An activity coefficient model, which is a combination of the entropic-free volume combinatorial term and a UNIFAC residual term, referred to as EFV/UNIFAC, is applied to the correlation and prediction of the molecular weight effect on liquid−liquid equilibria (LLE) of aqueous poly(ethylene glycol) (PEG) solutions. Interaction parameters between the end group of PEG and its repeating unit, as well as between the end group and the water molecule, are estimated by fitting activity coefficient experimental data of mixtures containing low molecular weight compounds. Interaction parameters between the water molecule and the repeating unit of PEG are determined from experimental LLE data for a single PEG molecular weight with water. These interaction parameters are used to predict the effect of the polymer molecular weight on miscibility. The proposed model provides a good compromise between accuracy and simplicity.
Abstract. Analytical work, especially for environmental purposes, involves in the typical case determination of organic compounds in compartments, where their presence is very small. Identi®cation of the appropriate experimental technique, whose range of applicability covers the concentration value involved, requires an estimate of this value. To this purpose, we present in this study a methodology for the prediction of the concentration of organic pollutants in the various environmental compartments (aquatic biota, air, sediment and soil) from the knowledge of the concentration in one of them. In case where the pollutant's concentration is not available for any of the compartments, the proposed methodology can be used to estimate the maximum expected concentration in each one of them assuming that the water phase is saturated with the pollutant. The latter value can be obtained from a simple correlation presented here.The methodology is based on the correlation of the partition coef®cients of pollutants among the various environmental compartments with two important thermodynamic quantities of pollutants: the octanol/ water partition coef®cient and the Henry's law coefficient, which can be easily predicted by simple models presented here. Application of the methodology to ®eld experimental data gives very satisfactory results at least for identifying the appropriate experimental technique.Key words: Pollutants; prediction; environmental compartments; octanol-water partition coef®cient; Henry's law coef®cient; solubility.Application of a certain experimental technique, for the measurement of the concentration of pollutants in the environment, has a certain range of applicability. For example Albanis and Hela [1] present detection limit concentrations for a variety of pesticides in water using gas chromatography with¯ame thermionic detection (GC-FTD) and mass selective detection (GC-MSD) using two different capillary columns: a DB-1 for GC-FTD and a HP-5 one for GC-MSD. Some of this information is presented in Table 1 and shows that detection limits can differ within the same method for the different pesticides as well as for the same pesticide with the two different methods by more than two orders of magnitude. In order, thus, for the experimentalist to decide on the most appropriate technique for the measurement of the concentration of a particular pollutant in a certain environmental compartment (aquatic biota, air, water, soil, and sediment) it is very useful to have a ®rst estimate of its concentration.We present here a methodology that provides this ®rst estimate for two possible cases. In the ®rst, the concentration is known in one of the compartments, usually water; in the second, the concentration is not known in any of the compartments. The methodology is applicable for slowly bio-degrading chemical pollutants, such as pesticides, polycyclic aromatic hydrocarbons (PAH's), halogenated compounds, polychlorinated biphenyls (PCB's) etc., whose distribution among the various environmental compartments reach ...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.