Combined supercritical-fluid chromatography/mass spectrometry in the analysis of diuron in plasma using on-line phase-system switching

On-line sample pretreatment by means of the phase-system switching approach is an interesting technique for the analysis of aqueous samples, e.g., plasma, by means of supercritical-fluid chromatography. In order to analyse plasma samples the following analytical procedure is used. The plasma sample is injected on to a short precolumn, which is washed with water and subsequently dried with nitrogen. Next, the solutes are desorbed with the supercritical mobile phase, analysed with packed-column supercritical-fluid chromatography and detected with either a UV detector or a mass spectrometer, equipped with a moving-belt interface. The herbicide diuron is selected as a test compound to study the feasibility of this approach. Using a selective detector the procedure is sufficiently sensitive to detect diuron in plasma, but not appropriate to detect the diuron metabolites in a post-mortem plasma sample. These have been identified with liquid chromatography/mass spectrometry. The detection limit of diuron in plasma using the procedure described is about 30 ng/mL.     

Control of small phase arrays to form a prescribed amplitude-phase distribution

Translated from Izvestiya Vysshikh Uchebnykh Zavedenii, Radiofizika, Vol. 32, No. 9, pp. 1132–1136, September, 1989.     

The effect of oxygen ordering through controlled exposure to oxygen on the superconductive properties of LaBa2Cu3Oy

In an effort to clarify the role of controlled oxygen exposure in the synthesis of LaBa₂Cu₃O_y samples which achieve zero resistivity above 90 K, we have prepared samples of La(_1+x)Ba(_2–x)Cu₃O_y with x approximately equal to 0 and with y varying between 6.22 and 7.00. We find that for large values of y, 6.88相似文献   

Resolution of Some Open Problems Concerning Multiple Zeta Evaluations of Arbitrary Depth

We prove some new evaluations for multiple polylogarithms of arbitrary depth. The simplest of our results is a multiple zeta evaluation one order of complexity beyond the well-known Broadhurst–Zagier formula. Other results we provide settle three of the remaining outstanding conjectures of Borwein, Bradley, and Broadhurst. A complete treatment of a certain arbitrary depth class of periodic alternating unit Euler sums is also given.     

Chemisorption at interfaces between organic semiconductors and metals: role of the electron affinity

Chemisorption is a well-known phenomenon for many interfaces between organic semiconductors and metals. It is shown that many published data indicate that the occurrence of chemisorption can be rationalized upon the consideration of the metal work function versus the electron affinity of the organic semiconductor.     

The Sample Average Approximation Method Applied to Stochastic Routing Problems: A Computational Study

The sample average approximation (SAA) method is an approach for solving stochastic optimization problems by using Monte Carlo simulation. In this technique the expected objective function of the stochastic problem is approximated by a sample average estimate derived from a random sample. The resulting sample average approximating problem is then solved by deterministic optimization techniques. The process is repeated with different samples to obtain candidate solutions along with statistical estimates of their optimality gaps.We present a detailed computational study of the application of the SAA method to solve three classes of stochastic routing problems. These stochastic problems involve an extremely large number of scenarios and first-stage integer variables. For each of the three problem classes, we use decomposition and branch-and-cut to solve the approximating problem within the SAA scheme. Our computational results indicate that the proposed method is successful in solving problems with up to 2¹⁶⁹⁴ scenarios to within an estimated 1.0% of optimality. Furthermore, a surprising observation is that the number of optimality cuts required to solve the approximating problem to optimality does not significantly increase with the size of the sample. Therefore, the observed computation times needed to find optimal solutions to the approximating problems grow only linearly with the sample size. As a result, we are able to find provably near-optimal solutions to these difficult stochastic programs using only a moderate amount of computation time.