Determination of lead in drinking waters by hydride generation and atomic-absorption spectroscopy, and three other methods

Simultaneous presence of copper and nickel in potable waters interferes with the determination of lead at trace levels by the hydride-atomic-absorption spectrophotometric method. This interference is eliminated by co-precipitating lead with manganese dioxide from acidic solution. The precipitate is dissolved in 0.85% nitric acid and analysed by the automated hydride-atomic-absorption method. This method has been applied to 22 representative water samples and results compared with those obtained by using differential pulse anodic-stripping voltammetry, flame atomic-absorption and graphite-furnace atomic-absorption spectrophotometry. The precision of the three methods is reported and their accuracy checked by the analysis of reference standard water samples. The sensitivity of the three methods is of the order of 1 mug/l., compared to 100 mug/l. for flame atomic-absorption. The merits of each method are discussed.     

In situ Raman studies on lithiated single-wall carbon nanotubes in liquid ammonia

The charge transfer induced lithiation of single-wall carbon nanotubes (SWNTs) was investigated by in situ monitoring by Raman spectroscopy as lithium was added incrementally to a dispersion of SWNTs in liquid ammonia. Charge transfer from liquid ammonia solvated lithium to the SWNTs led to intercalation of lithium into the SWNT ropes, as well as to the semi-covalent lithiation of the SWNTs. Raman spectra of the SWNTs recorded as lithium was added showed a 30 wavenumber downshift of the G band (1594 cm⁻¹) with the concomitant appearance of a new peak at 1350 cm⁻¹ that was assigned as the signature of the lithiated SWNTs. Addition of 1-iodododecane to the lithiated SWNTs resulted in the covalent attachment of dodecyl groups. The intercalation of lithium throughout the SWNT ropes led to complete dodecylation of all individual SWNTs.     

Carbon nanotube salts. Arylation of single-wall carbon nanotubes

[reaction: see text] Carbon nanotube salts prepared by treating single-wall carbon nanotubes (SWNTs) with lithium in liquid ammonia react readily with aryl iodides to give SWNTs functionalized by aryl groups.     

Mass spectra of sterically crowded trialkylsilyl derivatives of nucleosides

The electron impact mass spectra of tert-butyldimethylsilyl-, cyclo-tetramethylene-tert-butylsilyl and cyclo-tetramethylene-isopropylsilyl- ether derivatives of ribo- and 2′-deoxyribonucleosides are described in detail. The interpretation of fragmentation pathways of full and mixed derivatives was aided by metastable ion decomposition studies, precise mass and deuterium labelling measurements, and spectra of mixed derivatives containing the ‘passive’ (in these spectra) trimethylsilyl group. The sterically crowded silyl groups have a powerful fragmentation directing effect. Elimination of a bulky radical, R˙ (tert-butyl or isopropyl), from the molecular ion produces the siliconium ion [M? R]⁺, which is the precursor for most of the other prominent ions in the spectra. These arise from ‘siliconium ion rearrangements’ resulting from the interaction of the positively charged siliconium ion center with electron dense regions (i.e. oxygens) in the molecule, to form cyclic silyloxonium ions which subsequently decompose. Since the interacting oxygen and silicon must be sterically accessible, the fragment ion types and their abundances are very dependent upon structure. Consequently, [M? R]⁺ ions formed from 2′, 3′ or 5′-O-silyl groups give rise to different sets of daughter ions which, for the most part, are not found, or have very low abundances, in the mass spectra of underivatized or trimethylsilylated nucleosides. Detailed information on sugar and base moieties and isomeric substitution is readily obtained.     

Sampled-Data Nash Equilibria in Differential Games with Impulse Controls

Journal of Optimization Theory and Applications - We study a class of deterministic two-player nonzero-sum differential games where one player uses piecewise-continuous controls to affect the...     

A Predictive Approach Using Fractal Analysis for Analyte-Receptor Binding and Dissociation Kinetics for Surface Plasmon Resonance Biosensor Applications

A predictive approach using fractal analysis is presented for analyte-receptor binding and dissociation kinetics for biosensor applications. Data taken from the literature may be modeled, in the case of binding using a single-fractal analysis or a dual-fractal analysis. The dual-fractal analysis represents a change in the binding mechanism as the reaction progresses on the surface. A single-fractal analysis is adequate to model the dissociation kinetics in the examples presented. Predictive relationships developed for the binding and the affinity (k(diss)/k(bind)) as a function of the analyte concentration are of particular value since they provide a means by which the binding and the affinity rate coefficients may be manipulated. Relationships are also presented for the binding and the dissociation rate coefficients and for the affinity as a function of their corresponding fractal dimension, D(f), or the degree of heterogeneity that exists on the surface. When analyte-receptor binding or dissociation is involved, an increase in the heterogeneity on the surface (increase in D(f)) leads to an increase in the binding and in the dissociation rate coefficient. It is suggested that an increase in the degree of heterogeneity on the surface leads to an increase in the turbulence on the surface owing to the irregularities on the surface. This turbulence promotes mixing, minimizes diffusional limitations, and leads subsequently to an increase in the binding and in the dissociation rate coefficient. The binding and the dissociation rate coefficients are rather sensitive to the degree of heterogeneity, D(f,bind) (or D(f1)) and D(f,diss), respectively, that exists on the biosensor surface. For example, the order of dependence on D(f,bind) (or D(f1)) and D(f2) is 6.69 and 6.96 for k(bind,1) (or k(1)) and k(2), respectively, for the binding of 0.085 to 0.339 μM Fab fragment 48G7(L)48G7(H) in solution to p-nitrophenyl phosphonate (PNP) transition state analogue immobilized on a surface plasmon resonance (SPR) biosensor. The order of dependence on D(f,diss) (or D(f,d)) is 3.26 for the dissociation rate coefficient, k(diss), for the dissociation of the 48G7(L)48G7(H)-PNP complex from the SPR surface to the solution. The predictive relationships presented for the binding and the affinity as a function of the analyte concentration in solution provide further physical insights into the reactions on the surface and should assist in enhancing SPR biosensor performance. In general, the technique is applicable to other reactions occurring on different types of biosensor surfaces and other surfaces such as cell-surface reactions. Copyright 2000 Academic Press.     

A fractal analysis of analyte-estrogen receptor binding and dissociation kinetics using biosensors: environmental effects

A fractal analysis is used to model the binding and dissociation kinetics between analytes in solution and estrogen receptors (ER) immobilized on a sensor chip of a surface plasmon resonance (SPR) biosensor. Both cases are analyzed: unliganded as well as liganded. The influence of different ligands is also analyzed. A better understanding of the kinetics provides physical insights into the interactions and suggests means by which appropriate interactions (to promote correct signaling) and inappropriate interactions such as with xenoestrogens (to minimize inappropriate signaling and signaling deleterious to health) may be better controlled. The fractal approach is applied to analyte-ER interaction data available in the literature. Numerical values obtained for the binding and the dissociation rate coefficients are linked to the degree of roughness or heterogeneity (fractal dimension, D(f)) present on the biosensor chip surface. In general, the binding and the dissociation rate coefficients are very sensitive to the degree of heterogeneity on the surface. For example, the binding rate coefficient, k, exhibits a 4.60 order of dependence on the fractal dimension, D(f), for the binding of unliganded and liganded VDR mixed with GST-RXR in solution to Spp-1 VDRE (1,25-dihydroxyvitamin D(3) receptor element) DNA immobilized on a sensor chip surface (Cheskis and Freedman, Biochemistry 35 (1996) 3300-3318). A single-fractal analysis is adequate in some cases. In others (that exhibit complexities in the binding or the dissociation curves) a dual-fractal analysis is required to obtain a better fit. A predictive relationship is also presented for the ratio K(A)(=k/k(d)) as a function of the ratio of the fractal dimensions (D(f)/D(fd)). This has biomedical and environmental implications in that the dissociation and binding rate coefficients may be used to alleviate deleterious effects or enhance beneficial effects by selective modulation of the surface. The K(A) exhibits a 112-order dependence on the ratio of the fractal dimensions for the ligand effects on VDR-RXR interaction with specific DNA.     

Analyte-receptor binding kinetics for biosensor applications

The diffusion-limited binding kinetics of antigen (analyte), in solution with antibody (receptor) immobilized on a biosensor surface, is analyzed within a fractal framework. Most of the data presented is adequately described by a single-fractal analysis. This was indicated by the regression analysis provided by Sigmaplot. A single example of a dual-fractal analysis is also presented. It is of interest to note that the binding-rate coefficient (k) and the fractal dimension (D_f) both exhibit changes in the same and in the reverse direction for the antigen-antibody systems analyzed. Binding-rate coefficient expressions, as a function of the D_f developed for the antigen-antibody binding systems, indicate the high sensitivity of thek on the D_f when both a single- and a dual-fractal analysis are used. For example, for a single-fractal analysis, and for the binding of antibody Mab 0.5β in solution to gpl20 peptide immobilized on a BIAcore biosensor, the order of dependence on the D_f was 4.0926. For a dual-fractal analysis, and for the binding of 25-100 ng/mL TRITC-LPS (lipopolysaccharide) in solution with polymyxin B immobilized on a fiberoptic biosensor, the order of dependence of the binding-rate coefficients, k₁ and k₂ on the fractal dimensions, D_f1 and D_f2, were 7.6335 and-11.55, respectively. The fractional order of dependence of thek(s) on the D_f(s) further reinforces the fractal nature of the system. Thek(s) expressions developed as a function of the D_f(s) are of particular value, since they provide a means to better control biosensor performance, by linking it to the heterogeneity on the surface, and further emphasize, in a quantitative sense, the importance of the nature of the surface in biosensor performance.