The Origin of the Selectivity and Activity of Ruthenium‐Cluster Catalysts for Fuel‐Cell Feed‐Gas Purification: A Gas‐Phase Approach

Gas‐phase ruthenium clusters Ru_n⁺ (n=2–6) are employed as model systems to discover the origin of the outstanding performance of supported sub‐nanometer ruthenium particles in the catalytic CO methanation reaction with relevance to the hydrogen feed‐gas purification for advanced fuel‐cell applications. Using ion‐trap mass spectrometry in conjunction with first‐principles density functional theory calculations three fundamental properties of these clusters are identified which determine the selectivity and catalytic activity: high reactivity toward CO in contrast to inertness in the reaction with CO₂; promotion of cooperatively enhanced H₂ coadsorption and dissociation on pre‐formed ruthenium carbonyl clusters, that is, no CO poisoning occurs; and the presence of Ru‐atom sites with a low number of metal–metal bonds, which are particularly active for H₂ coadsorption and activation. Furthermore, comprehensive theoretical investigations provide mechanistic insight into the CO methanation reaction and discover a reaction route involving the formation of a formyl‐type intermediate.     

Thermal evaluation of a heat pipe working with n-pentane-acetone and n-pentane-methanol binary mixtures

Journal of Thermal Analysis and Calorimetry - In the present study, a set of experiments were accomplished to appraise the thermal performance and heat transfer of n-pentane-acetone and...     

Zinc-phosphate nanoparticles with reversibly attached TNF-α analogs: an interesting concept for potential use in active immunotherapy

The authors’ intention was to prepare nanometer-sized zinc-phosphate nanoparticles that would be capable of binding histidine-rich TNF-α analogs onto their surface via a coordinative bond. Zinc-phosphate nanoparticles with a size of around 60 nm were prepared by a wet precipitation method and characterized using SEM, EDX, XRD, and DLS. First, BSA was bound as a testing protein, afterward two TNF-α analogs with decreased activity were bound to the described nanoparticles. The efficiency of binding and the existence of coordinative bond were confirmed with SDS-PAGE analysis. During binding, particle storage, and release experiments, the prepared TNF-α analogs retained their biological activity—hence the epitopes necessary for formation of antibodies stayed intact. The particle size did not change within a period of 2 weeks. No significant agglomeration was observed, the particles could be quickly dispersed in ultrasound. The present nanoparticles and the general approach of coordinative binding are widely applicable for natural and engineered histidine-rich proteins. The nanoparticles bearing appropriate TNF-α analogs could also be potentially used for active immunotherapy to tackle the chronic inflammatory diseases associated with pathogenically elevated levels of TNF-α.     

Über die Synthese und Struktur der Magnesium- und Calcium-Hydrazidocarbonate

Ohne Zusammenfassung     

Effective and selective iodofunctionalisation of organic molecules in water using the iodine-hydrogen peroxide tandem

Efficient hydrogen peroxide enhanced iodofunctionalisation of ketones, 1,3-dicarbonyl derivatives and activated aromatic molecules using elemental iodine in water is achieved, whereas alkynes were stereoselectively converted into (E)-1,2-diiodoalkenes.     

A mesh-free numerical method for solution of the family of Kuramoto-Sivashinsky equations

In this paper, radial basis function (RBFs) based mesh-free method is implemented to find numerical solution of the Kuramoto-Sivashinsky equations. This approach has an edge over traditional methods such as finite-difference and finite element methods because it does not require a mesh to discretize the problem domain, and a set of scattered nodes in the domain of influence provided by initial data is required for the realization of the method. The accuracy of the method is assessed in terms of the error norms L₂,L_∞, number of nodes in the domain of influence, free parameter, dependent parameter RBFs and time step length. Numerical experiments demonstrate accuracy and robustness of the method for solving a class of nonlinear partial differential equations.     

On the selection of a good value of shape parameter in solving time-dependent partial differential equations using RBF approximation method

Radial basis function method is an effective tool for solving differential equations in engineering and sciences. Many radial basis functions contain a shape parameter c which is directly connected to the accuracy of the method. Rippa [1] proposed an algorithm for selecting good value of shape parameter c in RBF-interpolation. Based on this idea, we extended the proposed algorithm for selecting a good value of shape parameter c in solving time-dependent partial differential equations.     

Graph 3-coloring with a hybrid self-adaptive evolutionary algorithm

This paper proposes a hybrid self-adaptive evolutionary algorithm for graph coloring that is hybridized with the following novel elements: heuristic genotype-phenotype mapping, a swap local search heuristic, and a neutral survivor selection operator. This algorithm was compared with the evolutionary algorithm with the SAW method of Eiben et al., the Tabucol algorithm of Hertz and de Werra, and the hybrid evolutionary algorithm of Galinier and Hao. The performance of these algorithms were tested on a test suite consisting of randomly generated 3-colorable graphs of various structural features, such as graph size, type, edge density, and variability in sizes of color classes. Furthermore, the test graphs were generated including the phase transition where the graphs are hard to color. The purpose of the extensive experimental work was threefold: to investigate the behavior of the tested algorithms in the phase transition, to identify what impact hybridization with the DSatur traditional heuristic has on the evolutionary algorithm, and to show how graph structural features influence the performance of the graph-coloring algorithms. The results indicate that the performance of the hybrid self-adaptive evolutionary algorithm is comparable with, or better than, the performance of the hybrid evolutionary algorithm which is one of the best graph-coloring algorithms today. Moreover, the fact that all the considered algorithms performed poorly on flat graphs confirms that graphs of this type are really the hardest to color.