低温制备≥99.5%多晶氧化铝陶瓷

本文通过引入稀土氧化物Y2O3、Tm2O3为烧结助剂低温制备了氧化铝含量大于99.5;的多晶氧化铝陶瓷.实验表明:稀土氧化物的加入能够明显降低99.5;多晶氧化铝陶瓷的烧结温度,提高致密度.Y2O3、Tm2O3混合烧结助剂与单一稀土氧化物的烧结助剂相比能够明显抑制晶粒的生长,促进晶粒的均匀发育.当Y2O3+Tm2O3的含量为0.3;质量分数时,99.5;多晶氧化铝陶瓷的相对密度可达99.2;理论密度,抗弯强度为533MPa,显微硬度为17.2GPa.陶瓷断裂主要以穿晶断裂为主.     

Er3+:NaY(Wo.9Moo.1O4)2晶体的生长及光谱性能

采用提拉法(CZ法),生长出质量良好的Er3+:NaY(W0.9Mo0.1O4)2晶体.通过X射线粉末衍射,红外光谱分析,并与NaY(WO4)2相比较,得到Er3+:NaY(W0.9Mo0.1O4)2晶体的结构与NYW类似,仍为四方晶系的白钨矿结构,I4(1)/α空间群.测定了晶体的实际组成,得到晶体中各元素均按理论值进行掺杂,计算了掺杂离子的分凝系数约为1.15.在光谱性质上,测试了晶体的吸收光谱,及晶体在50～1000cm-1波数范围内的拉曼光谱,并计算了各吸收峰的半峰宽及吸收系数A.     

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.     

新版《高分子化学》解读

旨在剖析祖仁教授主编的《高分子化学》第四版的教学思想和撰写特点,涉及章节、内容的选择和安排、逻辑思维和教学方法,以及一些技术术语等。同时也指出了新版教材的一些印刷错误等。     

Tailoring Pore Structure and Morphologies in Covalent Organic Frameworks for Xe/Kr Capture and Separation

As a rising star among porous solid materials, covalent organic frameworks(COFs) with excellent properties including but not limit to facilely controllable structure, high porosity, and multi-chemical functionality represent significant potential for efficient ¹²⁷Xe/⁸⁵Kr capture and separation. In this study, through tuning the length of the organic ligands, two-dimensional(2D) COF mate-rials with identical connection group but different pore properties, denoted as ATFG-COF and TpPa-COF with AA-stacking model and TpBD-COF with AB-stacking model were synthesized and tested for Kr and Xe adsorption for the first time. Adsorption measurements indicate that the narrower pore apertures and higher porosity are conducive for COF materials to capture Xe and Kr. Furthermore, the Henry's constant, isosteric heat of adsorption(Q_st), and ideal adsorbed solution theory(IAST) selectivity of ATFG-COF, the pore size of which is closest to the kinetic diameter of the Xe atom(0.41 nm) among 2D COF materials, were carried out based on the single component sorption isotherms. The results illustrate that the high isosteric heat values of Xe/Kr adsorption on ATFG-COF are 25 and 16 kJ/mol at room temperature, respectively. Henry's law predicts that the selectivity factor of Xe to Kr is 6.07, consistent with the adsorption selectivity(ca. 6) calculated based on the IAST.     

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.     

含氢类金刚石薄膜的生长及表征

采用射频-等离子体辅助化学气相沉积(RF-PECVD)法在硅片、玻璃上生长类金刚石薄膜.通过Raman光谱、AFM等测试手段,研究不同的生长工艺条件下类金刚石薄膜的性质的变化.实验表明,RF-PECVD生长DLC膜,在上方电极处以及较低功率下可获得较高sp3含量的薄膜.     

光纤陀螺温控技术的研究

温度变化将严重地影响干涉型光纤陀螺仪(IFOG)系统的零位漂移.本文在初步研究温控技术的基础上,给出一种实用的温控电路,以用于在测试温度变化所引起的零位漂移中进行温度调节与控制.通过在此电路下实测的两者变化关系曲线采取补偿措施,以减小光纤陀螺的零位漂移.     

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.