Activated carbon supported bimetallic CoMo carbides synthesized by carbothermal hydrogen reduction

A carbothermal hydrogen reduction method was employed for the preparation of activated carbon supported bimetallic carbide. The resultant samples were characterized by BET surface area measurement, X-ray diffraction, and temperature-programmed reduction-mass spectroscopy. The results showed that nanostructured beta-Mo2C can be formed on the activated carbon by carbothermal hydrogen reduction above 700 degreesC. The particle sizes of beta-Mo2C increase with increasing reaction temperatures and Mo loading. The bimetallic CoMo carbide can be synthesized by the carbothermal hydrogen reduction even around 600 degreesC. The bimetallic CoMo carbide is from carbothermal hydrogen reduction of CoMoO4 precursor and is easily formed when the Co/Mo molar ratio is 1.0. Separation of the bimetallic CoMo carbide phase into Mo carbide and Co metal occurs when the temperature of the reduction is above 700 degreesC. The addition of a second metal such as Co and Ni, decreases the formation temperature of carbide because the second metal promotes formation of CHx species from reactive carbon atoms or groups on carbon material and hydrogen, which further carburizes oxide precursors. (C) 2003 Elsevier Science Ltd. All rights reserved.

Photocatalytic water reduction under visible light on a novel ZnIn2S4 catalyst synthesized by hydrothermal method

A novel ZnIn2S4 catalyst synthesized by hydrothermal method shows high and stable photocatalytic activity for water reduction under visible light illumination.

The synthesis of nanostructured W2C on ultrahigh surface area carbon materials via carbothermal hydrogen reduction

Nanostructured tungsten carbides were synthesized, for the first time, with a size distribution of 5-12 nm on ultrahigh surface area carbon material, by carbothermal hydrogen reduction (CHR) at 850degreesC and metal Ni promoted CHR at 650 degreesC.

A Mossbauer study of In-Fe2O3/HZSM-5 catalysts for the selective catalytic reduction of NO by methane

The selective catalytic reduction of NO by CH4 was compared over In-Fe2O3/HZSM-5 catalysts prepared by impregnation and co-impregnation methods. It was found that the catalyst preparation method greatly affected the catalyst activity. The impregnated catalyst was very active, but the co-impregnated one showed poor activity. The In Fe2O3/HZSM-5 catalysts were investigated by Mossbauer spectroscopy. The results showed that indium cations entered into the iron oxide lattice in the co-impregnated catalyst, while the impregnated catalyst exhibited a more stable structure, when both of the catalysts were treated severely in the reaction atmosphere. Characterization by means of combined in situ temperature programmed reduction (TPR)- Mossbauer spectroscopy further revealed that the performances of the two catalysts were different in the TPR processes.

Water reduction and oxidation on Pt-Ru/Y2Ta2O5N2 catalyst under visible light irradiation

Y2Ta2O5N2 is presented as a novel photocatalyst with high activity for water splitting under visible-light irradiation in the presence of appropriate sacrificial reagents; the activity for reduction to H-2 is increased by the incorporation of Pt or Ru as a co-catalyst, with a significant increase in production efficiency when both Pt and Ru are present.

Control of cocklebur seed germination by nitrogenous compounds : Nitrite, nitrate, hydroxylamine, thiourea, azide and cyanide

Germination of non-dormant upper cocklebur (Xanthium pinsylvanicum Wallr.) seeds was stimulated by not only CS(NH2)2 but also NH2OH, KCN and NaN3. This stimulation was not via the enhancement of aerobic C2H4 production. NH2OH, KCN and NaN3 in certain concentrations promoted the initial growth of axial and/or cotyledonary parts, but the degree of growth promotion by NH2OH, NaN3 and KCN was slight compared with that by CS(NH2)2. As in the case of CS(NH2)2, however, the germinationstimulating effect of NH2OH disappeared rapidly as the preceding imbibition period was prolonged. In contrast, KCN and NaN3 were still effective in stimulating the germination of aged seeds maintained on a water substratum, as previously seen with anaerobiosis. Anaerobic induction was enhanced not only by NaN3 and KCN but also by NH2OH, KNO3, KNO2 CO(NH2)2 and CS(NH2)2 applied during the anaerobic treatment, but without causing an increase in anaerobic production of C2H4. Furthermore, KCN and NaN3, given prior to the anaerobic treatment acted additively with anaerobic induction. The germination-stimulating actions of nitrogenous compounds are discussed in comparison with those of C2H4 and anaerobiosis.

Potential CO2 Emission Reduction by Development of Non-Grain-Based Bioethanol in China

Assessment of the potential CO2 emission reduction by development of non-grain-based ethanol in China is valuable for both setting up countermeasures against climate change and formulating bioethanol policies. Based on the land occupation property, feedstock classification and selection are conducted, identifying sweet sorghum, cassava, and sweet potato as plantation feedstocks cultivated from low-quality arable marginal land resources and molasses and agricultural straws as nonplantation feedstocks derived from agricultural by-products. The feedstock utilization degree, CO2 reduction coefficient of bioethanol, and assessment model of CO2 emission reduction potential of bioethanol are proposed and established to assess the potential CO2 emission reduction by development of non-grain-based bioethanol. The results show that China can obtain emission reduction potentials of 10.947 and 49.027 Mt CO2 with non-grain-based bioethanol in 2015 and 2030, which are much higher than the present capacity, calculated as 1.95 Mt. It is found that nonplantation feedstock can produce more bioethanol so as to obtain a higher potential than plantation feedstock in both 2015 and 2030. Another finding is that developing non-grain-based bioethanol can make only a limited contribution to China's greenhouse gas emission reduction. Moreover, this study reveals that the regions with low and very low potentials for emission reduction will dominate the spatial distribution in 2015, and regions with high and very high potentials will be the majority in 2030.

Semisupervised Dimensionality Reduction and Classification Through Virtual Label Regression

Semisupervised dimensionality reduction has been attracting much attention as it not only utilizes both labeled and unlabeled data simultaneously, but also works well in the situation of out-of-sample. This paper proposes an effective approach of semisupervised dimensionality reduction through label propagation and label regression. Different from previous efforts, the new approach propagates the label information from labeled to unlabeled data with a well-designed mechanism of random walks, in which outliers are effectively detected and the obtained virtual labels of unlabeled data can be well encoded in a weighted regression model. These virtual labels are thereafter regressed with a linear model to calculate the projection matrix for dimensionality reduction. By this means, when the manifold or the clustering assumption of data is satisfied, the labels of labeled data can be correctly propagated to the unlabeled data; and thus, the proposed approach utilizes the labeled and the unlabeled data more effectively than previous work. Experimental results are carried out upon several databases, and the advantage of the new approach is well demonstrated.

Supervised Gaussian Process Latent Variable Model for Dimensionality Reduction

The Gaussian process latent variable model (GP-LVM) has been identified to be an effective probabilistic approach for dimensionality reduction because it can obtain a low-dimensional manifold of a data set in an unsupervised fashion. Consequently, the GP-LVM is insufficient for supervised learning tasks (e. g., classification and regression) because it ignores the class label information for dimensionality reduction. In this paper, a supervised GP-LVM is developed for supervised learning tasks, and the maximum a posteriori algorithm is introduced to estimate positions of all samples in the latent variable space. We present experimental evidences suggesting that the supervised GP-LVM is able to use the class label information effectively, and thus, it outperforms the GP-LVM and the discriminative extension of the GP-LVM consistently. The comparison with some supervised classification methods, such as Gaussian process classification and support vector machines, is also given to illustrate the advantage of the proposed method.

Carbon nanotubes decorated with Pt nanocubes by a noncovalent functionalization method and their role in oxygen reduction

Combined with polymer wrapping and layer-by-layer techniques, a noncovalent functionalization method is developed to disperse Pt nanocubes (NCs) onto carbon nanotubes (CNTs). By adjusting the relative ratio of Pt NCs to CNTs, nanotubes with different Pt NC loadings are produced. The composites exhibit excellent electrocatalytic activity towards oxygen reduction.

Carbon-supported Pd nanocatalyst modified by non-metal phosphorus for the oxygen reduction reaction

A carbon-supported palladium catalyst modified by non-metal phosphorus(PdP/C) has been developed as an oxygen reduction catalyst for direct methanol fuel cells.The PdP/C catalyst was prepared by the sodium hypophosphite reduction method. The as-prepared Pd nanoparticles have a narrow size distribution with an average diameter of 2 nm. Energy dispersive X-ray analysis (EDX), X-ray photoelectron spectroscopy (XPS), and X-ray diffraction (XRD) results indicate that P enters into the crystal lattice of Pd and forms an alloy.

The synthesis of ionic-liquid-functionalized multiwalled carbon nanotubes decorated with highly dispersed Au nanoparticles and their use in oxygen reduction by electrocatalysis

Multiwalled carbon nanotube (MWCNT)/ionic liquid/gold nanoparticle hybrid materials have been prepared by a chemical route that involves functionalization of MWCNT with amine-terminated ionic liquids followed by deposition of Au. Transmission electron microscopy revealed well-distributed Au with a narrow size distribution centered around 3.3 nm. The identity of the hybrid material was confirmed through Raman and X-ray photoelectron spectroscopy.