Density Functional Theory Studies of Ethanol Decomposition on Rh(211)

Density functional theory calculations were carried out to examine the mechanism of ethanol decomposition on the Rh(211) surface. We found that there are two possible decomposition pathways: (1) CH(3)CH(2)OH -> CH(3)CHOH -> CH(3)COH -> CH(3)CO -> CH(3) + CO -> CH(2) + CO -> CH + CO -> C + CO and (2) CH(3)CH(2)OH -> CH(3)CHOH -> CH(3)COH -> CH(2)COH -> CHCOH -> CHCO -> CH + CO -> C + CO. Both pathways have a common intermediate of CH(3)COH, and the key step is the formation of CH(3)CHOH species. According to our calculations, the mechanism of ethanol decomposition on Rh(211) is totally different from that on Rh(111): the reaction proceeds via CH(3)COH rather than an oxametallacycle species (-CH(2)CH(2)O- for Rh( 111)), which implies that the decomposition process is structure sensitive. Further analyses on electronic structures revealed that the preference of the initial C(alpha)-H path is mainly due to the significant reduction of d-electron energy in the presence of the transition state (TS) complex, which may stabilize the TS-surface system. The present work first provides a clear picture for ethanol decomposition on stepped Rh(211), which is an important first step to completely understand the more complicated reactions, like ethanol steam reforming and electrooxidation.

Hierarchical Demand Response for Peak Reduction using Dantzig‐Wolfe Decomposition

Demand Response (DR) algorithms manipulate the energy consumption schedules of controllable loads so as to satisfy grid objectives. Implementation of DR algorithms using a centralised agent can be problematic for scalability reasons, and there are issues related to the privacy of data and robustness to communication failures. Thus it is desirable to use a scalable decentralised algorithm for the implementation of DR. In this paper, a hierarchical DR scheme is proposed for Peak Minimisation (PM) based on Dantzig-Wolfe Decomposition (DWD). In addition, a Time Weighted Maximisation option is included in the cost function which improves the Quality of Service for devices seeking to receive their desired energy sooner rather than later. The paper also demonstrates how the DWD algorithm can be implemented more efficiently through the calculation of the upper and lower cost bounds after each DWD iteration.

Interactions between ectomycorrhizal fungi and soil saprotrophs:Implications for decomposition of organic matter in soils and degradation of organic pollutants in the rhizosphere

Ectomycorrhizal fungi and saprotrophic microorganisms coexist and interact in the mycorrhizosphere. We review what is known regarding these interactions and how they may influence processes such as ectomycorrhiza formation, mycelial growth, and the dynamics of carbon movement to and within the rhizosphere. Particular emphasis is placed on the potential importance of interactions in decomposition of soil organic matter and degradation of persistant organic pollutants in soil. While our knowledge is currently fairly limited, it seems likely that interactions have profound effects on mycorrhizosphere processes. More extensive research is warranted to provide novel insights into mycorrhizosphere ecology and to explore the potential for manipulating the ectomycorrhizosphere environment for biotechnological purposes.

A further investigation of the kinetic demixing/decomposition of La0.6Sr0.4Co0.2Fe0.8O3- δ oxygen separation membranes

Kinetic demixing and decomposition were studied on three La_0.6Sr_0.4Co_0.2Fe_0.8O_{3- δ} oxygen-separation hollow fibre membrane modules, which were operated under a 0.21/0.009bar oxygen partial pressure difference at 950°C for 1128, 3672 and 5512h, respectively. The post-operation membranes were characterized by Secondary Ion Mass Spectrometry, Scanning Electronic Microscope, Energy Dispersive Spectrum and X-ray Diffraction. The occurrence of kinetic demixing and decomposition was confirmed through the microstructural evolution of the membranes. Secondary-phase grains were found on the air-side surface of the membranes after the long-term operation and Co and Fe enrichment as well as La depletion was found on the surface and in the bulk at the air side. Cation diffusivities were found to be in the order Co>Fe>Sr>La. Kinetic demixing and decomposition rates of the membranes at the air side were found to be self-accelerating with time; the role of A-site deficiency in the perovskite lattice in the bulk near the air side surface is implicated in the mechanism. The oxygen permeability was not affected by the kinetic demixing and decomposition of the material during long-term operation (up to 5512h), however, we may expect permeability to be affected by secondary phase formation on the air-side surface at even longer operational times. © 2010 Elsevier B.V.

Simultaneous cellulose conversion and hydrogen production assisted by cellulose decomposition under UV-light photocatalysis

Photocatalytic conversion of cellulose to sugars and carbon dioxide with simultaneous production of hydrogen assisted by cellulose decomposition under UV or solar light irradiation was achieved upon immobilization of cellulose onto a TiO2 photocatalyst. This approach enables production of hydrogen from water without using valuable sacrificial agents, and provides the possibility for recovering sugars as liquid fuels.

Using Graph Components Derived from an Associative Concept Dictionary to Predict fMRI Neural Activation Patterns that Represent the Meaning of Nouns

In this study, we introduce an original distance definition for graphs, called the Markov-inverse-F measure (MiF). This measure enables the integration of classical graph theory indices with new knowledge pertaining to structural feature extraction from semantic networks. MiF improves the conventional Jaccard and/or Simpson indices, and reconciles both the geodesic information (random walk) and co-occurrence adjustment (degree balance and distribution). We measure the effectiveness of graph-based coefficients through the application of linguistic graph information for a neural activity recorded during conceptual processing in the human brain. Specifically, the MiF distance is computed between each of the nouns used in a previous neural experiment and each of the in-between words in a subgraph derived from the Edinburgh Word Association Thesaurus of English. From the MiF-based information matrix, a machine learning model can accurately obtain a scalar parameter that specifies the degree to which each voxel in (the MRI image of) the brain is activated by each word or each principal component of the intermediate semantic features. Furthermore, correlating the voxel information with the MiF-based principal components, a new computational neurolinguistics model with a network connectivity paradigm is created. This allows two dimensions of context space to be incorporated with both semantic and neural distributional representations.

Memory-centric VDF Graph Transformations for Practical FPGA Implementation

Realising memory intensive applications such as image and video processing on FPGA requires creation of complex, multi-level memory hierarchies to achieve real-time performance; however commerical High Level Synthesis tools are unable to automatically derive such structures and hence are unable to meet the demanding bandwidth and capacity constraints of these applications. Current approaches to solving this problem can only derive either single-level memory structures or very deep, highly inefficient hierarchies, leading in either case to one or more of high implementation cost and low performance. This paper presents an enhancement to an existing MC-HLS synthesis approach which solves this problem; it exploits and eliminates data duplication at multiple levels levels of the generated hierarchy, leading to a reduction in the number of levels and ultimately higher performance, lower cost implementations. When applied to synthesis of C-based Motion Estimation, Matrix Multiplication and Sobel Edge Detection applications, this enables reductions in Block RAM and Look Up Table (LUT) cost of up to 25%, whilst simultaneously increasing throughput.