional coatings by means of a flexible tool (a rotating wire brush) is considered. Frictional cladding may be applied on metal-cutting machines by means of simple attachments or manual angle grinders.