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  • Title: Size-exclusion chromatography with evaporative light scattering detection: Method for determination of polydimethylsiloxanes. I. Testing dependence of molecular weight of polydimethylsiloxanes and injected mass upon the detector signal.
    Author: Mojsiewicz-Pieńkowska K.
    Journal: J Chromatogr B Analyt Technol Biomed Life Sci; 2008 Apr 01; 865(1-2):1-6. PubMed ID: 18343207.
    Abstract:
    In recent years the evaporative light scattering detector has become a promising device in the analysis of variable chemical compounds using liquid chromatography. Due to the detection specificity, based on the scattering of the laser light on non-volatile analyte particles, this detector is considered a most universal one. Many authors consider detector signal as a mass signal and subsequently, evaporative light scattering detector has been regarded as a mass detector. Although the scientists pinpoint to many advantages of this device, many of its drawbacks were also noticed. Due to variable examinations carried out some scientist characterised the detector response as a non-linear, seeing in fact a significant limitation of this detector for the purposes of quantitative tests. The author of the present study researched, in many ways, for the solution to this problem, by carrying out tests on polydimethylsiloxanes (PDMS) of a linear structure. The aim of this study was to test the dependence of the evaporative light scattering detector signal upon the molecular weight of PDMS of a linear structure and viscosity ranging from 10 to 60,000 cSt and the injected mass. The evaluation of function monotonicity of the detector response and determination of the function for particular analytes referred to the mass ranges of 8.9-149.0 microg. In order to find the dependence of the integrated signal value of the detector signal intensity, expressed as a surface area in mug, upon analyte mass for particular PDMS, several analytical functions and formulas were used. Parameters of regression equations were calculated for linear and non-linear functions as well as their logarithmic transformations. The aim of the research for the optimal regression equation could mean increased reliability of results obtained from analyses of PDMS.
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