1000 resultados para cefaclor determination


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Cefaclor is not reducible at a mercury electrode, but it can be determined polarographically and by cathodic stripping voltammetry as its initial alkaline degradation product which is obtained in high yield by hydrolysis of cefaclor in Britton-Robinson (B-R) buffer pH 10 at 50 degrees C for 30 min (reduction peak at pH 10, -0.70 V). Differential pulse polarographic calibration graphs are linear up to at least 1 x 10(-4) mol l(-1). Recoveries of 93% of the cefaclor (n = 3) were obtained from urine spiked with 38.6 mu g ml(-1) using this polarographic method with 1 ml urine made up to 10 ml with pH 10 buffer. Using cathodic stripping voltammetry and accumulating at a hanging mercury drop electrode at -0.2 V for 30 s, linear calibration graphs were obtained from 0.35 to 40 mu g ml(-1) cefaclor in B-R buffer pH 10. A relative standard deviation of 4.2% (eta = 5) was obtained, and the limit of detection was calculated to be 2.9 ng ml(-1). Direct determination of cefaclor in human urine (1 ml of urine was made up to 10 ml with pH 10 buffer) spiked to 0.39 mu g ml(-1) was made (recovery 98.6%). (C) 1999 Elsevier B.V. B.V. All rights reserved.

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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A sensitive method is described for the determination of cefaclor by cathodic stripping voltammetry at the hanging mercury drop electrode. cefaclor is accumulated at the electrode surface as a mercury salt, which is reduced at -0.67 V. The optimum accumulation potential and accumulation time were +0.15 V and up to 180 s, respectively. Linear calibration graphs were obtained between 3.9 mu g.L-1 to 39 mu g.L-1 and the limit of determination was evaluated to be 1.9 mu g.L-1. The method was applied successfully to the determination of cefaclor in pharmaceutical formulations.

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The time for conducting Preventive Maintenance (PM) on an asset is often determined using a predefined alarm limit based on trends of a hazard function. In this paper, the authors propose using both hazard and reliability functions to improve the accuracy of the prediction particularly when the failure characteristic of the asset whole life is modelled using different failure distributions for the different stages of the life of the asset. The proposed method is validated using simulations and case studies.