钇对金属陶瓷力学性能和组织的影响

研究了添加钇对Ｔｉ（Ｃ，Ｎ）基金属瓷力学性能和组织的影响。结果表明，加入适量的钇能提高Ｔｉ（Ｃ，Ｎ）基金属陶瓷的抗变强度和硬度，其原因是由于钇与杂质硫起反应在化合物，净化了陶瓷相－金属相，陶瓷相－陶瓷相界面提高子界面结合强度     

铜基催化剂的结构、物性及其对混合碳四选择加氢反应的催化性能

 用XRD，XPS，SEM和H2－TPR等手段研究了铜基负载型催化剂的结\r\n构和物性及其对混合碳四加氢脱炔的催化性能．结果表明，在负载铜催\r\n化剂中加入一定量的Co可提高催化剂的加氢脱炔活性，而Co含量较少时\r\n加入少量的Ce也能提高催化剂的活性和选择性．Cu和Co之间存在着相互\r\n协同作用，使得活性组分在催化剂表面偏析，CuO在催化剂表面呈非晶\r\n相分散状态，催化剂颗粒粒径变小，催化剂更容易被还原，从而改善了\r\n催化剂的催化性能．     

Novel palladium nanoparticles supported on β‐cyclodextrin@graphene oxide as magnetically recyclable catalyst for Suzuki–Miyaura cross‐coupling reaction with two different approaches in bio‐based solvents

A novel nanocatalyst was designed and prepared. Initially, the surface of magnetic graphene oxide (M‐GO) was modified using thionyl chloride, tris(hydroxymethyl)aminomethane and acryloyl chloride as linkers which provide reactive C═C bonds for the polymerization of vinylic monomers. Separately, β‐cyclodextrin (β‐CD) was treated with acryloyl chloride to provide a modified β‐CD. Then, in the presence methylenebisacrylamide as a cross‐linker, monomers of modified β‐CD and acrylamide were polymerized on the surface of the pre‐prepared M‐GO. Finally, palladium acetate and sodium borohydride were added to this composite to afford supported palladium nanoparticles. This fabricated nanocomposite was fully characterized using various techniques. The efficiency of this easily separable and reusable heterogeneous catalyst was successfully examined in Suzuki–Miyaura cross‐coupling reactions of aryl halides and boronic acid as well as in modified Suzuki–Miyaura cross‐coupling reactions of N‐acylsuccinimides and boronic acid in green media. The results showed that the nanocatalyst was efficient in coupling reactions for direct formation of the corresponding biphenyl as well as benzophenone derivatives in green media based on bio‐based solvents. In addition, the nanocatalyst was easily separable, using an external magnet, and could be reused several times without significant loss of activity under the optimum reaction conditions.     

凝固漂浮有机液滴-分散液液微萃取结合高效液相色谱法同时测定自来水中氯代多环芳烃与多环芳烃

建立了自来水中6种氯代多环芳烃和15种多环芳烃的凝固漂浮有机液滴-分散液液微萃取高效液相色谱分析方法,并探讨了萃取剂种类和用量、分散剂种类和用量、氯化钠含量及涡旋振荡时间等因素对萃取效率的影响。优化后的萃取实验条件为:10μL十二醇为萃取溶剂,500μL甲醇为分散溶剂,6%NaCl,涡旋振荡时间2 min。目标化合物经多环芳烃专用柱(SUPELCOSILTMLC-PAH,150 mm×4.6 mm,5μm)分离后,外标法定量。结果表明,21种目标化合物在一定质量浓度范围内线性良好,相关系数均不低于0.999;在低、中、高3个加标水平下的回收率为70.6%～98.7%,相对标准偏差(RSD)为2.0%～10%;方法的检出限(LOD,S/N=3)为0.000 7～0.009μg/L,定量下限(LOQ,S/N=10)为0.002 2～0.028μg/L。可用于自来水中氯代多环芳烃和多环芳烃的分析检测。     

A deep learning ensemble for function prediction of hypothetical proteins from pathogenic bacterial species

Protein function prediction is a crucial task in the post-genomics era due to their diverse irreplaceable roles in a biological system. Traditional methods involved cost-intensive and time-consuming molecular biology techniques but they proved to be ineffective after the outburst of sequencing data through the advent of cost-effective and advanced sequencing techniques. To manage the pace of annotation with that of data generation, there is a shift to computational approaches which are based on homology, sequence and structure-based features, protein-protein interaction networks, phylogenetic profiles, and physicochemical properties, etc. A combination of these features has proven to be promising for protein function prediction in terms of improving prediction accuracy. In the present work, we have employed a combination of features based on sequence, physicochemical property, subsequence and annotation features with a total of 9890 features extracted and/or calculated for 171,212 reviewed prokaryotic proteins of 9 bacterial phyla from UniProtKB, to train a supervised deep learning ensemble model with the aim to categorize a bacterial hypothetical/unreviewed protein’s function into 1739 GO terms as functional classes. The proposed system being fully dedicated to bacterial organisms is a novel attempt amongst various existing machine learning based protein function prediction systems based on mixed organisms. Experimental results demonstrate the success of the proposed deep learning ensemble model based on deep neural network method with F1 measure of 0.7912 on the prepared Test dataset 1 of reviewed proteins.     

丁苯、丁腈基聚氨酯的形态与性能

用示差扫描量热法 (DSC)、红外分光光度计 (FTIR)和原子力显微镜 (AFM)研究了端羟基聚丁二烯 苯乙烯共聚物 (HTBS)、端羟基聚丁二烯 丙烯腈共聚物 (HTBN)和端羟基聚丁二烯 (HTPB)与甲苯二异氰酸酯、1 ,4 丁二醇构成的溶液法聚二烯烃基聚氨酯 (PU)的形态结构 .结果表明HTPB和HTBS基PU的相分离程度很大 ,而HTBN基PU的相分离程度小 .这可能归因于HTBS软段的极性低 ,不能与硬段形成氢键 ,而HTBN软段中的腈基具有很强的极性 ,且可以与硬段形成氢键作用 ,增加了软硬段间的相容性 ,相分离程度明显降低 .AFM表明HTBN PU随着硬段含量提高 ,表面粗糙度增大 ,由软段为连续相逐渐过渡到双连续结构 .在硬段含量 6 3%时 ,HTBN和HTPB基PU均呈双连续结构 ,而HTBS PU中硬段为连续相 .HTBN PU软段的相区尺寸在1 2nm左右 ,表面粗糙度较大 ,HPBS PU软段的相区尺寸在 1 1nm左右 ,表面粗糙度最小 ,HTPB PU存在 1 4nm和 5 0nm大小不等的软段相区尺寸 .力学性能表明 ,在软段中引入苯乙烯和丙烯腈结构 ,可使聚氨酯抗张强度分别提高 1 5和 2倍 ,模量和断裂伸长率也明显提高     

基于课程标准的教学——以常见阴、阳离子的检验为例

以常见的阴、阳离子的检验为例，研究了如何实施基于标准的教学。校本教材的开发为标准、教材、教学、评价的一致性提供保障；以学生应知的和能做的驱动课堂活动；根据达成标准应有怎样的质量表现，试卷编制先于教学设计。课堂上，“教”“学”双方都明确学习目标，教师提供多种策略来满足学生多样的学习需要，如提供工具，搭建脚手架，并以“微”研究性学习的方式展开教学，给学生提供了充分的进步空间。