Snapshots of key intermediates unveiling the growth from silver ions to Ag70 nanoclusters

Nanoclusters (NCs) are considered as initial states of condensed matter, and unveiling their formation mechanism is of great importance for directional synthesis of nanomaterials. Here, we initiate the reaction of Ag(i) ions under weak reducing conditions. The prolonged reaction period provides a unique opportunity for revealing the five stages of the growth mechanism of 20-electron superatomic Ag₇₀ NCs by a time-dependent mass technique, that is, aggregate (I) → reduction (II) → decomposition and recombination (III) → fusion (IV) → surface recombination and motif enrichment (V), which is different from the formation process applicable to the gold clusters. More importantly, the key intermediates, Ag₁₄ without free electrons (0e) in the first (stage I) and Ag₂₄ (4e) in the second (stage II), were crystallized and structurally resolved, and the later transformation rate towards Ag₇₀ was further controlled by modulating solvents for easy identification of more intermediates. In a word, we establish a reasonable path of gradual expansion in size and electrons from Ag(i) ions to medium-sized 20e Ag₇₀. This work provides new insights into the formation and evolution of silver NCs, and unveils the corresponding optical properties along with the process.

The bottom-up synthesis of “medium-sized” Ag₇₀ (20e) was controlled and tracked, and then revealed. The crystallized key intermediates of Ag₁₄ (0e) and Ag₂₄ (4e) present the growth snapshots of silver nanoclusters.     

Deep Multilabel Multilingual Document Learning for Cross-Lingual Document Retrieval

Cross-lingual document retrieval, which aims to take a query in one language to retrieve relevant documents in another, has attracted strong research interest in the last decades. Most studies on this task start with cross-lingual comparisons at the word level and then represent documents via word embeddings, which leads to insufficient structure information. In this work, the cross-lingual comparison at the document level is achieved through the cross-lingual semantic space. Our method, MDL (deep multilabel multilingual document learning), leverages a six-layer fully connected network to project cross-lingual documents into a shared semantic space. The semantic distances can be calculated when the cross-lingual documents are transformed into embeddings in semantic space. The supervision signals are automatically extracted from the data and then used to construct the semantic space via a linear classifier. The ambiguity of manual labels could be avoided and the multilabel supervision signals can be acquired instead of a single label. The representation of the semantic space is enriched by multilabel supervision signals, which improves the discriminative ability of the embeddings. The MDL is easy to extend to other fields since it does not depend on specific data. Furthermore, MDL is more efficient than the models training all languages jointly, since each language is trained individually. Experiments on Wikipedia data showed that the proposed method outperforms the state-of-the-art cross-lingual document retrieval methods.     

Intelligent Fault Diagnosis of Industrial Robot Based on Multiclass Mahalanobis-Taguchi System for Imbalanced Data

One of the biggest challenges for the fault diagnosis research of industrial robots is that the normal data is far more than the fault data; that is, the data is imbalanced. The traditional diagnosis approaches of industrial robots are more biased toward the majority categories, which makes the diagnosis accuracy of the minority categories decrease. To solve the imbalanced problem, the traditional algorithm is improved by using cost-sensitive learning, single-class learning and other approaches. However, these algorithms also have a series of problems. For instance, it is difficult to estimate the true misclassification cost, overfitting, and long computation time. Therefore, a fault diagnosis approach for industrial robots, based on the Multiclass Mahalanobis-Taguchi system (MMTS), is proposed in this article. It can be classified the categories by measuring the deviation degree from the sample to the reference space, which is more suitable for classifying imbalanced data. The accuracy, G-mean and F-measure are used to verify the effectiveness of the proposed approach on an industrial robot platform. The experimental results show that the proposed approach’s accuracy, F-measure and G-mean improves by an average of 20.74%, 12.85% and 21.68%, compared with the other five traditional approaches when the imbalance ratio is 9. With the increase in the imbalance ratio, the proposed approach has better stability than the traditional algorithms.     

Isolation and Purification of Polysaccharides from Cordyceps minlitaris and Its Inhibition on the Proliferation of Rat Glomerular Mesangial Cells

The crude polysaccharide was obtained by means of the decolorization of porphyrized Cordyceps minlitaris stroma with organic solvent, extraction with hot water, precipitation in 80% ethanol, and protein removal with the Sevag method. After purification with Sephadex G-75, two of its components, CMP-1 and CMP-2, were obtained. Through the assay of gel chromatography and polarimetry, CMP-1 was identified as pure polysaccharide. The results demonstrated that CMP-1 had favorable oxidation resistance activity, which could scavenge not only oxygen-free radicals in the self-oxidation system of pyrogallic acid, but also the hydroxide-free radicals in the Fenton system. The study focused on the effects of low, medium, and high dosages of CMP-1 in rat blood serum on the proliferation of glomerular mesangial cells in vitro. Through MTT Colorimetric analysis, the activities were compared among the blank control group and the Niaoduqing positive control group CMP-1 and CMP-2. The results shows that CMP-1 was able to inhibit the proliferation of rat glomerular mesangial cells effectively. Therefore, CMP-1, one component of polysaccharides of Cordyceps minlitaris, was certainly a potential remedy for hyperplastic glomerular nephritis, whose antioxidant activity could slow down the process of chronic renal failure（CRF） to some extent.     

单谐振腔外部Q值的计算方法

 通过对带有耦合器的单驻波腔进行等效电路分析,推导出可计算带有耦合波导的单谐振腔外部Q值的反射相位法,该方法通过计算不同频率的反射相位来确定谐振频率和外部Q值。详细叙述了在CST Microwave Studio中用谐振模式求解法、直接时域跟踪法和反射相位法进行计算的步骤;用这3种方法分别计算了二次发射微波电子枪的外部Q值并和实验测量结果做了比较。结果显示3种计算方法都有很高的精度,但反射相位法的速度最快,耗时1 071 s。计算发现外部Q值与耦合口长度的4.1次方成反比。     

β-四(对羟甲基苯氧基)酞菁锌的合成及其抗肿瘤活性

以4-硝基邻苯二腈和对羟基苯甲醇为原料,经两步法设计并合成了两亲性β-四(对羟甲基苯氧基)酞菁锌(Ⅱ)光敏剂(1),其结构经UV-vis,1H NMR,IR,激光解吸电离飞行时间质谱(TOF-MS)及元素分析表征.用HE染料法和MTT染色法研究了1对Bel-7402人体肝癌细胞的抑制作用,考察了c(1)对Bel-7402细胞抑制率的影响.实验结果表明,当c(1)为50 mg·L-1时,抑制率达67%,其IC50=30.1 mg·L-1.     

Photoinduced Nal-Promoted Radical Borylation of Alkyl Halides and Pseudohalides

Main observation and conclusion A method for photoinduced Nal-promoted radical borylation of aliphatic halides and pseudohalides with bis(catecholato)diboron(B2...     

托卡马克真空室内线圈水冷管道接头强化二次流的数值研究

基于计算流体力学(CFD)理论,研究了不同曲率半径的螺旋导流片的托卡马克真空室内线圈水冷管道接头。利用湍流数值模拟方法,分析了线圈管道接头导流片曲率半径比、冷却水入口流速对线圈管道内流体平均雷诺数分布的影响。结果表明,不同导流片曲率半径比的线圈管道内的流体雷诺数分布曲线相似,平均雷诺数随入口流速的增加而增大,管道接头出口雷诺数随导流片曲率半径比的增大而减小,导流片曲率半径比小的管接头更适用于线圈水冷曲线管的二次流强化。此外,还为导流片曲率半径比为0.2的管接头拟合了管接头出口雷诺数与入口流速的关系式,为进一步研究类似于托卡马克真空室内线圈管道的曲线管接头的二次流强化提供理论基础。     

氩气感应耦合等离子体速度与温度数值模拟

为了获得用于研究再入飞行器热防护系统的感应耦合等离子体风洞流场数据,基于流场、电磁场和化学场的多场耦合建立了非平衡态感应耦合等离子体数值模型。利用该模型对不同入口质量流率和不同工作压力下的感应耦合等离子体进行了数值模拟,得到了相应工作参数下感应耦合等离子体温度与速度的分布特性。计算结果表明:等离子体中心线上的速度随着入口质量流率的增大而增大,而随着工作压力的增大而减小;同时,等离子体中心线上的温度随着入口质量流率的增大而减小,而随着压力的增大先减小后增大。这些结果可为感应耦合等离子体风洞优化设计及其工业应用提供理论指导。     

描述复合型疲劳裂纹扩展路径的等效模型

复合型裂纹的扩展路径不同于Ⅰ型裂纹,会沿着与初始裂纹面不同的方向扩展,扩展路径的准确预测对扩展速率的评估具有重要的作用.采用最大周向应力准则和最小应变能密度准则进行扩展路径的预测时,开裂角的误差会使裂纹不断偏离实际路径,造成最终结果的较大偏差.论文将复合裂纹在扩展过程中的弯折裂纹简化为直线裂纹,对简化过程中所产生的误差进行定量分析,并在此基础上提出了一种描述复合型裂纹扩展路径的等效修正模型.将此模型写入ABAQUS扩展有限元模块,实现了基于等效修正模型的疲劳裂纹扩展程序.通过对含中心斜裂纹板的疲劳裂纹扩展试验,验证了该模型的有效性,预测的开裂角与试验结果基本一致,所得到的载荷循环次数低于试验值,对含裂纹结构的寿命评估偏于保守.