ToF-SIMS analysis of surface-anchored organometallic clusters

ToF-SIMS analysis of the organometallic cluster Os₃(μ-H)(CO)₁₀(μ-SCH₂CH₂SH) chemically attached onto a silver or gold surface, and of the cluster fragment Os₃(μ-H)(CO)₁₀(μ-OSi) on a silica surface, allows for identification of the surface organometallic species, and also gives information on its reactivity.     

Persulphate initiated polymerisation of methacrylamide—I: Kinetics and molecular weight dependences

A kinetic study of the aqueous persulphate initiated polymerisation of methacrylamide has shown that the rate of polymerisation is represented by the equation $\text{R}{}_{\mathrm{p}}\text{= k [}\text{M}\text{] [}\text{I}\text{]}{}^{\mathrm{<mtext>1</mtext><mtext>2</mtext>}}$ where the overall rate constant, $\text{k = 1.3 × 10}{}^9\text{exp}\text{(?18,400/}\text{RT}\text{) 1}{}^{\mathrm{<mtext>1</mtext><mtext>2</mtext>}}\text{mol}{}^{\mathrm{<mtext>?</mtext><mtext>1</mtext><mtext>2</mtext>}}\text{s}{}^{\mathrm{?1}}$. Chain transfer with monomer, where C_M = 5.4 × 10^?3 at 60°, is shown to be the dominant transfer step. Comparison with kinetic studies of the analogous acrylamide polymerisation shed doubt on the ‘cage effect’ interpretation of complex orders with respect to monomer. An alternative explanation is proposed.     

Gas chromatographic-mass spectrometric analysis of acids and phenols in distilled alcohol beverages. Application of anion-exchange disk extraction combined with in-vial elution and silylation

A GC-MS protocol for profiling spirits, based on 19 acids and phenolic compounds, has been proposed and evaluated. The method combined a simple preconcentration procedure based on solid-phase (anion-exchange) disk extraction, and in-vial elution and silylation of the analytes. The derivatized extract was directly injected into the GC-MS system. These analytes were: C6, C8, C10, C12 acids, pyruvic acid, 2-furoic acid, succinic acid, fumaric acid, glutaric acid, lactic acid, glycolic acid, malic acid, tartaric acid, citric acid, vanillin, syringaldehyde, coniferaldehyde, vanillic acid and gallic acid. The profiles of six different spirits were found reproducible from day-to-day with <20% RSD for measurements of most of the analytes at different concentrations. Recoveries of individual analytes appear to be affected by the level of tannins in the spirits, and they varied from sample to sample. The method of standard addition was used to quantify age-related analytes. Good linearity of response with correlation coefficients in the range of 0.992-0.999 was obtained. The results of the study indicate that for spirits of the same brand but of different ages, the amounts of these analytes appear to increase with the ageing period.     

Fastiterative methods for least squares estimations

Least squares estimations have been used extensively in many applications, e.g. system identification and signal prediction. When the stochastic process is stationary, the least squares estimators can be found by solving a Toeplitz or near-Toeplitz matrix system depending on the knowledge of the data statistics. In this paper, we employ the preconditioned conjugate gradient method with circulant preconditioners to solve such systems. Our proposed circulant preconditioners are derived from the spectral property of the given stationary process. In the case where the spectral density functions() of the process is known, we prove that ifs() is a positive continuous function, then the spectrum of the preconditioned system will be clustered around 1 and the method converges superlinearly. However, if the statistics of the process is unknown, then we prove that with probability 1, the spectrum of the preconditioned system is still clustered around 1 provided that large data samples are taken. For finite impulse response (FIR) system identification problems, our numerical results show that annth order least squares estimator can usually be obtained inO(n logn) operations whenO(n) data samples are used. Finally, we remark that our algorithm can be modified to suit the applications of recursive least squares computations with the proper use of sliding window method arising in signal processing applications.Research supported in part by HKRGC grant no. 221600070, ONR contract no. N00014-90-J-1695 and DOE grant no. DE-FG03-87ER25037.