聚4-乙烯吡啶基碱性膜的制备及性能分析

采用自由基聚合制备聚4-乙烯吡啶(P4VP)。引入溴乙醇(BE)对制备的P4VP进行季铵化处理,合成一系列不同季铵化取代度的季铵化聚4-乙烯吡啶(QPVPE),最后,引入戊二醛(GA)与QPVPE进行交联,制备得到适合燃料电池使用的交联型聚4-乙烯吡啶基碱性膜。实验过程中,借助傅里叶变换红外光谱(FTIR)、紫外光谱(UV)、扫描电子显微镜(SEM)、热重分析(TGA,DTG)及力学性能测试等分析手段,对得到的导电膜结构及其理化性能进行分析,并对其导电性能进行性能测试。结果表明,QPVPE77.6%-GA16.87%膜综合性能最佳,该膜的电导率为10.48 mS/cm、含水率(WU)86.4%、溶胀率(SR)66.8%、拉伸强度为23.97 MPa。在25℃条件下,于1 mol/L的KOH溶液中浸泡192 h后,电导率损失为19%。因此,该膜具有较好的耐碱性能。     

多级孔HZSM-5分子筛催化快速热解生物质制芳烃

采用酸和/或碱处理法制备了一系列多级孔HZSM-5分子筛，采用XRD、N₂吸附、XRF、TEM、²⁷Al MAS NMR和NH₃-TPD等表征手段对其孔道结构和酸性进行表征。表征结果表明，采用碱处理方法，可获得孔径集中于3~6 nm的介孔结构，通过改变酸、碱处理次序，可调变酸中心数量和强酸/总酸中心比例。在Py-GC/MS装置上，以纤维素和水稻秸秆为原料，研究多级孔分子筛结构对催化快速热解（CFP）制芳烃反应的影响。反应评价结果表明，同商品级HZSM-5相比，采用先碱后酸处理获得的多级孔HZSM-5分子筛（HZ-OH/H），可将纤维素CFP芳烃碳产率由32.3%提高至43.6%，可将水稻秸秆CFP芳烃碳产率由23.0%提高至30.8%。多级孔HZ-OH/H分子筛的孔道结构和酸中心分布特征，对开发应用于生物质制芳烃的高效工业催化剂具有借鉴意义。     

Hydrogen-rich syngas production and carbon dioxide formation using aqueous urea solution in biogas steam reforming by thermodynamic analysis

Biogas is a renewable biofuel that contains a lot of CH₄ and CO₂. Biogas can be used to produce heat and electric power while reducing CH₄, one of greenhouse gas emissions. As a result, it has been getting increasing academic attention. There are some application ways of biogas; biogas can produce hydrogen to feed a fuel cell by reforming process. Urea is also a hydrogen carrier and could produce hydrogen by steam reforming. This study then employes steam reforming of biogas and compares hydrogen-rich syngas production and carbon dioxide with various methane concentrations using steam and aqueous urea solution (AUS) by Thermodynamic analysis. The results show that the utilization of AUS as a replacement for steam enriches the production of H₂ and CO and has a slight CO₂ rise compared with pure biogas steam reforming at a temperature higher than 800 °C. However, CO₂ formation is less than the initial CO₂ in biogas. At the reaction temperature of 700 °C, carbon formation does not occur in the reforming process for steam/biogas ratios higher than 2. These conditions led to the highest H₂, CO production, and reforming efficiency (about 125%). The results can be used as operation data for systems that combine biogas reforming and applied to solid oxide fuel cell (SOFC), which usually operates between 700 °C to 900 °C to generate electric power in the future.     

Polishing pad for reducing edge roll-off while maintaining good global flatness of silicon wafer

In the double-sided polishing process of silicon wafers, there is a strong demand to reduce amount of edge roll-off (ERO) while improving global flatness of a wafer. In the present study, we clarified the negative effects of uneven wear of the polishing pads on the global flatness of a wafer can be suppressed when the deformation of the polishing pads is large. As for the ERO, we found small deformation of the polishing pad near the top surface was effective in reducing the amount of ERO. In addition, we revealed small distance from the surface of the polishing pad at the area under the wafer to that at the area around the wafer was also effective in reducing the amount of ERO. On the basis of the findings, we developed a three-layered polishing pad which was expected to reduce the amount of ERO while achieving the good global flatness.     

Ternary metal sulfides MoCoNiS derived from metal organic frameworks for efficient oxygen evolution

Developing a highly active and low-cost non-precious metal electrocatalyst for oxygen evolution has been urgent for the clean energy system. Herein, the ternary metal sulfides MoCoNiS supported on nickel foam (MoCoNiS/NF) are successfully prepared using Mo doping Co-based metal-organic framework (Co-MOF) as precursor, which may be helpful for the good dispersion of different metal element. The uniform elements distribution of Mo, Co and Ni on MoCoNiS/NF is determined by all kinds of physical characterization. Mo doping may regulate the electronic environment around Co and Ni, suggesting the potential synergistic effects between different heteroatoms. Electrochemical test shows that MoCoNiS/NF exhibits the excellent OER activity than other single metal or binary metal sulfides as comparison samples, needing only 151 and 226 mV overpotential to achieve current density of 10 (η₁₀ = 151 mV) and 100 mA cm⁻² (η₁₀₀ = 226 mV), respectively. The excellent stability of MoCoNiS/NF has been achieved. The remarkable OER performance of MoCoNiS/NF may due to the synergistic effects and good electrical conductivity as well as the three-dimensional structure of NF as substrate. Therefore, the rational design of MOF derived multi transition metal-based electrocatalysts will be an effective way for increasing OER performance.     

稠油地下改质开采技术及发展趋势

稠油作为全球重要的非常规原油资源，是保障我国能源安全、重大工程需求的重要资源。目前常规的热采稠油油藏陆续进入开采后期，高能耗、高污染、高成本问题日趋严重，亟需依靠技术换代实现开发方式升级。稠油地下改质是通过向油藏中注入改质催化剂，使其与稠油发生化学反应，实现稠油地下不可逆降黏并高效采出的一种开采方式，是近十年来最受瞩目的下一代稠油开采技术之一。本文从技术机理、改质催化剂及开采效果影响因素三方面阐述了技术内涵，通过系统调研国内外相关学者和企业的代表性成果，按照催化剂种类、反应温度和降黏效果等进行综合性分类统计，对比了现有矿场试验的开采方式和采油效果，指出制约技术应用的两个关键问题，并展望了技术未来发展方向。     

To pursue FexCoy-PANI/CNT catalysts for oxygen reduction reaction in acid medium with controlled molecular self-assembly method

The mainstream of pyrolyzed transitional metal-nitrogen-carbon (M-N-C) catalysts for ORR still confront difficulty in PEMFC application. To pursue M-N-C structure from wet chemistry at ambient temperature, this paper prepares Fe_xCo_y-PANI/CNT porous structures composed of amorphous Fe and Co NPs into PANI layer on CNT surface, supported by the controlled molecular self-assembly mechanism (MS). For their ORR behaviors in acid medium, all Fe_xCo_y-PANI/CNT catalysts demonstrate similar features as Pt-based catalyst in low current density region, and 4e pathway and active sites in pore utilization in high current density region. Specifically, we disclosed nitrogen in PANI matrix dominates specific activity for ORR, and a little transitional metal attain mass activity at maximum. The active sites mounted into PANI matrix and 4e pathway help catalysts to achieve high durability. Thus, we extend a new type of platinum-free catalyst and develop a bottom-up approach for preparation-structure-activity, expecting to drive PEMFC remarkably.     

Fabrication of platinum thin films for ultra-high electrocatalytic hydrogen evolution reaction

While the noble metals (e.g., platinum, (Pt)) remain the benchmark electrocatalyst for the hydrogen evolution reaction (HER), their mass production require a reduced metal loading and faster fabrication protocols. The aim of the present work is to prepare Pt thin films by simple and fast fabrication technique, and to evaluate their performance for HER. The thin films of Pt are grown on two substrates, namely titanium foil (Ti) and nickel foam (NF), using a single step aerosol assisted chemical vapor deposition (AACVD) method. The film deposition time are varied from 20 to 60 min. Microscopic analyses suggest a gradual evolution of the films into percolated and/or porous nanostructures, a feature that remains highly desired to allow the maximum access of active sites. The performance of the as-prepared electrodes is evaluated by monitoring the HER in acidic electrolyte. The Pt film on nickel foam (Pt/NF) exhibits better electrical conductivity and smaller charge transfer resistance, while the film deposited on the Ti foil (Pt/Ti) demonstrates superior catalytic activity per active sites. The as-prepared Pt/Ti and Pt/NF electrodes produce 10 mA cm⁻² at overpotential of 28 mV and 26 mV, respectively, better in performance than commercial Pt/C electrode (~39 mV), set a new bench mark electrocatalyst for the HER.     

Framework to manage multiple goals in community-based energy sharing network in smart grid

Smart grid has opened up a new role of “prosumer” in an energy value network, transforming many conventional energy consumers into prosumers, who not only generate green energy but also share the surplus with utilities and other consumers. The concept of a goal-oriented prosumer community group (PCG) has emerged recently as an effective way to fulfill sustainable energy exchange. Such community-based energy sharing networks comprise multiple irreconcilable objectives such as demand constraints, cost constraints, and income maximization. In many cases, one goal may be achievable only at the expense of other goals. This necessitates the development of an effective framework to manage the multiple goals and reduce the gap with their achievement levels. Therefore, in this research paper, an effective framework is developed to negotiate among the multiple goals and thus to define optimal mutual goals for each PCG in a more sustainable manner using multiple-criteria goal programming techniques. Simulation results are presented to illustrate how the methods work in practical situations, where each of the objective measure is given a target value and the unwanted deviations from this set are minimized in an achievement function.     

Multi-mode data augmentation and fault diagnosis of rotating machinery using modified ACGAN designed with new framework

As one of the representative unsupervised data augmentation methods, generative adversarial networks (GANs) have the potential to solve the problem of insufficient samples in fault diagnosis of rotating machinery. However, the existing unsupervised GANs are usually incapable of simultaneously generating multi-mode fault samples and have some shortcomings such as mode collapse and gradient vanishing. To overcome these deficiencies, a supervised model called modified auxiliary classifier GAN (MACGAN) designed with new framework is proposed in this paper. Firstly, a new ACGAN framework is developed by adding an independent classifier to improve the compatibility between the classification and discrimination. Secondly, the Wasserstein distance is introduced in the new loss functions to overcome mode collapse and gradient vanishing. Finally, to achieve stable training, a spectral normalization is used to replace the weight clipping to constrain the weight parameters of discriminator. The proposed method is applied to fault diagnosis of bearing and gear. Compared with the existing GANs, the proposed method can more efficiently generate multi-mode fault samples with higher qualities, which can be used to assist the training of deep learning-based fault diagnosis models with high accuracy and good stability.