Stereochemistry and thermal stability of tartaric acid on the intrinsically chiral Cu{531} surface

Intrinsically chiral metal surfaces provide enantiospecific reaction environments without the need of coadsorbed modifiers. Amongst the intrinsically chiral copper surfaces, Cu{531} has the smallest unit cell and the highest density of chiral sites. XPS, NEXAFS and TPD were employed to investigate the adsorption and decomposition behaviour of the two chiral enantiomers of tartaric acid on this surface. The results obtained from XPS and NEXAFS show that at saturation coverage both enantiomers of tartaric acid adsorb in a μ4 configuration through the two carboxylic groups,which are rotatedwith respect to each other by 90°±≈15°within the surface plane. At intermediate coverage the R,R enantiomer adopts a similar configuration, but the S,S enantiomer is different and shows a high degree of dissociation. Growth of multilayers is observed at high exposures when the sample is kept at below 370 K. TPD experiments show that multilayers desorb between 390 K and 470 K and decomposition of the chemisorbed layer occurs between 470 K and 600 K. The desorption spectra support a two-step decomposition mechanism with a O_C_C_O or HO–HC_CH–OH intermediate that leads to production of CO2 and CO. Enantiomeric differences are observed in the desorption features related to the decomposition of the chemisorbed layer.

Mechanism of the antioxidant to pro-oxidant switch in the behavior of dehydroascorbate during LDL oxidation by copper(II) ions

Oxidised low density lipoprotein (LDL) may be involved in the pathogenesis of atherosclerosis. We have therefore investigated the mechanisms underlying the antioxidant/pro-oxidant behavior of dehydroascorbate, the oxidation product of ascorbic acid, toward LDL incubated With Cu2+ ions. By monitoring lipid peroxidation through the formation of conjugated dienes and lipid hydroperoxides, we show that the pro-oxidant activity of dehydroascorbate is critically dependent on the presence of lipid hydroperoxides, which accumulate during the early stages of oxidation. Using electron paramagnetic resonance spectroscopy, we show that dehydroascorbate amplifies the generation of alkoxyl radicals during the interaction of copper ions with the model alkyl hydroperoxide, tert-butylhydroperoxide. Under continuous-flow conditions, a prominent doublet signal was detected, which we attribute to both the erythroascorbate and ascorbate free radicals. On this basis, we propose that the pro-oxidant activity of dehydroascorbate toward LDL is due to its known spontaneous interconversion to erythroascorbate and ascorbate, which reduce Cu2+ to Cu+ and thereby promote the decomposition of lipid hydroperoxides. Various mechanisms, including copper chelation and Cu+ oxidation, are suggested to underlie the antioxidant behavior of dehydroascorbate in LDL that is essentially free of lipid hydroperoxides. (C) 2007 Elsevier Inc. All rights reserved.

A neurofuzzy network knowledge extraction and extended gram-Schmidt algorithm for model subspace decomposition

This paper introduces a new neurofuzzy model construction and parameter estimation algorithm from observed finite data sets, based on a Takagi and Sugeno (T-S) inference mechanism and a new extended Gram-Schmidt orthogonal decomposition algorithm, for the modeling of a priori unknown dynamical systems in the form of a set of fuzzy rules. The first contribution of the paper is the introduction of a one to one mapping between a fuzzy rule-base and a model matrix feature subspace using the T-S inference mechanism. This link enables the numerical properties associated with a rule-based matrix subspace, the relationships amongst these matrix subspaces, and the correlation between the output vector and a rule-base matrix subspace, to be investigated and extracted as rule-based knowledge to enhance model transparency. The matrix subspace spanned by a fuzzy rule is initially derived as the input regression matrix multiplied by a weighting matrix that consists of the corresponding fuzzy membership functions over the training data set. Model transparency is explored by the derivation of an equivalence between an A-optimality experimental design criterion of the weighting matrix and the average model output sensitivity to the fuzzy rule, so that rule-bases can be effectively measured by their identifiability via the A-optimality experimental design criterion. The A-optimality experimental design criterion of the weighting matrices of fuzzy rules is used to construct an initial model rule-base. An extended Gram-Schmidt algorithm is then developed to estimate the parameter vector for each rule. This new algorithm decomposes the model rule-bases via an orthogonal subspace decomposition approach, so as to enhance model transparency with the capability of interpreting the derived rule-base energy level. This new approach is computationally simpler than the conventional Gram-Schmidt algorithm for resolving high dimensional regression problems, whereby it is computationally desirable to decompose complex models into a few submodels rather than a single model with large number of input variables and the associated curse of dimensionality problem. Numerical examples are included to demonstrate the effectiveness of the proposed new algorithm.

Robust neurofuzzy rule base knowledge extraction and estimation using subspace decomposition combined with regularization and D-optimality

A new robust neurofuzzy model construction algorithm has been introduced for the modeling of a priori unknown dynamical systems from observed finite data sets in the form of a set of fuzzy rules. Based on a Takagi-Sugeno (T-S) inference mechanism a one to one mapping between a fuzzy rule base and a model matrix feature subspace is established. This link enables rule based knowledge to be extracted from matrix subspace to enhance model transparency. In order to achieve maximized model robustness and sparsity, a new robust extended Gram-Schmidt (G-S) method has been introduced via two effective and complementary approaches of regularization and D-optimality experimental design. Model rule bases are decomposed into orthogonal subspaces, so as to enhance model transparency with the capability of interpreting the derived rule base energy level. A locally regularized orthogonal least squares algorithm, combined with a D-optimality used for subspace based rule selection, has been extended for fuzzy rule regularization and subspace based information extraction. By using a weighting for the D-optimality cost function, the entire model construction procedure becomes automatic. Numerical examples are included to demonstrate the effectiveness of the proposed new algorithm.