Mixed ligand binuclear copper(I) complexes containing (Cu2N8)-N-I chromophores. Observation of emissions from MLCT excited states involving two different ligands

Using the 1:2 condensate (L) of diethylenetriamine and benzaldehyde as the main ligand, binuclear copper(l) complexes [Cu2L2(4,4'-bipyridine)](CIO4)(2).0.5H(2)O (1a) and [Cu2L2(1,2-bis(4-pyridyl)ethane)](CIO4)(2) (1b) are synthesised. The two metal ions in la are bridged by 4,4'-bipyridine and those in 1b by 1,2-bis(4-pyridyl)ethane, From the X-ray crystal structure of la, each metal ion is found to be bound to three N atoms of L and one of the two N atoms of the bridging ligand in a distorted tetrahedral fashion. The Cu(I)-N bond lengths in la lie in the range of 1.998(5)-2.229(6) Angstrom. Electrochemical studies in dichloromethane (DCM) show that the (Cu2N8)-N-I moieties in la and 1b are composed of two essentially non-interacting (CuN4)-N-I cores with Cu-II/I potential of 0.44 V vs. SCE. While la displays metal induced quenching of the inherent emission of 4,4'-bipyridine in DCM solution, 1b exhibits two weak emission bands in DCM solution at 425 and 477 nm (total quantum yield = 3.59 x 10(-5)) originating from MLCT excited states. With the help of Extended Huckel calculations it is established that the higher energy emission in 1b is from Cu(I) --> bridging-ligand charge transfer excited state and the lower energy one in 1b from Cu(I) --> L charge transfer excited state.

Magnetically separable, carbon-supported nanocatalysts for the manufacture of fine chemicals

The best of both worlds: The synthesis of carbon-encapsulated iron-based magnetic nanoparticles is described. With such small catalysts that have macroscopic magnetic properties, the advantages of homogeneous or colloidal and heterogeneous catalysts can be combined.

Silica-encapsulated nanomagnetic particle as a new recoverable biocatalyst carrier

Enzymes are versatile biocatalysts with major advantages of ultrahigh reaction selectivity and specificity under mild conditions, which currently find increasing applications. However, their applications are often hampered by difficulties in recovery and recycling. As a result, we carried out detailed investigations on the synthesis and characterization of silica-encapsulated iron oxide magnetic nanoparticles of controlled dimension as an enzyme carrier. It is shown that the relatively smaller sized silica-coated magnetic nanoparticle prepared by the microemlusion technique can a carry bulky enzyme, beta-lactamase, via chemical linkages on the silica overlayer without severely blocking the enzymatic active center ( which is commonly encountered in conventional solid supports). An activity study by Michalis-Menten kinetics reflects that this new type of immobilization allows enzyme isolation with accessibility as good as free enzyme. The recovery and reusability of the nanoparticle-supported enzyme upon application of magnetic separation are also demonstrated.

Ultra-thin porous silica coated silver-platinum alloy nano-particle as a new catalyst precursor

Templated sol-gel encapsulation of surfactant-stabilised micelles containing metal precursor(s) with ultra-thin porous silica coating allows solvent extraction of organic based stabiliser from the composites in colloidal state hence a new method of preparing supported alloy catalysts using the inorganic silica-stabilised nano-sized, homogenously mixed, silver - platinum (Ag-Pt) colloidal particles is reported.

Hitting emissions targets with (statistical) confidence in multi-instrument Emissions Trading Schemes

A means of assessing, monitoring and controlling aggregate emissions from multi-instrument Emissions Trading Schemes is proposed. The approach allows contributions from different instruments with different forms of emissions targets to be integrated. Where Emissions Trading Schemes are helping meet specific national targets, the approach allows the entry requirements of new participants to be calculated and set at a level that will achieve these targets. The approach is multi-levelled, and may be extended downwards to support pooling of participants within instruments, or upwards to embed Emissions Trading Schemes within a wider suite of policies and measures with hard and soft targets. Aggregate emissions from each instrument are treated stochastically. Emissions from the scheme as a whole are then the joint probability distribution formed by integrating the emissions from its instruments. Because a Bayesian approach is adopted, qualitative and semi-qualitative data from expert opinion can be used where quantitative data is not currently available, or is incomplete. This approach helps government retain sufficient control over emissions trading scheme targets to allow them to meet their emissions reduction obligations, while minimising the need for retrospectively adjusting existing participants’ conditions of entry. This maintains participant confidence, while providing the necessary policy levers for good governance.

Chylomicron particle size and number, factor VII activation and dietary monounsaturated fatty acids

This study evaluated the effects of substituting dietary saturated fatty acids (SFAs) with monounsaturated fatty acids (MUFAs) on postprandial chylomicron (triacylglycerol (TAG), apolipoprotein B-48 (apo B-48) and retinyl ester (RE)), chylomicron particle size and factor VII (FVII) response when subjects were given a standard meal. In a controlled sequential design, 51 healthy young subjects followed an SFA-rich diet (Reference diet) for 8 weeks after which half of the subjects followed a moderate MUFA diet (n = 25) and half followed a high MUFA diet (n = 26) for 16 weeks. Fasting lipoprotein and lipid measurements were evaluated at baseline and at 8-week intervals during the Reference and MUFA diets. In 25 of the subjects (n = 12 moderate MUFA, n = 13 high MUFA), postprandial responses to a standard test meal containing RE and 13 C-tripalmitin were investigated at the end of the Reference and the MUFA diet periods. Although there were no differences in the postprandial lipid markers (TAG, RE, C-13-TAG) on the two diets, the postprandial apo B-48 response (incremental area under the curve (IAUC) was reduced by 21% on the moderate MUFA diet (NS) and by 54% on the high MUFA diet (P < 0.01). The postprandial peak concentrations of apo B-48 were reduced by 33% on the moderate MUFA diet (P < 0.01) and 48% on the high MUFA diet (P < 0.001). Fasting values for factor VII activity (FVIIc), activated factor VII (FVIIa) or factor VII antigen (FVIIag) did not differ significantly when subjects were transferred from Reference to MUFA diets. However, the postprandial increases in coagulation FVII activity (FVIIc) were 18% lower and of activated FVII (FVIIa) were 17% lower on the moderate MUFA diet (NS). Postprandial increases in FVIIc and FVIIa were 50% (P < 0.05) and 29% (P < 0.07) lower on the high MUFA diet and the area under the postprandial FVIIc response curve (AUC) was also lower on the high MUFA diet (P < 0.05). Significantly higher ratios of RE:apo B-48 (P < 0.001) and 13 C-palmitic acid:apo B-48 (P < 0.01) during both MUFA diets suggest that the CMs formed carry larger amounts of dietary lipids per particle, reflecting an adaptation to form larger lipid droplets in the enterocyte when increased amounts of dietary MUFAs are fed. Smaller numbers of larger chylomicrons may explain attenuated activation of factor VII during the postprandial state when the background diet is rich in MUFA. (C) 2002 Elsevier Science Ireland Ltd. All rights reserved.

Saturated fat-induced changes in S-f 60-400 particle composition reduces uptake of LDL by HepG2 cells

The ability of human postprandial triacylglycerol-rich lipoproteins (TRLs), isolated after meals enriched in saturated fatty acids (SFAs), n-6 PUFAs, and MUFAs, to inhibit the uptake of I-125-labeled LDL by the LDL receptor was investigated in HepG2 cells. Addition of TRLs resulted in a dose-dependent inhibition of heparin-releasable binding, cell-associated radioactivity, and degradation products of I-125-labeled LDL (P < 0.001). SFA-rich Svedberg flotation rate (S-f) 60-400 resulted in significantly greater inhibition of cell-associated radioactivity than PUFA-rich particles (P = 0.016) and total uptake of I-125-labeled LDL compared with PUFA- and MUFA-rich particles (P = 0.02). Normalization of the apolipoprotein (apo)E but not apoC-III content of the TRLs removed the effect of meal fatty acid composition, and addition of an anti-apoE antibody reversed the inhibitory effect of TRLs on the total uptake of I-125-labeled LDL. Real time RT-PCR showed that the SFA-rich Sf 60-400 increased the expression of genes involved in hepatic lipid synthesis (P < 0.05) and decreased the expression of the LDL receptor-related protein 1 compared with MUFAs (P = 0.008). In conclusion, these findings suggest an alternative or additional mechanism whereby acute fat ingestion can influence LDL clearance via competitive apoE-dependent effects of TRL on the LDL receptor.-Jackson, K. G., V. Maitin, D. S. Leake, P. Yaqoob, and C. M. Williams. Saturated fat-induced changes in Sf 60 400 particle composition reduces uptake of LDL by HepG2 cells.

Towards the automatic reconstruction of dendritic trees using particle filters

The 3D reconstruction of a Golgi-stained dendritic tree from a serial stack of images captured with a transmitted light bright-field microscope is investigated. Modifications to the bootstrap filter are discussed such that the tree structure may be estimated recursively as a series of connected segments. The tracking performance of the bootstrap particle filter is compared against Differential Evolution, an evolutionary global optimisation method, both in terms of robustness and accuracy. It is found that the particle filtering approach is significantly more robust and accurate for the data considered.

A tunable radial basis function model for nonlinear system identification using particle swarm optimisation

A tunable radial basis function (RBF) network model is proposed for nonlinear system identification using particle swarm optimisation (PSO). At each stage of orthogonal forward regression (OFR) model construction, PSO optimises one RBF unit's centre vector and diagonal covariance matrix by minimising the leave-one-out (LOO) mean square error (MSE). This PSO aided OFR automatically determines how many tunable RBF nodes are sufficient for modelling. Compared with the-state-of-the-art local regularisation assisted orthogonal least squares algorithm based on the LOO MSE criterion for constructing fixed-node RBF network models, the PSO tuned RBF model construction produces more parsimonious RBF models with better generalisation performance and is computationally more efficient.

Nonlinear system identification using particle swarm optimisation tuned radial basis function models

A novel particle swarm optimisation (PSO) tuned radial basis function (RBF) network model is proposed for identification of non-linear systems. At each stage of orthogonal forward regression (OFR) model construction process, PSO is adopted to tune one RBF unit's centre vector and diagonal covariance matrix by minimising the leave-one-out (LOO) mean square error (MSE). This PSO aided OFR automatically determines how many tunable RBF nodes are sufficient for modelling. Compared with the-state-of-the-art local regularisation assisted orthogonal least squares algorithm based on the LOO MSE criterion for constructing fixed-node RBF network models, the PSO tuned RBF model construction produces more parsimonious RBF models with better generalisation performance and is often more efficient in model construction. The effectiveness of the proposed PSO aided OFR algorithm for constructing tunable node RBF models is demonstrated using three real data sets.

Mixed initial-boundary value problem in particle modeling of microelectronic devices

The Boltzmann equation in presence of boundary and initial conditions, which describes the general case of carrier transport in microelectronic devices is analysed in terms of Monte Carlo theory. The classical Ensemble Monte Carlo algorithm which has been devised by merely phenomenological considerations of the initial and boundary carrier contributions is now derived in a formal way. The approach allows to suggest a set of event-biasing algorithms for statistical enhancement as an alternative of the population control technique, which is virtually the only algorithm currently used in particle simulators. The scheme of the self-consistent coupling of Boltzmann and Poisson equation is considered for the case of weighted particles. It is shown that particles survive the successive iteration steps.

Carbon fractions associated with silt-size particles in surface and subsurface soil horizons

Information on the distribution and behavior of C fractions in soil particle sizes is crucial for understanding C dynamics in soil. At present little is known about the behavior of the C associated with silt-size particles. We quantified the concentrations, distribution, and enrichment of total C (TC), readily oxidizable C (ROC), hotwater- extractable C (HWC), and cold-water-extractable C (CWC) fractions in coarse (63–20-mm), medium (20–6.3-mm), and fine (6.3–2-mm) silt-size subfractions and in coarse (2000–250 mm) and fine (250–63 mm) sand and clay (<2-mm) soil fractions isolated from bulk soil (<2 mm), and 2- to 4-mm aggregate-size fraction of surface (0–25 cm) and subsurface (25–55 cm) soils under different land uses. All measured C fractions varied significantly across all soil particle-size fractions. The highest C concentrations were associated with the <20-mm soil fractions and peaked in the medium (20–6.3-mm) and fine (6.3–2-mm) silt subfractions in most treatments. Carbon enrichment ratios (ERC) revealed the dual behavior of the C fractions associated with the medium silt-size fraction, demonstrating the simultaneous enrichment of TC and ROC, and the depletion of HWC and CWC fractions. The medium silt (20–6.3-mm) subfraction was identified in this study as a zone where the associated C fractions exhibit transitory qualities. Our results show that investigating subfractions within the silt-size particle fraction provides better understanding of the behavior of C fractions in this soil fraction.

U.K. HiGEM: Simulations of desert dust and biomass burning aerosols with a high-resolution atmospheric GCM

The atmospheric component of the United Kingdom’s new High-resolution Global Environmental Model (HiGEM) has been run with interactive aerosol schemes that include biomass burning and mineral dust. Dust emission, transport, and deposition are parameterized within the model using six particle size divisions, which are treated independently. The biomass is modeled in three nonindependent modes, and emissions are prescribed from an external dataset. The model is shown to produce realistic horizontal and vertical distributions of these aerosols for each season when compared with available satellite- and ground-based observations and with other models. Combined aerosol optical depths off the coast of North Africa exceed 0.5 both in boreal winter, when biomass is the main contributor, and also in summer, when the dust dominates. The model is capable of resolving smaller-scale features, such as dust storms emanating from the Bode´ le´ and Saharan regions of North Africa and the wintertime Bode´ le´ low-level jet. This is illustrated by February and July case studies, in which the diurnal cycles of model variables in relation to dust emission and transport are examined. The top-of-atmosphere annual mean radiative forcing of the dust is calculated and found to be globally quite small but locally very large, exceeding 20 W m22 over the Sahara, where inclusion of dust aerosol is shown to improve the model radiative balance. This work extends previous aerosol studies by combining complexity with increased global resolution and represents a step toward the next generation of models to investigate aerosol–climate interactions. 1. Introduction Accurate modeling of mineral dust is known to be important because of its radiative impact in both numerical weather prediction models (Milton et al. 2008; Haywood et

Particle swarm optimization aided orthogonal forward regression for unified data modeling

We propose a unified data modeling approach that is equally applicable to supervised regression and classification applications, as well as to unsupervised probability density function estimation. A particle swarm optimization (PSO) aided orthogonal forward regression (OFR) algorithm based on leave-one-out (LOO) criteria is developed to construct parsimonious radial basis function (RBF) networks with tunable nodes. Each stage of the construction process determines the center vector and diagonal covariance matrix of one RBF node by minimizing the LOO statistics. For regression applications, the LOO criterion is chosen to be the LOO mean square error, while the LOO misclassification rate is adopted in two-class classification applications. By adopting the Parzen window estimate as the desired response, the unsupervised density estimation problem is transformed into a constrained regression problem. This PSO aided OFR algorithm for tunable-node RBF networks is capable of constructing very parsimonious RBF models that generalize well, and our analysis and experimental results demonstrate that the algorithm is computationally even simpler than the efficient regularization assisted orthogonal least square algorithm based on LOO criteria for selecting fixed-node RBF models. Another significant advantage of the proposed learning procedure is that it does not have learning hyperparameters that have to be tuned using costly cross validation. The effectiveness of the proposed PSO aided OFR construction procedure is illustrated using several examples taken from regression and classification, as well as density estimation applications.