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Viscosimetric detection in size‐exclusion chromatography (SEC/GPC): The Goldwasser method and beyond
Author(s) -
Castignolles Patrice,
Gaborieau Marianne
Publication year - 2010
Publication title -
journal of separation science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.72
H-Index - 102
eISSN - 1615-9314
pISSN - 1615-9306
DOI - 10.1002/jssc.201000511
Subject(s) - size exclusion chromatography , viscometer , polymer , gel permeation chromatography , chromatography , calibration , volume (thermodynamics) , detector , chemistry , detection limit , analytical chemistry (journal) , molar mass distribution , range (aeronautics) , calibration curve , biological system , materials science , mathematics , viscosity , statistics , thermodynamics , physics , organic chemistry , optics , composite material , enzyme , biology
Abstract Size‐exclusion chromatography (SEC or GPC) is the most widely used separation method to characterize polymers. The high level of complexity of most polymeric materials necessitates the use of not only concentration‐sensitive detection but also structure‐sensitive detection. Viscometry is usually used in conjunction with a concentration‐sensitive detector and universal calibration to determine molecular weights of polymers. Goldwasser proposed to use a viscometer as a single detector to determine number‐average molecular weights, M n (ACS Symposium Series, 521, 143). The method is particularly of interest when concentration‐sensitive detection is not available, because the sample is isorefractive or not UV‐absorbing, or because composition is not constant (copolymers). It has known very little applications so far. It actually does not only allow determining M n , but also the number hydrodynamic volume distribution. This opens a wider range of applications for the Goldwasser method. Size‐exclusion chromatography only yields inaccurate molecular weight distributions for some complex branched polymers. Hydrodynamic volume distributions have then a strong potential for comparative studies owing to their far higher accuracy. Our experimental tests highlight the fact that the method is highly sensitive to noise and careful optimization of the injection concentration is needed, but number distribution can be obtained as well as M n .