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Different transition metal compounds in conjunction with suitable promoters have been reported in the literature to catalyze the alcohols homologation (1–9)(Equation 1).

A significant improvement of the catalytic performance of triphenylphosphine polyacrylate Rh(I) complexes, prepared by reaction of RhH(PPh 3 ) 4 with atactic polyacrylic acid, has been attained by optimizing the preparative procedure (proper choice of reagent ratio and reaction medium) and by adding a crosslinking agent (hexamethylenediamine). The crosslinked systems, insoluble in the organic solvents, have been obtained in high yields and showed a catalytic activity for pent-1-ene hydrogenation and isomerization higher than that of a low-molecular-weight analog working in solution. These systems also show a remarkable catalytic stability and a very constant selectivity upon recycle. IR spectra of the catalysts and their carbonyl derivatives, obtained upon treatment in the solid state with CO, gave information about the nature of the different metal species present within the polymeric systems and explain the different catalytic behavior of the catalysts.

Ruthenium(II) and rhodium(I) complexes with alternating copolymers of maleic acid and different alkyl and aryl vinyl ethers have been prepared starting from RuH2(CO)(PPh3)3 or RhH(CO)(PPh3)3. The carboxylic groups of the maleic co-units functioned as ‘monodentate’ or ‘bidentate’ ligands for anchoring the metal complexes to macromolecular carriers having chemical and physical properties depending on the hydrophobicity, electronegativity, and bulkines of the ether co-units. The importance of the primary and secondary structures of the polymers in determining the distribution of metal species and the catalytic properties of the heterogeneous systems was demonstrated in a series of catalytic experiments carried out in different reaction media. The drop of the catalytic activity of the Ru derivatives and the progressive activation of the Rh systems observed in successive catalytic cycles were related to chemical modifications of the polymer-bound metal species. RuCl2(PPh3)3 was used to obtain heterogenized complexes through reaction with the sodium salts of poly(acrylic acid) and ethylene/maleic acid alternating copolymer. These last systems exhibited a practically constant catalytic activity with time.

The structure of palladium acetate complexes with phosphinated polystyrene has been studied by IR and UV spectroscopy. The results indicate that on polystyrene both mono- and dinuclear palladium complexes are formed. The comparison of the catalytic properties of palladium complexes on phosphinated polystyrene with those of mono- and dinuclear complexes supported on silica confirmed the formation of dinuclear palladium complexes on polystyrene surface.

Error Correcting Output Coding (ECOC) methods for multiclass classification present several open problems ranging from the trade-off between their error recovering capabilities and the learnability of the induced dichotomies to the selection of proper base learners and to the design of well-separated codes for a given multiclass problem. We experimentally analyse some of the main factors affecting the effectiveness of ECOC methods. We show that the architecture of ECOC learning machines influences the accuracy of the ECOC classifier, highlighting that ensembles of parallel and independent dichotomic Multi-Layer Perceptrons are well-suited to implement ECOC methods. We quantitatively evaluate the dependence among codeword bit errors using mutual information based measures, experimentally showing that a low dependence enhances the generalisation capabilities of ECOC. Moreover we show that the proper selection of the base learner and the decoding function of the reconstruction stage significantly affects the performance of the ECOC ensemble. The analysis of the relationships between the error recovering power, the accuracy of the base learners, and the dependence among codeword bits show that all these factors concur to the effectiveness of ECOC methods in a not straightforward way, very likely dependent on the distribution and complexity of the data.

Decomposition methods for multiclass classification problems constitute a powerful framework to improve generalization capabilities of a large set of learning machines, including support vector machines and multi-layer perceptrons. We present a review of the main decomposition approach to classification and an experimental comparison of One-Per-Class (OPC), Correcting Classifiers (CC) and Error Correcting Output Codes (ECOC) decomposition methods implemented using multi-layer perceptrons as dichotomizers. The results show that CC and ECOC outperform OPC over the considered data sets.

The study of metal complexes able to react with dioxygen has aroused great interest in recent years. t001. Oxidation Potential of the [M(NAPRDPT)] Complexes from Cyclic Voltammetry. Complex E°II′/III/V [Mn(NAPHDPT)] −0.31 [MN(NAPMeDPT)] −0.31 [Fe(NAPHDPT)] −0.32 [Fe(NAPMeDPT)] −0.27 [Co(NAPHDPT)] −0.37 [Co(NAPMeDPT)] −0.23 [Ni(NAPHDPT)] +0.35 [Ni(NAPMeDPT)] +0.40 [Cu(NAPHDPT)] +0.751 Cu(NAPMeDPT)] +0.861 1 Peak potential values at 0.2 V sec−1. Particular attention has been paid to those derivatives which take up dioxygen reversibly because they can mimic biological oxygen carriers [1]. In an attempt to find the parameters able to give some rationalization on the oxygenation reactions we have previously studied the behaviour towards dioxygenation of a series of five-coordinated metal complexes with O2N3 donor set (namely [M(SALRDPT)]) by electrochemical and X-ray diffraction techniques [2–5]. We wish to report here on the behaviour of the complexes [M(NAPRDPT)] having the same O2N3 donor set. M = Mn(II), Fe(II), Co(II), Ni(II), Cu(II) R = H, Me As in the case of SAL derivatives, all the NAP compounds undergo in DMSO solution a quasi-reversible one-electron anodic oxidation, except for copper(II) compounds, which are irreversibly oxidized (Table I). The comparison with SAL complexes shows that NAP derivatives are more easily oxidizable. The reaction with dioxygen in DMSO solution was followed by voltammetric techniques. The results indicate that [Co(NAPRDPT)] compounds are able to add dioxygen reversibly. For the manganese(II) and iron(II) complexes irreversible addition occurs, with formation of different oxygenated products. Copper(II) and nickel(II) complexes do not react with dioxygen The equilibrium constant of the reaction: [Co(NAPMeDPT)] + O2 ⇄ [Co(NAPMeDPT)] · O2 in DMSO at 20 °C resulted (1.0 ± 0.5) × 104 dm3 mol−1. At least for these types of pentadentate complexes it is possible to state that the location of the oxidation potential allows one to predict whether a compound is able to react with dioxygen but it is not sufficient to predict whether the dioxygenation reaction proceeds reversibly. The manganese(II), iron(II) and cobalt(II) complexes were ESR inactive both as powders or in degassed solutions. By exposure to O2 of toluene solutions of the [Co(NAPMeDPT)] or [Co(NAPHDPT)] complexes strong ESR signals were recorded, characterized by g-factor values of 2.022 and 2.037 respectively. The [Co(NAPHDPT)] adduct signal exhibited to Co hyperfine interaction, whereas the [Co(NAPMeDPT)] one did not, due to the rotation of the methyl group in this latter system. The ESR spectra have been related to metal species reversibly coordinating O2 which also showed a different spontaneous decaying process. The IR spectra of solid samples or homogeneous solutions of the different complexes showed significant changes due to O2 coordination. Hypotheses on the nature of the reversibly and irreversibly coordinating O2 species have been made on the basis of the frequencies of the IR absorption.

One of the methods used to evaluate the performance of ensemble classifiers is bias and variance analysis. In this chapter, we analyse bootstrap aggregating (Bagging) and Error Correcting Output Coding (ECOC) ensembles using a biasvariance framework; and make comparisons with single classifiers, while having Neural Networks (NNs) as base classifiers. As the performance of the ensembles depends on the individual base classifiers, it is important to understand the overall trends when the parameters of the base classifiers – nodes and epochs for NNs –, are changed. We show experimentally on 5 artificial and 4 UCI MLR datasets that there are some clear trends in the analysis that should be taken into consideration while designing NN classifier systems.