Investigation on the catalytic roles of silver species in the selective catalytic reduction of NOx with methane

Silver impregnated H-ZSM-5 zeolite catalysts with silver loading from 3 to 15 wt.% were investigated for the selective catalytic reduction (SCR) of NOx with CH4 in the excess of oxygen. X-ray diffraction (XRD) and UV-Vis measurements established the structure of silver catalysts. A relationship between the structure of silver catalysts and their catalytic functions for the SCR of NOx by CH4 was clarified. The NO conversion to N-2 showed a S-shape dependence on the increase of Ag loading. No linear dependence of catalytic activity on the amount of silver ions in the zeolite cation sites was observed. Contrastively, the activity was significantly enhanced by the nano-sized silver particles formed on the higher Ag loading samples (greater than or equal to7 wt.%). Temperature programmed desorption (TPD) and temperature programmed reduction (TPR) studies showed that nano-silver particles provided much stronger adsorption centers for active intermediates NO3-(s) on which adsorbed NO3-(s) could be effectively reduced by the activated methane. Silver ions in the zeolite cation sites might catalyze the reaction through activation of CH4 at lower temperatures. Activated CH4 reacted with NO3-(s) adsorbed on nano-silver particles to produce N-2 and CO2. (C) 2003 Elsevier B.V. All rights reserved.

Reduction property and catalytic activity of Ce1-XNiXO2 mixed oxide catalysts for CH4 oxidation

Ce1-XNiXO2 oxides with X varying from 0.05 to 0.5 were prepared by different methods and characterized by XRD and TPR techniques. Ce(0.7)Mi(0.3)O(2) sample prepared by sol-gel method shows the highest reducibility and the highest catalytic activity for methane combustion. Three kinds of Ni phases co-exist in the Ce1-XNiXO2 catalysts prepared by sol-gel method: (i) aggregated NiO on the support CeO2, (ii) highly dispersed NiO with strong interaction with CeO2 and (iii) Ni atoms incorporated into CeO2 lattice. The distribution of different Ni species strongly depends on the preparation methods. The highly dispersed NiO shows the highest activity for methane combustion. The NiO aggregated on the support CeO2 shows lower catalytic activity for methane combustion, while the least catalytic activity is found for the Ni species incorporated into CeO2. Any oxygen vacancy formed in CeO2 lattice due to the incorporating of Ni atoms adsorbs and activates the molecular oxygen to form active oxygen species. So the highest catalytic activity for methane combustion on Ce0.7Ni0.3O2 catalyst is attributed not only to the highly dispersed Ni species but also to the more active oxygen species formed. (C) 2002 Elsevier Science B.V. All rights reserved.

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.

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.

Enzyme Colorimetric Assay Using Unmodified Silver Nanoparticles

Colorimetric assay based on the unique surface plasmon resonance properties of metallic nanoparticles has received considerable attention in bioassay due to its simplicity, high sensitivity, and low cost. Most of colorimetric methods previously reported employed gold nanoparticles (GNPs) as sensing elements. In this work, we develop a sensitive, selective, simple, and label-free colorimetric assay using unmodified silver nanoparticle (AgNP) probes to detect enzymatic reactions. Enzymatic reactions concerning adenosine triphosphate (ATP) dephosphorylation by calf intestine alkaline phosphatase (CLAP) and peptide phosphorylation by protein kinase A (PKA) were studied.