Warm-white light performance of Dy3+, Eu3+: NaLa(WO4)2 phosphors excited with a 450-nm LED

Dy³⁺, Eu³⁺: NaLa(WO₄)₂ phosphors are successfully synthesized through the solid-state reaction technique. The phase-structure and morphology are measured via X-ray diffraction and energy dispersive spectrometry. The concentrations of Dy³⁺, Eu³⁺, La³⁺, and W⁶⁺ are measured via ICP. The absorption and excited spectra are presented, which indicate that a blue band ranging from 430 to 480 nm is suitable for excitation. Using a commercial blue LED with a wavelength of 450 nm as the excitation light source, emission spectra for samples with varying dopant concentration ratios of Dy³⁺ to Eu³⁺ are obtained, which show good tunable yellow and red emission. For the purpose of investigating white LED performance, CIE spectra and a white light photo are also presented. The results reveal that varying the dopant concentration ratio of Dy³⁺ to Eu³⁺ plays a key role in the warm-white performance. With increasing concentration of Eu³⁺, the correlated color temperature decreases from 4069 to 3172 K, which indicates good warm-white performance.     

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.     

Combined effects of wheat gluten and carboxymethylcellulose on dough rheological behaviours and gluten network of potato–wheat flour-based bread

Incorporating high level of potato flour into wheat flour enhances nutritional values of bread but induces a series of problems that lead to the decline of the bread quality. To overcome the barrier, wheat gluten and carboxymethylcellulose (CMC) were added into potato–wheat composite flour to improve dough machinability and bread quality. The rheological properties, thermo-mechanical properties and microstructures of dough were investigated. The results showed that the interaction between gluten and CMC mitigated the discontinuity of gluten matrix and gluten protein aggregation caused by the addition of potato flour, which yielded a more branched and compact gluten network. The compact three-dimensional viscoelastic structure induced improvements of gas retention capacity and dough stability, making it mimic the machinability properties of wheat flour dough. Bread qualities were apparently improved with the combined use of 4% gluten and 6% CMC, of which specific volume increased by 42.86%, and simultaneously, hardness reduced by 75.93%.     

纳米氧化物对水泥基料化学收缩的影响

采用ASTM化学收缩试验方法,研究纳米SiO2、MgO、Al2O3对水泥基材料化学收缩变化的影响,明确纳米材料种类的差异性。结果表明,各实验组水泥基材料化学收缩均经历三阶段:急速变化区、缓慢变化区、平稳区,7d始水泥基材料化学收缩基本稳定。几种纳米材料的最佳掺量对水泥基材料的化学收缩改善作用大小为:SiO2>Al2O3>MgO。研究发现,纳米材料种类和掺量的不同对化学收缩的影响也较大,在选用时,需综合考虑。     

Disease Ionomics: Understanding the Role of Ions in Complex Disease

Ionomics is a novel multidisciplinary field that uses advanced techniques to investigate the composition and distribution of all minerals and trace elements in a living organism and their variations under diverse physiological and pathological conditions. It involves both high-throughput elemental profiling technologies and bioinformatic methods, providing opportunities to study the molecular mechanism underlying the metabolism, homeostasis, and cross-talk of these elements. While much effort has been made in exploring the ionomic traits relating to plant physiology and nutrition, the use of ionomics in the research of serious diseases is still in progress. In recent years, a number of ionomic studies have been carried out for a variety of complex diseases, which offer theoretical and practical insights into the etiology, early diagnosis, prognosis, and therapy of them. This review aims to give an overview of recent applications of ionomics in the study of complex diseases and discuss the latest advances and future trends in this area. Overall, disease ionomics may provide substantial information for systematic understanding of the properties of the elements and the dynamic network of elements involved in the onset and development of diseases.