二甲醚电氧化及其阳极催化剂研究

Anode electro-catalysts for direct dimethyl-ether fuel cell (DDFC), Pt/C, PtRu/C (1∶1) and PtSn/C (3∶2), were prepared by chemical impregnation-reduction method with formaldehyde as the reductant. DME electro-oxidation and adsorption at Pt electrode and Pt electro-catalysts were investigated by Cyclic Voltammetry(CV), Quasi-steady state polarization and Gas Chromatography(GC). CV showed that there were two current peaks of DME electro-oxidation at Pt electrode around 0.8V (vs RHE); DME was adsorbed at Pt electrode more weakly and slowly than oxygen, methanol, even hydrogen; the onset potential of DME oxidation was 50mV less than that of methanol, and DME peak potential 110 mV lower, thus more advantageous for using in fuel cells than methanol. GC showed that small amount of HCHO was generated during DME electro-oxidation. The mechanism of DME electro-oxidation was proposed. Among the three electro-catalysts (Pt/C, PtRu/C and PtSn/C), Pt alloy catalysts, especially PtRu/C, showed a higher performance toward DME electro-oxidation, as in the case of methanol. Temperature experiments showed that both DME electro-oxidation and adsorption on Pt and Pt alloy catalysts were favored with increased temperature.     

钌—钛金属氧化物涂层电极的电子能谱分析

用X-光电子能谱分析了Ru-Ti金属氧化物电极的组成、结构和元素价态。结果表明,电极表面不同于体内,讨论了表面和体内Ti的价态区别对Ru-Ti阳极活性和寿命的影响,指出掺杂Ir和Pd后会提高电极活性。     

环氧环己烷的电化学合成

环氧环己烷的电化学合成何俊翔＊周锦成（温州师范学院化学系温州３２５００３）关键词电化学环氧化，环氧环己烷，环己烯，钛基氧化物电极１９９６－１１－２２收稿，１９９７－０６－０５修回间接电解氧化合成环氧化合物的研究已有报道［１～４］．它们均在铂电极上完成...     

电化学合成镍配合物的研究

采用金属镍为“牺牲”阳极，首次在无隔膜电解槽中，电化学溶解金属镍一步 制备了纳米NiO前驱体Ni(Oet)2,Ni(Obu)2,Ni(Oet)2(acac)2，Ni(Obu)2(acac)2 [acac为乙酰丙酮基].产物通过红外光谱（FT-IR）、拉曼光谱（Raman spectrum） 进行表征。同时讨论了影响电合成镍醇盐及其配合物的关键因素。实验表明，防止 阳极钝化，温度控制在30-40℃，采用有机胺溴化物为导电盐，可以提高电合成效 率。     

电化学溶解金属铜制备纳米CuO

有机电解合成;铜电极;水解;电化学溶解金属铜制备纳米CuO     

电化学合成铜配合物的研究

采用金属铜为"牺牲"阳极,首次在无隔膜电解槽中,电化学溶解金属铜一步制备了纳米CuO前体Cu(OEt)2, Cu(OBu)2, Cu(acac)2, Cu(OEt)(acac), Cu(OBu)(acac) (acac为乙酰丙酮基).产物通过红外光谱(FT-IR)、拉曼光谱(Raman spectrum)进行表征.同时讨论了影响电合成铜醇盐及其配合物的关键因素.实验表明,防止阳极钝化,温度控制在30～50 ℃,采用有机胺溴化物为导电盐, 电极电位控制在0.8～1.2 V之间,可以提高电合成效率.实验同时表明Cu(acac)2, Cu(OEt)(acac), Cu(OBu)(acac)可作为制备含铜纳米材料前驱体.     

锂离子电池用多孔硅/石墨/碳复合负极材料的研究

在两步高能球磨和酸蚀条件下制得了多孔硅/石墨复合材料，并对其进行碳包覆制成多孔硅/石墨/碳复合材料。通过TEM，SEM等测试手段研究了多孔硅材料的结构。作为锂离子电池负极材料，电化学测试结果表明多孔硅/石墨/碳复合材料相比纳米硅/石墨/碳复合材料有更好的循环稳定性。同时，改变复合体配比、热解碳前驱物、粘结剂种类和用量也会对材料的电化学性能产生较大的影响。其中使用质量分数为10％的LA132粘结剂的电极200次循环以后充电容量保持在649.9 mAh·g^-1，几乎没有衰减。良好的电化学性能主要归因于主活性体-多孔硅颗粒中的纳米孔隙很好地抑制了嵌锂过程中自身的体积膨胀，而且亚微米石墨颗粒和碳的复合也减轻了电极材料的体积效应并改善了其导电性。     

Electrolytic Co3O4 for thin-layer anodes of lithium-ion batteries

Electrolytic (e) cobalt oxide of a spinel structure, e-Co₃O₄, is obtained from the sulfate and nitrate (aqueous, water-alcohol) solutions containing Co²⁺ with the aim of using it in thin-layer anodes of lithium-ion batteries. The physicochemical and structural properties of the synthesized compounds are examined using thermal and x-ray diffraction analyses, absorption IR spectroscopy, and atomic force microscopy. The electrochemical characteristics of e-Co₃O₄ are determined in breadboards of lithium power sources and in the lithiumion system LiCoO₂/e-Co₃O₄.     

添加剂对SiO电性能的影响及其机理分析

SiO_x/CoO and SiO/Li₂CO₃ composite materials were prepared by mechanical ball-milling. The structures of the obtained materials were characterized by X-ray diffraction (XRD). And scanning electron microscopes (SEM) of three samples after 20 cycles were also given. In addition， the electrochemical performances of three materials with galvanostatic charge-discharge cycling were investigated. The results show that the composite samples have larger initial reversible capacities and better cycle performance than pure SiO. Also，a schematic diagram showing the buffer effects of Li₂CO₃ addition and the mechanism of improving electrochemical performance by adding Li₂CO₃ are suggested.     

Improving (Q)SAR predictions by examining bias in the selection of compounds for experimental testing

ABSTRACT

Existing data on structures and biological activities are limited and distributed unevenly across distinct molecular targets and chemical compounds. The question arises if these data represent an unbiased sample of the general population of chemical-biological interactions. To answer this question, we analyzed ChEMBL data for 87,583 molecules tested against 919 protein targets using supervised and unsupervised approaches. Hierarchical clustering of the Murcko frameworks generated using Chemistry Development Toolkit showed that the available data form a big diffuse cloud without apparent structure. In contrast hereto, PASS-based classifiers allowed prediction whether the compound had been tested against the particular molecular target, despite whether it was active or not. Thus, one may conclude that the selection of chemical compounds for testing against specific targets is biased, probably due to the influence of prior knowledge. We assessed the possibility to improve (Q)SAR predictions using this fact: PASS prediction of the interaction with the particular target for compounds predicted as tested against the target has significantly higher accuracy than for those predicted as untested (average ROC AUC are about 0.87 and 0.75, respectively). Thus, considering the existing bias in the data of the training set may increase the performance of virtual screening.