Surface treatment of titanium dioxide nanopowder using rotary electrode dielectric barrier discharge reactor

Titanium dioxide (TiO2) nanopowder (P-25;Degussa AG) was treated using dielectric barrier discharge (DBD) in a rotary electrode DBD (RE-DBD) reactor.Its electrical and optical characteristics were investigated during RE-DBD generation.The treated TiO2 nanopowder properties and structures were analyzed using x-ray diffraction (XRD) and Fourier-transform infrared spectroscopy (FTIR).After RE-DBD treatment,XRD measurements indicated that the anatase peak theta positions shifted from 25.3° to 25.1°,which can be attributed to the substitution of new functional groups in the TiO2 lattice.The FTIR results show that hydroxyl groups (OH) at 3400 cm-1 increased considerably.The mechanism used to modify the TiO2 nanopowder surface by air DBD treatment was confirmed from optical emission spectrum measurements.Reactive species,such as OH radical,ozone and atomic oxygen can play key roles in hydroxyl formation on the TiO2 nanopowder surface.     

用协同还原一步法制备氢化丁腈橡胶/还原氧化石墨烯纳米复合材料（英文）

选择具有还原特性的物质,如维生素C、对苯二酚或茶多酚作为协同还原剂,协同水合肼对丁腈橡胶/氧化石墨烯(GO)纳米复合胶乳中的碳碳双键及GO片层同时进行加氢和还原,制备了氢化丁腈橡胶(HNBR)/还原GO纳米复合材料,并利用衰减全反射傅里叶变换红外光谱仪、拉曼光谱仪和热重分析仪等仪器对所制备的纳米复合材料进行了表征。结果表明,GO的引入进一步改善了HNBR的性能;同时协同还原剂可与水合肼共同参与还原反应,提高了碳碳双键的加氢度和GO片层的还原程度,使得复合材料的热稳定性得到改善。     

Scalable distributed Louvain algorithm for community detection in large graphs

Community detection (or clustering) in large-scale graphs is an important problem in graph mining. Communities reveal interesting organizational and functional characteristics of a network. Louvain algorithm is an efficient sequential algorithm for community detection. However, such sequential algorithms fail to scale for emerging large-scale data. Scalable parallel algorithms are necessary to process large graph datasets. In this work, we show a comparative analysis of our different parallel implementations of Louvain algorithm. We design parallel algorithms for Louvain method in shared memory and distributed memory settings. Developing distributed memory parallel algorithms is challenging because of inter-process communication and load balancing issues. We incorporate dynamic load balancing in our final algorithm DPLAL (Distributed Parallel Louvain Algorithm with Load-balancing). DPLAL overcomes the performance bottleneck of the previous algorithms and shows around 12-fold speedup scaling to a larger number of processors. We also compare the performance of our algorithm with some other prominent algorithms in the literature and get better or comparable performance . We identify the challenges in developing distributed memory algorithm and provide an optimized solution DPLAL showing performance analysis of the algorithm on large-scale real-world networks from different domains.