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Published on: June 10, 2018
Aqueous size exclusion chromatography applied to polymer analysis: experimental conditions and molecular weight
Rocío B Pellegrino Vidal1, Federico N Castañeda1, Mariano E Garrido2
1Laboratorio de Investigación y Desarrollo en Métodos Analíticos (LIDMA)-CIC-PBA, Facultad de Ciencias Exactas, Universidad Nacional de La Plata (UNLP), Calle 49 y 115 (B1900AJL), La Plata, Argentina; División Química Analítica, Facultad de Ciencias Exactas, Universidad Nacional de La Plata, (UNLP), 47 y 115 (B1900AJL), Ciudad de La Plata, Provincia de Buenos Aires, Argentina.
Abstract:
Aqueous mode size exclusion chromatography (SEC) was employed for the analysis and construction of molecular weight (MW) calibration curves of three water-soluble polymers, namely, polyethylene glycol, polyethylene oxide, and polyacrylic acid sodium salt. Several calibration curves were obtained, varying chromatographic conditions such as columns arrangement, ionic strength, temperature and pH, in addition trends in polymeric chromatographic behavior were examined. The variation in SEC distribution coefficients at different temperatures was found to be below 10 %, indicating that the studied polymers follow an ideal SEC mechanism under the tested conditions. Thus, differences in chromatographic behavior were ascribed to changes in polymer configuration induced by media and/or temperature. These variations in morphology were consistent with the observed SEC behavior. Regarding MW calibration, polynomial regression models ranging from first to fifth order were applied, and the most adequate ones were selected based on their fit and prediction capabilities. Third order polynomials were the preferred models for polyethylene glycol and polyacrylic acid sodium salt, independently of chromatographic conditions. Meanwhile for polyethylene oxide, either third or fifth-order polynomial models were optimal depending on the chromatographic conditions. All the selected regression models presented coefficients of multiple determination (R2) above 0.990, while achieving relative errors of prediction (REP%) in MW ranging from 0.3 to 4 % for cross-validation.
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