Catalytic oxidation of alcohols using molecular oxygen mediated by poly(ethylene glycol)-supported nitroxyl radicals

The selective catalytic oxidation of alcohols over a mixture of copper(l) chloride and a number of linear 'linker-less' or 'branched' poly(ethylene glycol)-supported nitroxyl radicals of the 2,2,6,6-tetramethyl-piperidine-1-oxyl (TEMPO) family as a catalyst system has been investigated in the presence of molecular oxygen in a batch reactor. It is found that the activity profile of the polymer-supported nitroxyl radicals is in good agreement with that of low-molecular weight nitroxyl catalysts, for example, allylic and benzylic alcohols are oxidised faster than aliphatic alcohols. The oxidations can be tuned to be highly selective such that aldehydes are the only oxidation products observed in the oxidation of primary alcohols and the oxidations of secondary alcohols yield the corresponding ketones. A strong structural effect of the polymeric nitroxyl species on catalytic activity that is dependent upon their spatial orientation of the nitroxyl radicals is particularly noted. The new soluble macromolecular catalysts can be recovered readily from the reaction mixture by solvent precipitation and filtration. In addition, the recycled catalysts demonstrate a similar selectivity with only a small decrease in activity compared to the fresh catalyst even after five repetitive cycles. (c) 2005 Elsevier B.V. All rights reserved.

Synthesis of heterosulfamates. Search for structure-taste relationships

Thirty one new sodium heterosulfamates, RNHSO3Na, where the R portion contains mainly thiazole, benzothiazole, thiadiazole and pyridine ring structures, have been synthesized and their taste portfolios have been assessed. A database of 132 heterosulfamates ( both open-chain and cyclic) has been formed by combining these new compounds with an existing set of 101 heterosulfamates which were previously synthesized and for which taste data are available. Simple descriptors have been obtained using (i) measurements with Corey-Pauling-Koltun (CPK) space- filling models giving x, y and z dimensions and a volume VCPK, (ii) calculated first order molecular connectivities ((1)chi(v)) and (iii) the calculated Spartan program parameters to obtain HOMO, LUMO energies, the solvation energy E-solv and V-SPART AN. The techniques of linear (LDA) and quadratic (QDA) discriminant analysis and Tree analysis have then been employed to develop structure-taste relationships (SARs) that classify the sweet (S) and non-sweet (N) compounds into separate categories. In the LDA analysis 70% of the compounds were correctly classified ( this compares with 65% when the smaller data set of 101 compounds was used) and in the QDA analysis 68% were correctly classified ( compared to 80% previously). TheTree analysis correctly classified 81% ( compared to 86% previously). An alternative Tree analysis derived using the Cerius2 program and a set of physicochemical descriptors correctly classified only 54% of the compounds.

Superacid-catalyzed polycondensation of acenaphthenequinone with aromatic hydrocarbons

A novel series of linear, high molecular weight polymers were synthesized by one-pot, superacid-catalyzed reaction of acenaphthenequinone (1) with aromatic hydrocarbons. The reactions were performed at room temperature in the Bronsted superacid CF3SO3H (trifluoromethanesulfonic acid, TFSA) and in a mixture of TFSA with methanesulfonic acid (MSA) and trifluoroacetic acid (TFA), which was used as both solvent and a medium for generation of electrophilic species from acenaphthenequinone. The polymer-forming reaction was found to be dependent greatly on the acidity of the reaction medium, as judged from the viscosity of the polymers obtained. Polycondensations of acenaphthenequinone with 4,4'-diphenoxybenzophenone (f), 1,3-bis(4-phenoxybenzoyl)benzene (g), 1,4-bis(4-phenoxybenzoyl)benzene (h), 1,10-bis(4-phenoxyphenyl)decane-1,10-dione (i), 2,6-diphenoxybenzonitrile), 2,6-diphenoxybenzoic acid (k), and 2-(4-biphenylyl)-6-phenylbenzoxazole (1) proceeded in a reaction medium of wide range of acidity, including pure TFSA (Hammett acidity function H-0 of pure TFSA is -14.1), whereas condensation of 1 with biphenyl, terphenyl, diphenyl ether, and 1,4-diphenoxybenzene needed a reaction medium of acidity H-0 less than -11.5. A possible reaction mechanism is suggested. The polymers obtained were found to be soluble in the common organic solvents, and flexible transparent films could be cast from the solutions. H-1 and C-13 NMR analyses of the polymers synthesized revealed their linear, highly regular structure. The polymers also possess high thermostability. Char yields for polymers 3a, 3c, 3d, and 3l in nitrogen were close to 80% at 1000 degrees C.

Robust identification for linear-in-the-parameters models

In this paper new robust nonlinear model construction algorithms for a large class of linear-in-the-parameters models are introduced to enhance model robustness, including three algorithms using combined A- or D-optimality or PRESS statistic (Predicted REsidual Sum of Squares) with regularised orthogonal least squares algorithm respectively. A common characteristic of these algorithms is that the inherent computation efficiency associated with the orthogonalisation scheme in orthogonal least squares or regularised orthogonal least squares has been extended such that the new algorithms are computationally efficient. A numerical example is included to demonstrate effectiveness of the algorithms. Copyright (C) 2003 IFAC.

Model selection approaches for non-linear system identification: a review

The identification of non-linear systems using only observed finite datasets has become a mature research area over the last two decades. A class of linear-in-the-parameter models with universal approximation capabilities have been intensively studied and widely used due to the availability of many linear-learning algorithms and their inherent convergence conditions. This article presents a systematic overview of basic research on model selection approaches for linear-in-the-parameter models. One of the fundamental problems in non-linear system identification is to find the minimal model with the best model generalisation performance from observational data only. The important concepts in achieving good model generalisation used in various non-linear system-identification algorithms are first reviewed, including Bayesian parameter regularisation and models selective criteria based on the cross validation and experimental design. A significant advance in machine learning has been the development of the support vector machine as a means for identifying kernel models based on the structural risk minimisation principle. The developments on the convex optimisation-based model construction algorithms including the support vector regression algorithms are outlined. Input selection algorithms and on-line system identification algorithms are also included in this review. Finally, some industrial applications of non-linear models are discussed.

Comparison of the computational cost of a Monte Carlo and deterministic algorithm for computing bilinear forms of matrix powers

In this paper we consider bilinear forms of matrix polynomials and show that these polynomials can be used to construct solutions for the problems of solving systems of linear algebraic equations, matrix inversion and finding extremal eigenvalues. An almost Optimal Monte Carlo (MAO) algorithm for computing bilinear forms of matrix polynomials is presented. Results for the computational costs of a balanced algorithm for computing the bilinear form of a matrix power is presented, i.e., an algorithm for which probability and systematic errors are of the same order, and this is compared with the computational cost for a corresponding deterministic method.

Perturbation, extraction and refinement of invariant pairs for matrix polynomials

Generalizing the notion of an eigenvector, invariant subspaces are frequently used in the context of linear eigenvalue problems, leading to conceptually elegant and numerically stable formulations in applications that require the computation of several eigenvalues and/or eigenvectors. Similar benefits can be expected for polynomial eigenvalue problems, for which the concept of an invariant subspace needs to be replaced by the concept of an invariant pair. Little has been known so far about numerical aspects of such invariant pairs. The aim of this paper is to fill this gap. The behavior of invariant pairs under perturbations of the matrix polynomial is studied and a first-order perturbation expansion is given. From a computational point of view, we investigate how to best extract invariant pairs from a linearization of the matrix polynomial. Moreover, we describe efficient refinement procedures directly based on the polynomial formulation. Numerical experiments with matrix polynomials from a number of applications demonstrate the effectiveness of our extraction and refinement procedures.

Condition number estimates for combined potential integral operators in acoustics and their boundary element discretisation

We consider the classical coupled, combined-field integral equation formulations for time-harmonic acoustic scattering by a sound soft bounded obstacle. In recent work, we have proved lower and upper bounds on the $L^2$ condition numbers for these formulations, and also on the norms of the classical acoustic single- and double-layer potential operators. These bounds to some extent make explicit the dependence of condition numbers on the wave number $k$, the geometry of the scatterer, and the coupling parameter. For example, with the usual choice of coupling parameter they show that, while the condition number grows like $k^{1/3}$ as $k\to\infty$, when the scatterer is a circle or sphere, it can grow as fast as $k^{7/5}$ for a class of `trapping' obstacles. In this paper we prove further bounds, sharpening and extending our previous results. In particular we show that there exist trapping obstacles for which the condition numbers grow as fast as $\exp(\gamma k)$, for some $\gamma>0$, as $k\to\infty$ through some sequence. This result depends on exponential localisation bounds on Laplace eigenfunctions in an ellipse that we prove in the appendix. We also clarify the correct choice of coupling parameter in 2D for low $k$. In the second part of the paper we focus on the boundary element discretisation of these operators. We discuss the extent to which the bounds on the continuous operators are also satisfied by their discrete counterparts and, via numerical experiments, we provide supporting evidence for some of the theoretical results, both quantitative and asymptotic, indicating further which of the upper and lower bounds may be sharper.

Blind equalization of nonminimum phase channels: higher order cumulant based algorithm

Higher order cumulant analysis is applied to the blind equalization of linear time-invariant (LTI) nonminimum-phase channels. The channel model is moving-average based. To identify the moving average parameters of channels, a higher-order cumulant fitting approach is adopted in which a novel relay algorithm is proposed to obtain the global solution. In addition, the technique incorporates model order determination. The transmitted data are considered as independently identically distributed random variables over some discrete finite set (e.g., set {±1, ±3}). A transformation scheme is suggested so that third-order cumulant analysis can be applied to this type of data. Simulation examples verify the feasibility and potential of the algorithm. Performance is compared with that of the noncumulant-based Sato scheme in terms of the steady state MSE and convergence rate.

Power control requirements in linear decorrelating detectors for CDMA

This paper addresses the effects of synchronisation errors (time delay, carrier phase, and carrier frequency) on the performance of linear decorrelating detectors (LDDs). A major effect is that all LDDs require certain degree of power control in the presence of synchronisation errors. The multi-shot sliding window algorithm (SLWA) and hard decision method (HDM) are analysed and their power control requirements are examined. Also, a more efficient one-shot detection scheme, called “hard-decision based coupling cancellation”, is proposed and analysed. These schemes are then compared with the isolation bit insertion (IBI) approach in terms of power control requirements.

Clinical potential and design of programmable mechanical impedances for orthotic applications

A person with a moderate or severe motor disability will often use specialised or adapted tools to assist their interaction with a general environment. Such tools can assist with the movement of a person's arms so as to facilitate manipulation, can provide postural supports, or interface to computers, wheelchairs or similar assistive technologies. Designing such devices with programmable stiffness and damping may offer a better means for the person to have effective control of their surroundings. This paper addresses the possibility of designing some assistive technologies using impedance elements that can adapt to the user and the circumstances. Two impedance elements are proposed. The first, based on magnetic particle brakes, allows control of the damping coefficient in a passive element. The second, based on detuning the P-D controller in a servo-motor mechanism, allows control of both stiffness and damping. Such a mechanical impedance can be modulated to the conditions imposed by the task in hand. The limits of linear theory are explored and possible uses of programmable impedance elements are proposed.

On the utility of input selection and pruning for financial distress prediction models

Analyzes the use of linear and neural network models for financial distress classification, with emphasis on the issues of input variable selection and model pruning. A data-driven method for selecting input variables (financial ratios, in this case) is proposed. A case study involving 60 British firms in the period 1997-2000 is used for illustration. It is shown that the use of the Optimal Brain Damage pruning technique can considerably improve the generalization ability of a neural model. Moreover, the set of financial ratios obtained with the proposed selection procedure is shown to be an appropriate alternative to the ratios usually employed by practitioners.

Conditioning of incremental variational data assimilation, with application to the Met Office system

Implementations of incremental variational data assimilation require the iterative minimization of a series of linear least-squares cost functions. The accuracy and speed with which these linear minimization problems can be solved is determined by the condition number of the Hessian of the problem. In this study, we examine how different components of the assimilation system influence this condition number. Theoretical bounds on the condition number for a single parameter system are presented and used to predict how the condition number is affected by the observation distribution and accuracy and by the specified lengthscales in the background error covariance matrix. The theoretical results are verified in the Met Office variational data assimilation system, using both pseudo-observations and real data.

Hydrogen-bonding-driven self-assembly of PEGylated organosilica nanoparticles with poly(acrylic acid) in aqueous solutions and in layer-by-layer deposition at solid surfaces

PEGylated organosilica nanoparticles have been synthesized through self-condensation of (3-mercaptopropyl)trimethoxysilane in dimethyl sulfoxide into thiolated nanoparticles with their subsequent reaction with methoxypoly(ethylene glycol) maleimide. The PEGylated nanoparticles showed excellent colloidal stability over a wide range of pH in contrast to the parent thiolated nanoparticles, which have a tendency to aggregate irreversibly under acidic conditions (pH < 3.0). Due to the presence of a poly(ethylene glycol)-based corona, the PEGylated nanoparticles are capable of forming hydrogen-bonded interpolymer complexes with poly(acrylic acid) in aqueous solutions under acidic conditions, resulting in larger aggregates. The use of hydrogen-bonding interactions allows more efficient attachment of the nanoparticles to surfaces. The alternating deposition of PEGylated nanoparticles and poly(acrylic acid) on silicon wafer surfaces in a layer-by-layer fashion leads to multilayered coatings. The self-assembly of PEGylated nanoparticles with poly(acrylic acid) in aqueous solutions and at solid surfaces was compared to the behavior of linear poly(ethylene glycol). The nanoparticle system creates thicker layers than the poly(ethylene glycol), and a thicker layer is obtained on a poly(acrylic acid) surface than on a silica surface, because of the effects of hydrogen bonding. Some implications of these hydrogen-bonding-driven interactions between PEGylated nanoparticles and poly(acrylic acid) for pharmaceutical formulations are discussed.

A two-dimensional X-ray scattering system for in-situ time-resolving studies of polymer structures subjected to controlled deformations

A two-dimensional X-ray scattering system developed around a CCD-based area detector is presented, both in terms of hardware employed and software designed and developed. An essential feature is the integration of hardware and software, detection and sample environment control which enables time-resolving in-situ wide-angle X-ray scattering measurements of global structural and orientational parameters of polymeric systems subjected to a variety of controlled external fields. The development and operation of a number of rheometers purpose-built for the application of such fields are described. Examples of the use of this system in monitoring degrees of shear-induced orientation in liquid-crystalline systems and crystallization of linear polymers subsequent to shear flow are presented.