Mg-based nanocomposites with high capacity and fast kinetics for hydrogen storage

Magnesium and its alloys have shown a great potential in effective hydrogen storage due to their advantages of high volumetric/ gravimetric hydrogen storage capacity and low cost. However, the use of these materials in fuel cells for automotive applications at the present time is limited by high hydrogenation temperature and sluggish sorption kinetics. This paper presents the recent results of design and development of magnesium-based nanocomposites demonstrating the catalytic effects of carbon nanotubes and transition metals on hydrogen adsorption in these materials. The results are promising for the application of magnesium materials for hydrogen storage, with significantly reduced absorption temperatures and enhanced ab/desorption kinetics. High level Density Functional Theory calculations support the analysis of the hydrogenation mechanisms by revealing the detailed atomic and molecular interactions that underpin the catalytic roles of incorporated carbon and titanium, providing clear guidance for further design and development of such materials with better hydrogen storage properties.

Phase separation, porous structure, and cure kinetics in aliphatic epoxy resin containing hyperbranched polyester

Thermosetting blends of an aliphatic epoxy resin and a hydroxyl-functionalized hyperbranched polymer (HBP), aliphatic hyperbranched polyester Boltorn H40, were prepared using 4,4'-diaminodiphenylmethane (DDM) as the curing agent. The phase behavior and morphology of the DDM-cured epoxy/HBP blends with HBP content up to 40 wt% were investigated by differential scanning calorimetry (DSC), dynamic mechanical analysis (DMA), and scanning electron microscopy (SEM). The cured epoxy/HBP blends are immiscible and exhibit two separate glass transitions, as revealed by DMA. The SEM observation showed that there exist two phases in the cured blends, which is an epoxy-rich phase and an HBP-rich phase, which is responsible for the two separate glass transitions. The phase morphology was observed to be dependent on the blend composition. For the blends with HBP content up to 10 wt%, discrete HBP domains are dispersed in the continuous cured epoxy matrix, whereas the cured blend with 40 wt% HBP exhibits a combined morphology of connected globules and bicominuous phase structure. Porous epoxy thermosets with continuous open structures on the order of 100-300 nm were formed after the HBP-rich phase was extracted with solvent from the cured blend with 40 wt% HBP. The DSC study showed that the curing rate is not obviously affected in the epoxy/HBP blends with HBP content up to 40 wt %. The activation energy values obtained are not remarkably changed in the blends; the addition of HBP to epoxy resin thus does not change the mechanism of cure reaction of epoxy resin with DDM. (c) 2006 Wiley Periodicals, Inc.

Inhibition studies of purple acid phosphatases: Implications for the catalytic mechanism

Purple acid phosphatases (PAPs) belong to the family of binuclear metallohydrolases and catalyse the hydrolysis of a large group of phosphoester substrates at acidic pH. Despite structural conservation in their active sites PAPs appear to display mechanistic versatility. Here, aspects of the catalytic mechanism of two PAPs are investigated using the inhibitors vanadate and fluoride as probes. While the magnitude of their vanadate inhibition constants are similar the two enzymes differ with respect to the mode of inhibition; vanadate interacts in a non-competitive fashion with pig PAP (K-i = 40 mu mol L-1) while it inhibits red kidney bean PAP competitively (K-i = 30 mu mol L-1). Similarly, fluoride also acts as a competitive inhibitor for red kidney bean PAP, independent of pH, while the inhibition of pig PAP by fluoride is uncompetitive at low pH and non-competitive at higher pH, independent of metal ion composition. Furthermore, while fluoride acts as a slow-binding inhibitor in pig PAP it binds rapidly to the catalytic site of the red kidney bean enzyme. Since vanadate and fluoride are proposed to act as transition state and nucleophile mimics, respectively, the observed differences in inhibition kinetics indicate subtle but distinct variations in the reaction mechanism of these enzymes.

Mechanism and kinetics of dithiobenzoate-mediated RAFT polymerization. I. The current situation

Investigations into the kinetics and mechanism of dithiobenzoate-mediated Reversible Addition-Fragmentation Chain Transfer (RAFT) polymerizations, which exhibit nonideal kinetic behavior, such as induction periods and rate retardation, are comprehensively reviewed. The appreciable uncertainty in the rate coefficients associated with the RAFT equilibrium is discussed and methods for obtaining RAFT-specific rate coefficients are detailed. In addition, mechanistic studies are presented, which target the elucidation of the fundamental cause of rate retarding effects. The experimental and theoretical data existing in the literature are critically evaluated and apparent discrepancies between the results of different studies into the kinetics of RAFT polymerizations are discussed. Finally, recommendations for further work are given. (c) 2006 Wiley Periodicals, Inc.

Accelerated leap methods for simulating discrete stochastic chemical kinetics

Biologists are increasingly conscious of the critical role that noise plays in cellular functions such as genetic regulation, often in connection with fluctuations in small numbers of key regulatory molecules. This has inspired the development of models that capture this fundamentally discrete and stochastic nature of cellular biology - most notably the Gillespie stochastic simulation algorithm (SSA). The SSA simulates a temporally homogeneous, discrete-state, continuous-time Markov process, and of course the corresponding probabilities and numbers of each molecular species must all remain positive. While accurately serving this purpose, the SSA can be computationally inefficient due to very small time stepping so faster approximations such as the Poisson and Binomial τ-leap methods have been suggested. This work places these leap methods in the context of numerical methods for the solution of stochastic differential equations (SDEs) driven by Poisson noise. This allows analogues of Euler-Maruyuma, Milstein and even higher order methods to be developed through the Itô-Taylor expansions as well as similar derivative-free Runge-Kutta approaches. Numerical results demonstrate that these novel methods compare favourably with existing techniques for simulating biochemical reactions by more accurately capturing crucial properties such as the mean and variance than existing methods.