Prediction of Experimental Methanol Decomposition Rates on Platinum from First Principles

A microkinetic model for methanol decomposition on platinum is presented. The model incorporates competitive decomposition pathways, beginning with both O–H and C–H bond scission in methanol, and uses results from density functional theory (DFT) calculations [Greeley and Mavrikakis, J. Am. Chem. Soc. 124 (2002) 7193, Greeley and Mavrikakis, J. Am. Chem. Soc. 126 (2004) 3910]. Results from reaction kinetics experiments show that the rate of H₂ production increases with increasing temperature and methanol concentration in the feed and is only nominally affected by the presence of CO or H₂ with methanol. The model, based on the values of binding energies, pre-exponential factors and activation energy barriers derived from first principles calculations, accurately predicts experimental reaction rates and orders. The model also gives insight into the most favorable reaction pathway, the rate-limiting step, the apparent activation energy, coverages, and the effects of pressure. It is found that the pathway beginning with the C–H bond scission (CH₃OH→H₂COH→HCOH→CO) is dominant compared with the path beginning with O–H bond scission. The cleavage of the first C–H bond in methanol is the rate-controlling step. The surface is highly poisoned by CO, whereas COH appears to be a spectator species.     

Synthetic Secoisolariciresinol Diglucoside (LGM2605) Prevents Asbestos-Induced Inflammation and Genotoxic Cell Damage in Human Mesothelial Cells

Although alveolar macrophages play a critical role in malignant transformation of mesothelial cells following asbestos exposure, inflammatory and oxidative processes continue to occur in the mesothelial cells lining the pleura that may contribute to the carcinogenic process. Malignant transformation of mesothelial cells following asbestos exposure occurs over several decades; however, amelioration of DNA damage, inflammation, and cell injury may impede the carcinogenic process. We have shown in an in vitro model of asbestos-induced macrophage activation that synthetic secoisolariciresinol diglucoside (LGM2605), given preventively, reduced inflammatory cascades and oxidative/nitrosative cell damage. Therefore, it was hypothesized that LGM2605 could also be effective in reducing asbestos-induced activation and the damage of pleural mesothelial cells. LGM2605 treatment (50 µM) of huma n pleural mesothelial cells was initiated 4 h prior to exposure to asbestos (crocidolite, 20 µg/cm²). Supernatant and cells were evaluated at 0, 2, 4, and 8 h post asbestos exposure for reactive oxygen species (ROS) generation, DNA damage (oxidized guanine), inflammasome activation (caspase-1 activity) and associated pro-inflammatory cytokine release (IL-1β, IL-18, IL-6, TNFα, and HMGB1), and markers of oxidative stress (malondialdehyde (MDA) and 8-iso-prostaglandin F2a (8-iso-PGF2α). Asbestos induced a time-dependent ROS increase that was significantly (p < 0.0001) reduced (29.4%) by LGM2605 treatment. LGM2605 pretreatment also reduced levels of asbestos-induced DNA damage by 73.6% ± 1.0%. Although levels of inflammasome-activated cytokines, IL-1β and IL-18, reached 29.2 pg/mL ± 0.7 pg/mL and 43.9 pg/mL ± 0.8 pg/mL, respectively, LGM2605 treatment significantly (p < 0.0001) reduced cytokine levels comparable to baseline (non-asbestos exposed) values (3.8 pg/mL ± 0.2 pg/mL and 5.4 pg/mL ± 0.2 pg/mL, respectively). Furthermore, levels of IL-6 and TNFα in asbestos-exposed mesothelial cells were high (289.1 pg/mL ± 2.9 pg/mL and 511.3 pg/mL ± 10.2 pg/mL, respectively), while remaining undetectable with LGM2605 pretreatment. HMGB1 (a key inflammatory mediator and initiator of malignant transformation) release was reduced 75.3% ± 0.4% by LGM2605. Levels of MDA and 8-iso-PGF2α, markers of oxidative cell injury, were significantly (p < 0.001) reduced by 80.5% ± 0.1% and 76.6% ± 0.3%, respectively. LGM2605, given preventively, reduced ROS generation, DNA damage, and inflammasome-activated cytokine release and key inflammatory mediators implicated in asbestos-induced malignant transformation of normal mesothelial cells.     

Why Au and Cu Are More Selective Than Pt for Preferential Oxidation of CO at Low Temperature

Self-consistent, periodic density functional theory (DFT) calculations and micro-kinetic modeling are used to compare selectivity for the preferential oxidation of CO (PROX) with respect to H₂ based on studies of elementary reaction steps on the (111) facet of Au, Cu and Pt. The first step of H oxidation (OH formation) has a higher activation barrier than the second step (H₂O formation) on all three metal surfaces, indicating that OH formation competes with CO oxidation for the removal of trace amounts of CO from a typical reformate gas. The activation energy barrier for CO oxidation is found to be 0.18eV on Au(111), 0.82eV on Cu(111) and 0.96eV on Pt(111), whereas the barrier for OH formation is 0.90, 1.28 and 0.83eV respectively. A micro-kinetic model based on the DFT results shows that trends in the selectivity of these metals at different temperatures is due to (i) differences in the rate constants of the competitive CO and H oxidation reactions, and (ii) differences in the CO and H surface coverages. Our results explain why Au and Cu are more selective PROX catalysts compared to Pt at low temperatures. At higher temperatures, Pt and Cu lose some of their selectivity to CO oxidation, whereas the selectivity on Au decreases substantially primarily because of the significantly weaker CO adsorption.     

A framework for classifying trust for online systems

World Wide Web - There is no single universally agreed definition of trust for online systems. This has resulted in various trust metrics being proposed by researchers with diverse methods of...     

A Peptide-PNA Hybrid Beacon for Sensitive Detection of Protein Biomarkers in Biological Fluids

Specific and rapid detection of proteins in biological fluids poses a challenging problem. In biological fluids, many proteins are present at low concentrations, requiring high affinity and specificity of the beacon-protein interaction. We report the design of a peptide-PNA hybrid beacon that exploits the dimeric nature of a target protein, S100B, a biomarker for brain trauma, to enhance binding affinity and specificity. The complementary base-pairing of the PNA bases brings the two arms of the beacon, one carrying an Alexa tag and the other carrying a Dabcyl moiety, into proximity, thus quenching Alexa fluorescence. Each of the arms carries a sequence that binds to one of the subunits. Binding to the target separates the quencher from the probe lifting the quenching of fluorescence. Enhanced affinity and specificity resulting from simultaneously binding to two sites allowed specific detection of S100B at low-nanomolar concentrations in the presence of serum. The design can be easily adapted for the detection of proteins containing multiple binding sites and could prove useful for rapid and sensitive biomarker detection.     

A rule-based availability-driven cosynthesis scheme

The co-synthesis of hardware–software systems for complex embedded applications has been studied extensively with focus on various qualitative system objectives such as high speed performance and low power dissipation. One of the main challenges in the construction of multiprocessor systems for complex real time applications is provide high levels of system availability that satisfies the users’ expectations. Even though the area of hardware software cosynthesis has been studied extensively in the recent past, the issues that specifically relate to design exploration for highly available architectures need to be addressed more systematically and in a manner that supports active user participation. In this paper, we propose a user-centric co-synthesis mechanism for generating gracefully degrading, heterogeneous multiprocessor architectures that fulfills the dual objectives of achieving real-time performance as well as ensuring high levels of system availability at acceptable cost. A flexible interface allows the user to specify rules that effectively capture the users’ perceived availability expectations under different working conditions. We propose an algorithm to map these user requirements to the importance attached to the subset of services provided during any functional state. The system availability is evaluated on the basis of these user-driven importance values and a CTMC model of the underlying fail-repair process. We employ a stochastic timing model in which all the relevant performance parameters such as task execution times, data arrival times and data communication times are taken to be random variables. A stochastic scheduling algorithm assigns start and completion time distributions to tasks. A hierarchical genetic algorithm optimizes the selections of resources, i.e. processors and busses, and the task allocations. We report the results of a number of experiments performed with representative task graphs. Analysis shows that the co-synthesis tool we have developed is effectively driven by the user’s availability requirements as well as by the topological characteristics of the task graph to yield high quality architectures. We experimentally demonstrate the edge provided by a stochastic timing model in terms of performance assessment, resource utilization, system-availability and cost. An erratum to this article is available at .     

Hydrogen on and in Selected Overlayer Near-Surface Alloys and the Effect of Subsurface Hydrogen on the Reactivity of Alloy Surfaces

The interaction of hydrogen with the close-packed facets of seventeen transition metals overlaid with 1 ML of five transition metals (Au, Ag, Cu, Pt, and Pd) has been studied using periodic self-consistent (GGA-PW91) density functional theory (DFT) calculations. For noble metal overlayers (Au, Ag, and Cu), hydrogen at the host-metal/overlayer interface (subsurface hydrogen) is more stable than subsurface hydrogen in the pure host. For certain Au and Ag overlayers, subsurface hydrogen is more stable than surface hydrogen in the same system. The presence of subsurface hydrogen was found to have a significant effect on the electronic structure of the overlayer, resulting in its modified surface reactivity.     

Review based emotion profiles for cross domain recommendation

Several e-commerce sites are reaping the benefits of Cross-Domain Recommendation (CDR) systems to cross-sell products, guide new users and increase revenues. Current research works augment user-item ratings with a variety of auxiliary information such as location, personality, geo-tags and multimedia content that link multiple domains to provide effective CDR. In this paper, we propose a fresh perspective for generating recommendations across different domains by tapping the emotions that are encapsulated within user generated textual content such as reviews, blogs and comments. Such emotions serve as strong socio-psychological links between various entertainments domains and have the potential to obviate the cold start problems. Our CDR scheme uses an enriched emotion lexicon to analyze the emotions in online content expressed by users in the source and target domains and generates emotion-profiles of items and users in both domains. Subsequently, it applies collaborative filtering to match these profiles in order to recommend items in the target domain. We illustrate the working of our emotion-based CDR scheme using the movie and book domains as a case study. Experimental results on Movielens and Bookcrossing datasets yield 28.9% F1-measure which is a marked improvement of 71.1% as compared with a recently reported topic modeling approach to CDR for entertainment domains.