PTC: Full and Drift Particle Orbit Tracing Code for α Particles in Tokamak Plasmas

Fusion born α particle confinement is one of the most important issues in burning plasmas,such as ITER and CFETR.However,it is extremely complex due to the nonequilibrium characteristics,and multiple temporal and spatial scales coupling with background plasma.A numerical code using particle orbit tracing method(PTC)has been developed to study energetic particle confinement in tokamak plasmas.Both full orbit and drift orbit solvers are implemented to analyze the Larmor radius effects on α particle confinement.The elastic collisions between alpha particles and thermal plasma are calculated by a Monte Carlo method.A triangle mesh in poloidal section is generated for electromagnetic fields expression.Benchmark between PTC and ORBIT has been accomplished for verification.For CFETR burning plasmas,PTC code is used for α particle source and slowing down process calculation in 2D equilibrium.In future work,3D field like toroidal field ripples,Alfven and magnetohydrodynamics instabilities perturbation inducing α particle transport will be analyzed.     

Asymmetric synthesis of chromanone lactones via vinylogous conjugate addition of butenolide to 2-ester chromones

Chiral chromanone lactones are a class of natural products with important biological activity. We report a direct diastereo- and enantioselective vinylogous conjugate addition of butenolide to 2-ester substituted chromones. The transformation proceeded well in the presence of as low as 1 mol% of a chiral N,N′-dioxide/Sc^III complex, 3 Å MS and a catalytic amount of hexafluoroisopropanol (HFIP). The scope of Michael acceptors includes a variety of substituted chromones at different positions, and the desired chromanone lactones upon reduction are afforded in good yield and diastereoselectivity, and excellent enantioselectivity (up to 99% ee). The strategy could be used in the concise synthesis of blennolide C and gonytolide A, C and G.

We report a direct, diastero- and enantioselective vinylogous 1,4-addition of butanolide to 2-ester chromones. A chiral Sc^III complex enabled the reaction to proceed smoothly to give a variety of chraomanone lactones.     

金催化乙炔氢氯化的密度泛函理论研究进展

乙炔氢氯化(AH)是生产氯乙烯的主要途径之一,传统上使用高毒性的汞催化剂,因此开发无汞催化剂迫在眉睫。金(Au)催化剂是最有潜力的替代催化剂之一,然而其活性Au物种、反应物的活化过程或反应过渡态结构等催化机理仍不够清晰。密度泛函理论（DFT）在研究由Au催化AH的反应机理中发挥了极其重要的作用。本文综述了DFT对金催化剂活性位点、反应物在催化剂上的吸附性质及反应机理的研究进展。重点讨论了DFT对阳离子金和金簇催化AH反应过程的模拟计算,包括Au电子状态、其它原子掺杂及金簇尺寸和形状对催化AH反应影响的模拟。结果表明DFT模拟计算在微观分子尺度上研究反应物的吸附、反应中间体及过渡态等方面发挥了关键作用,对理解Au催化AH反应机理做出了重要贡献。     

ZnO纳米钉的制备和光学性质研究

通过化学气相沉积(CVD)方法在Si(100)衬底上制备出新型的ZnO纳米钉结构.X射线衍射(XRD)结果表明纳米钉是六角纤锌矿结构.纳米钉顶部对角线在450～750 nm之间,纳米钉长度为几个微米.研究了不同气氛下退火样品的可见发光性质,认为绿光发射来自于导带电子和反位氧中空穴的辐射复合.     

Improved Stretchable and Sensitive Fe Nanowire-Based Strain Sensor by Optimizing Areal Density of Nanowire Network

Flexible strain sensors, when considering high sensitivity and a large strain range, have become a key requirement for current robotic applications. However, it is still a thorny issue to take both factors into consideration at the same time. Here, we report a sandwich-structured strain sensor based on Fe nanowires (Fe NWs) that has a high GF (37–53) while taking into account a large strain range (15–57.5%), low hysteresis (2.45%), stability, and low cost with an areal density of Fe NWs of 4.4 mg/cm². Additionally, the relationship between the contact point of the conductive network, the output resistance, and the areal density of the sensing unit is analyzed. Microscopically, the contact points of the conductive network directly affect the sensor output resistance distribution, thereby affecting the gauge factor (GF) of the sensor. Macroscopically, the areal density and the output resistivity of the strain sensor have the opposite percolation theory, which affects its linearity performance. At the same time, there is a positive correlation between the areal density and the contact point: when the stretching amount is constant, it theoretically shows that the areal density affects the GF. When the areal density reaches this percolation threshold range, the sensing performance is the best. This will lay the foundation for rapid applications in wearable robots.     

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.     

Harnessing Protein-Ligand Interaction Fingerprints to Predict New Scaffolds of RIPK1 Inhibitors

Necroptosis has emerged as an exciting target in oncological, inflammatory, neurodegenerative, and autoimmune diseases, in addition to acute ischemic injuries. It is known to play a role in innate immune response, as well as in antiviral cellular response. Here we devised a concerted in silico and experimental framework to identify novel RIPK1 inhibitors, a key necroptosis factor. We propose the first in silico model for the prediction of new RIPK1 inhibitor scaffolds by combining docking and machine learning methodologies. Through the data analysis of patterns in docking results, we derived two rules, where rule #1 consisted of a four-residue signature filter, and rule #2 consisted of a six-residue similarity filter based on docking calculations. These were used in consensus with a machine learning QSAR model from data collated from ChEMBL, the literature, in patents, and from PubChem data. The models allowed for good prediction of actives of >90, 92, and 96.4% precision, respectively. As a proof-of-concept, we selected 50 compounds from the ChemBridge database, using a consensus of both molecular docking and machine learning methods, and tested them in a phenotypic necroptosis assay and a biochemical RIPK1 inhibition assay. A total of 7 of the 47 tested compounds demonstrated around 20–25% inhibition of RIPK1’s kinase activity but, more importantly, these compounds were discovered to occupy new areas of chemical space. Although no strong actives were found, they could be candidates for further optimization, particularly because they have new scaffolds. In conclusion, this screening method may prove valuable for future screening efforts as it allows for the exploration of new areas of the chemical space in a very fast and inexpensive manner, therefore providing efficient starting points amenable to further hit-optimization campaigns.     

一种超高耐久混凝土——梯度结构混凝土

分析超高耐久混凝土的研究现状,提出了一种高耐久、低成本的超高耐久混凝土--梯度结构混凝土(Gradient Structural Concrete,简称GSC),采用电量法和氯离子扩散系数法来评价梯度结构混凝土的抗氯离子渗透性能,最后进行梯度结构混凝土的寿命预测与成本分析.结果表明,高性能混凝土(High Performance Concrete,简称HPC)、无细观界面过渡区水泥基复合材料(Meso-interfacial transition zone-free cement-based materials,简称 MIF)和梯度结构混凝土MIF-HPC的6 h导电量和氯离子扩散系数的排列顺序均为:HPC>MIF-HPC>MIF,其抗氯离子渗透性能排列顺序为:MIF>MIF-HPC>HPC.梯度结构混凝土的成本会有所增加,但由于其氯离子渗透性能大幅降低,其使用寿命得到大幅延长,使得其年损耗费用大幅下降.     

Polyoxometalate/Cellulose Nanofibrils Aerogels for Highly Efficient Oxidative Desulfurization

Polyoxometalate (POM) presents great potential in oxidative desulfurization (ODS) reaction. However, the high dissolubility of POM in common solvents makes it difficult to recycle. Besides, the small specific surface area of POM also limits the interaction between them and the substrate. Depositing polyoxometalates onto three-dimensional (3D) network structured materials could largely expand the application of POM. Here, the surfaces of cellulose nanofibrils (CNFs) were modified with very few (3-Aminopropyl) trimethoxysilane (APTS) to endow positive charges on the surfaces of CNFs, and then phosphotungstic acid (PTA) was loaded to obtain the aerogel A-CNF/PTA as the ODS catalyst. FT-IR indicated the successful deposition of PTA onto aminosilane modified CNF surfaces. UV-VIS further suggested the stability of PTA in the aerogels. BET and SEM results suggested the increased specific surface area and the relatively uniform 3D network structure of the prepared aerogels. TGA analysis indicated that the thermal stability of the aerogel A-CNF/PTA50% was a little higher than that of the pure CNF aerogel. Most importantly, the aerogel A-CNF/PTA50% showed good catalytic performance for ODS. Catalysis results showed that the substrate conversion rate of the aerogel A-CNF/PTA50% reached 100% within 120 min at room temperature. Even after five cycles, the substrate conversion rate of the aerogel A-CNF/PTA50% still reached 91.2% during the dynamic catalytic process. This work provides a scalable and facile way to stably deposit POM onto 3D structured materials.