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.

Green synthesis of 1-2 nm gold nanoparticles stabilized by amine-terminated ionic liquid and their electrocatalytic activity in oxygen reduction

Stable gold nanoparticles with average size 1.7 nm synthesized by an amine-terminated ionic liquid showed enhanced electrocatalytic activity and high stability.

Pd nanoparticles supported on WO3/C hybrid material as catalyst for oxygen reduction reaction

Pd nanoparticles supported on WO3/C hybrid material have been developed as the catalyst for the oxygen reduction reaction (ORR) in direct methanol fuel cells. The resultant Pd-WO3/C catalyst has an ORR activity comparable to the commercial Pt/C catalyst and a higher activity than the Pd/C catalyst prepared with the same method. Based on the physical and electrochemical characterizations, the improvement in the catalytic performance may be attributed to the small particle sizes and uniform dispersion of Pd on the WO3/C, the strong interaction between Pd and WO3 and the formation of hydrogen tungsten bronze which effectively promote the direct 4-electron pathway of the ORR at Pd.

Determination of Cd2+ and Pb2+ on glassy carbon electrode modified by electrochemical reduction of aromatic diazonium salts

Carbon modified by the reduction of aromatic diazonium derivatives was first used as electrode for the electrochemical stripping analysis of heavy metals. As a model, the glassy carbon electrode was modified with benzoic acid by electrochemical reduction of diazobenzoic acid, and the resulting modified electrodes were used for determination of Cd2+ and Pb2+. The anodic peak currents of cadmium and lead at the benzoic acid-modified glassy carbon electrode are 7.2 and 6 times of that at the bare glassy carbon electrode. A linear response was observed for Pb2+ and Cd2+ in the range of 0.5-50 mu g/l.

A method for quantitative analysis of nitrogen oxides and N2 by means of mass spectrometry with the assistance of a catalytic technique for the reduction of NO with CO or hydrocarbons

Mass spectrometry is not able to differentiate NOx and N2 from other interferences (e.g. CO and C2H4) in the deNOx reactions. In the present study, a quantitative method for analysis of NOx and N2 simultaneously in these reactions with an assisted converter operated at higher temperature under O2-rich condition, which eliminates the interferences, is developed. The NOx conversion from this method is comparable to the one from an Automotive Emission Analyser equipped with NOx electrochemical sensor. Two types of deNOx reactions are tested in terms of selectivity of N2 production. The application of this method is discussed.

Development of durable carbon black/titanium dioxide supported macrocycle catalysts for oxygen reduction reaction

Carbon black and titanium dioxide supported iron tetraphenylporphyrin (FeTPP/TiO2/C) catalysts for oxygen reduction reaction (ORR) were prepared by sol-gel and precipitation methods followed by a heat-treatment at temperatures of 400-1000 degrees C. The FeTPP/C and TiO2/C were also studied for comparison. The FeTPP/TiO2/C pyrolyzed at 700 degrees C exhibits significantly improved stability while maintaining high activity towards ORR in comparison with the FeTPP/C counterpart. The electrochemical study combined with XRD, XPS, and SEM/EDX analyses revealed that the appropriate dispersion of TiO2 on the surface of FeTPP/TiO2/C catalysts, which depending on heat-treatment temperature, plays a crucial role in determining the activity and stability of catalysts.

PtCeOx/C as a novel methanol-tolerant electrocatalyst of oxygen reduction for direct methanol fuel cells

A prominent methanol-tolerant characteristic of the PtCeOx/C electrocatalyst was found during oxygen reduction reaction process. The carbon-supported platinum modified with cerium oxide (PtCeOx/C) as cathode electrocatalyst for direct methanol fuel cells was prepared via a simple and effective route. The synthesized electrocatalysts were characterized by X-ray diffraction and transmission electron microscopy. It was found that the cerium oxide within PtCeOx/C present in an amorphous form on the carbon support surface and the PtCeOx/C possesses almost similar disordered morphological structure and slightly smaller particle size compared with the unmodified Pt/C catalyst.