Theoretical investigation of solvent effects on the selective hydrogenation of furfural over Pt(111)

It was well known that solvent effect plays a very important role in the catalytic reaction. There are many theoretical studies on the solvent effect in homogeneous catalysis while there are few theoretical studies on the solvent effect in the heterogeneous catalytic reaction and there has been no work to investigate the solvent effect on furfural transformation in heterogeneous catalysis. In the present work, both the density functional calculations and the microkinetic analysis were performed to study the selective hydrogenation of furfural over Pt(111) in the presence of methanol as well as toluene and compared with that in the gas condition. The present results indicated that the methanol can enhance the adsorption strength of furfural and other oxygen-containing reaction species due to its relatively strong polarity properties and this can be a main reason for solvent-induced high activity and selectivity. Another reason is that reaction paths study showed that the presence of methanol solvent makes the dehydrogenation of furfural less thermochemical due to the fact that furfural is more stabilized than that of dehydrogenation species, and methanol also has an inhibition effect on the dehydrogenation of furfural in the kinetic aspect, and further energetic span theory proves highest activity and selectivity for hydrogenation in methanol solvent of vapor, methanol and toluene. Moreover, microkinetic model simulation demonstrated that the activity and selectivity of hydrogenation in methanol is both higher than that in vapor and toluene. The much higher activity in methanol is due to the stabilized adsorbed reactants in the surface, which leads to a higher surface coverage of furfural. It might be proposed based on the present work that a solvent with relatively strong polarity may be favorable for the high selective hydrogenation of furfural.     

Performance investigation on a novel 3D wave flow channel design for PEMFC

Flow field structure can largely determine the output performance of Polymer electrolyte membrane fuel cell. Excellent channel configuration accelerates electrochemical reactions in the catalytic layer, effectively avoiding flooding on the cathode side. In present study, a three-dimensional, multi-phase model of PEMFC with a 3D wave flow channel is established. CFD method is applied to optimize the geometry constructions of three-dimensional wave flow channels. The results reveal that 3D wave flow channel is overall better than straight channel in promoting reactant gases transport, removing liquid water accumulated in microporous layer and avoiding thermal stress concentration in the membrane. Moreover, results show the optimal flow channel minimum depth and wave length of the 3D wave flow channel are 0.45 mm and 2 mm, respectively. Due to the periodic geometric characteristics of the wave channel, the convective mass transfer is introduced, improving gas flow rate in through-plane direction. Furthermore, when the cell output voltage is 0.4 V, the current density in the novel channel is 23.8% higher than that of conventional channel.     

Fabrication of hierarchical PANI@W-type barium hexaferrite composites for highly efficient microwave absorption

Hexagonal barium ferries is a promising and efficient microwave (MW) absorbing material, but the low dielectric loss and poor conductivity have limited their extensive applications. In this work, a simple tactic of coating conductive polymer PANI on hexaferrite BaCo₂Fe₁₆O₂₇ is presented, wherein the dielectric properties are customized, and more significantly, the electromagnetic loss is greatly enhanced. As displayed from structural characterizations, PANI were coated equably on the surface of hexaferrite grains by an in-situ polymerization process. The outcomes exhibit the as-prepared PANI@hexaferrite composite has remarkable electromagnetic wave absorption capacity. When the thickness is 6.0 mm, the minimal RL of ?40.4 dB was achieved at 2.9 GHz. The effective absorption bandwidth (RL < ?20 dB) of 0.65 GHz, 0.53 GHz, 0.65 GHz, 0.52 GHz, 0.46 GHz and 0.39 GHz was achieved separately when the thickness ranges from 4 to 9 mm. The highly efficient MW absorbing performance of PANI@hexaferrite composite were the consequence of multiple loss mechanisms and perfect impedance matching. It is demonstrated that the PANI@hexaferrite composite with excellent MW absorption performance is expected to be potential MW absorbers for extensive applications.     

赤红球菌组氨酸激酶的融合表达和功能分析

对赤红球菌的组氨酸激酶基因进行密码子优化，将优化后的组氨酸激酶基因（rhks）构建重组表达质粒pGEX-4T-2-rhks。将此质粒导入到大肠杆菌BL21（DE3）中进行异源表达。在25 ℃和1 mmol/L异丙基-β-D-硫代吡喃半乳糖苷诱导条件下，组氨酸激酶融合蛋白（GST-RHK）获得成功表达，并具有催化活性。经谷胱甘肽琼脂糖亲和层析纯化，获得电泳纯的GST-RHK，其中纯化倍数为3.1，得率为19.5%。该蛋白大小约为72.75 kDa，Km、Vmax和Kcat值分别为20.92 μmol/L、0.17 μmol/（L·min）和1.4 min－1。野生型赤红球菌、组氨酸激酶基因增强株sdrhkE和组氨酸激酶基因敲减株sdrhkD在分别含有苯酚、甲苯、氯苯、异辛烷4 种有机溶剂的培养基中培养，菌株sdrhkD的生长情况都优于野生型赤红球菌，菌株sdrhkE的生长情况都低于野生型赤红球菌。本研究为进一步揭示赤红球菌SD3中组氨酸激酶涉及的信号转导途径与赤红球菌有机溶剂耐受性的关联机制提供一定参考依据。     

Wearing prediction of stellite alloys based on opposite degree algorithm

In order to predict the wearing of stellite alloys,the related methods of rare metals data processing were discussed. The method of opposite degree(OD) algorithm was put forward to predict the wearing of stellite alloys.OD algorithm is based on prior numerical data, posterior numerical data and the opposite degree between numerical forecast data. To compare the performance of predicted results based on different algorithms, the back propagation(BP) and radial basis function(RBF) neural network methods were introduced. Predicted results show that the relative error of OD algorithm is smaller than those of BP and RBF neural network methods. OD algorithm is an effective method to predict the wearing of stellite alloys and it can be applied in practice.     

综采工作面安全快速过逆断层群的技术研究与应用

综采工作面在推进过程中经常会受到断层的影响，严重制约了矿井的安全生产及经济效益。以寺河煤矿西井区W2303工作面采用留顶煤、割底矸等手段过逆断层群为工程实例，分析了综采工作面安全、快速过逆断层群技术的应用效果，对于类似矿山也有一定的借鉴价值。     

Distributed learning consensus control based on neural networks for heterogeneous nonlinear multiagent systems

This paper considers a novel distributed iterative learning consensus control algorithm based on neural networks for the control of heterogeneous nonlinear multiagent systems. The system's unknown nonlinear function is approximated by suitable neural networks; the approximation error is countered by a robust term in the control. Two types of control algorithms, both of which utilize distributed learning laws, are provided to achieve consensus. In the provided control algorithms, the desired reference is considered to be an unknown factor and then estimated using the associated learning laws. The consensus convergence is proven by the composite energy function method. A numerical simulation is ultimately presented to demonstrate the efficacy of the proposed control schemes.     

Study on wax precipitation characteristics of wax deposit in pipes

Wax deposit properties are a significant concern in pipeline pigging during waxy crude oil transportation. In the present work, the impacts of flow conditions and oil properties on the wax precipitation characteristics of wax deposits are investigated. A flow loop apparatus was developed to conduct wax deposition experiments using four crude oils collected from different field pipes. The differential scanning calorimetry (DSC) technique was employed to observe the wax precipitation characteristics of crude oil and wax deposit. The results show that the wax content and the wax appearance temperature (WAT) of the deposits increase with shear stress and radial temperature gradient, and decrease with radial wax molecule concentration gradient near the pipe wall. The DSC tests on the wax deposits revealed that the deposit wax content is strongly correlated to the oil wax content. Furthermore, an empirical correlation was developed to predict the wax content and the WAT of the wax deposit. Verification of the empirical correlation using the different oils indicated that the average relative error of the wax content prediction and average absolute error of WAT prediction were 13.2% and 3.6°C, respectively.