含亚甲基兰的印染废水培养螺旋藻的研究

在含 2 5mg·L- 1 的亚甲基兰的正常和无氮培养液中培养盐泽螺旋藻 ,观测盐泽螺旋藻的生长、叶绿素含量、藻胆素含量及其对亚甲基兰的脱色率。结果表明 ,盐泽螺旋藻可由染料亚甲基兰提供唯一氮源 ,但不能提供唯一碳源。在培养 8d后 ,无氮培养液中的螺旋藻液吸光度达到 0 2 1 0 ;在正常培养液下螺旋藻液吸光度为 0 32 1。盐泽螺旋藻对染料的脱色效果也比较好 ,在培养 8d后 ,无氮培养液下亚甲基兰脱色率为 85 %;正常培养液下亚甲基兰脱色率为 86%。     

Reduction of defects and improvement in the performance of blue phosphor for thick‐dielectric electroluminescent displays using Color‐by‐Blue

Abstract— Defect‐free large‐area inorganic thick‐dielectric EL (TDEL) displays using Color by Blue (CBB) technology have been successfully developed. We have achieved the world's highest blue‐phosphor luminance of 900 cd/m² for a single‐pixel device by using CBB and by optimizing the e‐beam gun configuration and the flow rate of H₂S in the vacuum chamber. By analyzing the defects on panels with triple‐pattern phosphors and CBB panels, we also found that the number of defects on CBB panels can be drastically reduced compared with those on triple‐pattern panels. The defect‐free 17‐in. VGA CBB panels show better characteristics, a high peak luminance of 600 cd/m² and a high contrast ratio of 1000:1, compared with those of triple‐pattern panels.     

Dried jujube classification using support vector machine based on fractal parameters and red,green and blue intensity

A new method that combines fractal theory and red, green and blue (RGB) colour intensity was developed to sort dried jujube fruits by using support vector machine (SVM). Our result shows that the new method is fast and accurate in dried jujube fruits classification. The SVM models based on fractal parameters only achieved 85.18–92.73% total accuracy rate. The total classification accuracy of SVM based on RGB intensity values was 94.44%. However, the SVM models based on combining fractal parameters with RGB intensity values achieved 94.44–98.15% total accuracy rate. The best classification accuracy (98.15%) was found when using SVM model based on combining fractal measures (FM) with RGB intensity values (C = 512, γ = 0.0078125). Therefore, the SVM model based on combining FMs with RGB intensity is recommended in dried jujube fruits classification.     

Modified hydrothermal fabrication of a CoS2–graphene hybrid with improved photocatalytic performance

CoS₂ and a CoS₂–graphene composite were synthesized via a facile sonochemical and hydrothermal method. As-prepared samples were characterized by X-ray diffraction, scanning electron microscopy with energy-dispersive X-ray analysis, transmission electron microscopy, and UV-vis diffuse reflectance spectroscopy. The CoS₂–graphene composites exhibited high dye adsorption, extended light absorption, and efficient charge separation properties. Hence, for photodegradation of methylene blue (MB), significant enhancement of the reaction rate was observed for CoS₂–graphene compared to pure CoS₂. The high activity can be attributed to positive synergetic effects between CoS₂ and graphene. The excellent photoinduced charge separation and observed red shift make these hybrid materials potential candidates for the development of high-performance next-generation photocatalyst materials.     

元青花《昭君出塞》罐艺术特征与主题思想研究

"昭君出塞"故事源于汉代,绵延流传,元青花《昭君出塞》罐表现的即为"昭君出塞"故事题材。元青花《昭君出塞》罐具有鲜明的艺术特征,同时,亦具有丰富而深邃的主题思想内涵。     

Molecular design of deep blue fluorescent emitters with 20% external quantum efficiency and narrow emission spectrum

A thermally activated delayed fluorescent (TADF) emitter, 2,7-bis(9,9-dimethylacridin-10(9H)-yl)-9,9-dimethyl-9H-thioxanthene 10,10-dioxide (DMTDAc), was developed as a deep blue TADF emitter using a rigid 9,9-dimethyl-9H-thioxanthene 10,10-dioxide (DMTD) acceptor and an acridine donor. The rigid DMTD acceptor narrowed emission spectrum of DMTDAc by interlocking two phenyl units of diphenylsulfone. A deep blue TADF device with an external quantum efficiency close to 20% with a deep blue color coordinate of (0.15,0.13) was provided using the DMTDAc TADF emitter.     

水热法制备钒酸铋及可见光催化降解亚甲基蓝溶液研究

利用水热法制备了BiVO4粉体,并通过X射线衍射(XRD)、扫描电镜(SEM)、紫外一可见漫反射光谱(DRS)等对催化剂进行了表征。以制备的BiVO4为催化剂,考察了可见光照射下其对亚甲基蓝的降解效果。研究结果表明对初始质量浓度为10 mg/L的亚甲基蓝溶液50 mL,BiVO4加入量为0.6 g/L,经500 W氙灯(可见光)照射6 h后,亚甲基蓝的脱色率可达64%。     

Visual abstraction of dynamic network via improved multi-class blue noise sampling

Massive sequence view (MSV) is a classic timeline-based dynamic network visualization approach. However, it is vulnerable to visual clutter caused by overlapping edges, thereby leading to unexpected misunderstanding of time-varying trends of network communications. This study presents a new edge sampling algorithm called edge-based multi-class blue noise (E-MCBN) to reduce visual clutter in MSV. Our main idea is inspired by the multi-class blue noise (MCBN) sampling algorithm, commonly used in multi-class scatterplot decluttering. First, we take a node pair as an edge class, which can be regarded as an analogy to classes in multi-class scatterplots. Second, we propose two indicators, namely, class overlap and inter-class conflict degrees, to measure the overlapping degree and mutual exclusion, respectively, between edge classes. These indicators help construct the foundation of migrating the MCBN sampling from multi-class scatterplots to dynamic network samplings. Finally, we propose three strategies to accelerate MCBN sampling and a partitioning strategy to preserve local high-density edges in the MSV. The result shows that our approach can effectively reduce visual clutters and improve the readability of MSV. Moreover, our approach can also overcome the disadvantages of the MCBN sampling (i.e., long-running and failure to preserve local high-density communication areas in MSV). This study is the first that introduces MCBN sampling into a dynamic network sampling.