27,502 research outputs found

    On the Stabilizing Action of Protein Denaturants: Acetonitrile Effect on Stability of Lysozyme in Aqueous Solutions

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    Stability of hen lysozyme in the presence of acetonitrile (MeCN) at different pH values of the medium was studied by scanning microcalorimetry with a special emphasis on determination of reliable values of the denaturational heat capacity change. It was found that the temperature of denaturation decreases on addition of MeCN. However, the free energy extrapolation showed that below room temperature the thermodynamic stability increases at low concentrations of MeCN in spite of the general destabilizing effect at higher concentrations and temperatures. Charge-induced contribution to this stabilization was shown to be negligible (no pH-dependence was found); therefore, the most probable cause for the phenomenon is an increase of hydrophobic interactions at low temperatures in aqueous solutions containing small amounts of the organic additive. The difference in preferential solvation of native and denatured states of lysozyme was calculated from the stabilization free energy data. It was found that the change in preferential solvation strongly depends on the temperature in the water-rich region. At the higher MeCN content this dependence decreases until, at 0.06 mole fractions of MeCN, the difference in the preferential solvation between native and denatured lysozyme becomes independent of the temperature over a range of 60 K. The importance of taking into account non-ideality of a mixed solution, when analyzing preferential solvation phenomena was emphasized

    Selection of elastomeric membranes for the removal of volatile organics from water

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    A wide range of homogeneous elastomeric membranes has been prepared using dicumylperoxide as a general cross-linking agent. The membranes have been used for both equilibrium sorption measurements and steady-state pervaporation experiments to study solution-diffusion phenomena in the removal of volatile organic components from aqueous solutions. Pervaporation experiments have been performed under identical hydrodynamic conditions in order to fix the boundary layer mass transfer coefficient at a constant and known value. For comparison of the permeabilities of different pervaporation membrane materials, this is of utmost importance. A wide range of selectivity factors up to a value of 100,000 are obtained, whereas usually the permeabilities for the organic component are in the range of 10-10-10-9m2/s and 10-14-10-12m2/s for water. The permeation and sorption data obtained for the various elastomers have been related to the chemical and physical nature of the elastomers through the solubility parameter and the glass transition temperature, respectively. Both diffusional and sorption effects seem to be important, determining the water-transport behavior in the elastomeric membranes. The solubility of the organic component appears to be independent of this combined solubility parameter. Differences in the permeabilities of the organic component can primarily be ascribed to structural parameters in the membrane material, like degree of unsaturation and presence of steric side groups

    An evaluation of thermodynamic models for the prediction of drug and drug-like molecule solubility in organic solvents

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    Prediction of solubility of active pharmaceutical ingredients (API) in different solvents is one of the main issue for crystallization process design. Experimental determination is not always possible because of the small amount of product available in the early stages of a drug development. Thus, one interesting perspective is the use of thermodynamic models, which are usually employed for predicting the activity coefficients in case of Vapour–Liquid equilibria or Liquid–Liquid equilibria (VLE or LLE). The choice of the best thermodynamic model for Solid–Liquid equilibria (SLE) is not an easy task as most of them are not meant particularly for this. In this paper, several models are tested for the solubility prediction of five drugs or drug-like molecules: Ibuprofen, Acetaminophen, Benzoic acid, Salicylic acid and 4-aminobenzoic acid, and another molecule, anthracene, a rather simple molecule. The performance of predictive (UNIFAC, UNIFAC mod., COSMO-SAC) and semi-predictive (NRTL-SAC) models are compared and discussed according to the functional groups of the molecules and the selected solvents. Moreover, the model errors caused by solid state property uncertainties are taken into account. These errors are indeed not negligible when accurate quantitative predictions want to be performed. It was found that UNIFAC models give the best results and could be an useful method for rapid solubility estimations of an API in various solvents. This model achieves the order of magnitude of the experimental solubility and can predict in which solvents the drug will be very soluble, soluble or not soluble. In addition, predictions obtained with NRTL-SAC model are also in good agreement with the experiments, but in that case the relevance of the results is strongly dependent on the model parameters regressed from solubility data in single and mixed solvents. However, this is a very interesting model for quick estimations like UNIFAC models. Finally, COSMO-SAC needs more developments to increase its accuracy especially when hydrogen bonding is involved. In that case, the predicted solubility is always overestimated from two to three orders of magnitude. Considering the use of the most accurate equilibrium equation involving the ΔCp term, no benefits were found for drug predictions as the models are still too inaccurate. However, in function of the molecules and their solid thermodynamic properties, the ΔCp term can be neglected and will not have a great impact on the results

    New and extended parameterization of the thermodynamic model AIOMFAC: calculation of activity coefficients for organic-inorganic mixtures containing carboxyl, hydroxyl, carbonyl, ether, ester, alkenyl, alkyl, and aromatic functional groups

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    We present a new and considerably extended parameterization of the thermodynamic activity coefficient model AIOMFAC (Aerosol Inorganic-Organic Mixtures Functional groups Activity Coefficients) at room temperature. AIOMFAC combines a Pitzer-like electrolyte solution model with a UNIFAC-based group-contribution approach and explicitly accounts for interactions between organic functional groups and inorganic ions. Such interactions constitute the salt-effect, may cause liquid-liquid phase separation, and affect the gas-particle partitioning of aerosols. The previous AIOMFAC version was parameterized for alkyl and hydroxyl functional groups of alcohols and polyols. With the goal to describe a wide variety of organic compounds found in atmospheric aerosols, we extend here the parameterization of AIOMFAC to include the functional groups carboxyl, hydroxyl, ketone, aldehyde, ether, ester, alkenyl, alkyl, aromatic carbon-alcohol, and aromatic hydrocarbon. Thermodynamic equilibrium data of organic-inorganic systems from the literature are critically assessed and complemented with new measurements to establish a comprehensive database. The database is used to determine simultaneously the AIOMFAC parameters describing interactions of organic functional groups with the ions H^+, Li^+, Na^+, K^+, NH_(4)^+, Mg^(2+), Ca^(2+), Cl^−, Br^−, NO_(3)^−, HSO_(4)^−, and SO_(4)^(2−). Detailed descriptions of different types of thermodynamic data, such as vapor-liquid, solid-liquid, and liquid-liquid equilibria, and their use for the model parameterization are provided. Issues regarding deficiencies of the database, types and uncertainties of experimental data, and limitations of the model, are discussed. The challenging parameter optimization problem is solved with a novel combination of powerful global minimization algorithms. A number of exemplary calculations for systems containing atmospherically relevant aerosol components are shown. Amongst others, we discuss aqueous mixtures of ammonium sulfate with dicarboxylic acids and with levoglucosan. Overall, the new parameterization of AIOMFAC agrees well with a large number of experimental datasets. However, due to various reasons, for certain mixtures important deviations can occur. The new parameterization makes AIOMFAC a versatile thermodynamic tool. It enables the calculation of activity coefficients of thousands of different organic compounds in organic-inorganic mixtures of numerous components. Models based on AIOMFAC can be used to compute deliquescence relative humidities, liquid-liquid phase separations, and gas-particle partitioning of multicomponent mixtures of relevance for atmospheric chemistry or in other scientific fields

    A molecular-based group contribution equation of state for the description of fluid phase behaviour and thermodynamic derivative properties of mixtures (SAFT-γ Mie)

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    An accurate knowledge of the thermophysical properties and phase behaviour of fluid mixtures is essential for the reliable design of products and processes across a wide range of chemical engineering applications, varying from the processing of petroleum fluids to the manufacturing of pharmaceuticals. Thermodynamic tools and, in the context of this work, group contribution (GC) methods are predictive approaches that are expected to play an important role in meeting these industrial needs. The principal focus of the work presented in this thesis is the development of a novel GC method based on the statistical associating fluid theory (SAFT): the SAFT-γ Mie approach. The method is developed based on a detailed molecular model and a realistic intermolecular potential, the Mie potential with variable attractive and repulsive ranges, for the description of interactions at a molecular level. Over the past decade, an increasing research effort has been devoted to developing formalisms that couple the accuracy of the SAFT equation of state (EoS) with the predictive capabilities of group contribution approaches. In the development of such methods one aims to overcome the limitations inherent to GC approaches based on activity coefficient models, such as in the well-established universal quasi-chemical functional group activity coefficient (UNIFAC) approach. A more recent landmark has been the development of heteronuclear methods within SAFT. The SAFT-γ EoS based on the square-well (SW) potential has been shown to describe accurately the phase behaviour of a wide variety of fluids. In the work presented in this thesis, SAFT-γ SW is applied to the study of the fluid phase behaviour of aqueous solutions of hydrocarbons. These mixtures are of high industrial relevance, and the accurate representation of their highly non-ideal nature is very challenging from a theoretical perspective. The SAFT-γ method is shown to perform comparatively well in predicting the behaviour of the systems examined. Nonetheless, some challenges are identified, such as the description of thermodynamic derivative properties and the description of near-critical fluid phase behaviour, where the performance of the method is shown to be less accurate. These challenges partially arise from the simplistic intermolecular square-well potential employed within SAFT-γ SW, which allows for a rigorous theoretical development, but fails to reproduce accurately finer aspects of the thermophysical behaviour of fluids, such as second-order derivative thermodynamic properties. These challenges are tackled here with the development of the SAFT-γ Mie GC approach, based on the versatile Mie intermolecular potential and a third-order treatment of the thermodynamics of the monomer segments. The SAFT-γ Mie method is applied to the study of the properties of two chemical families, n-alkanes and 2- ketones, and it is shown that a significant improvement over existing SAFT-based group contribution approaches can be achieved in the description of the pure component phase behaviour of the compounds studied. Moreover, the application of a realistic intermolecular potential is shown to allow for an excellent description of second-order derivative thermodynamic properties, and the accurate treatment of the intersegment interactions is shown to improve the performance of the method in the description of the near-critical fluid phase behaviour. The predictive capability of the method is demonstrated in the description of mixture fluid phase behaviour and excess thermodynamic properties in a predictive manner. Given the promising performance of the SAFT-γ Mie EoS, the method is applied to the case study of the solubility of two active pharmaceutical ingredients in organic solvents. The method is shown to satisfactorily predict the solubilities of the mixtures considered, based on limited experimental data for simple systems. Given the complexity of the mixtures studied, the performance of the SAFT-γ Mie is considered very encouraging and shows that there is great potential in the application of the method to this challenging field

    In Silico Prediction of Physicochemical Properties

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    This report provides a critical review of computational models, and in particular(quantitative) structure-property relationship (QSPR) models, that are available for the prediction of physicochemical properties. The emphasis of the review is on the usefulness of the models for the regulatory assessment of chemicals, particularly for the purposes of the new European legislation for the Registration, Evaluation, Authorisation and Restriction of CHemicals (REACH), which entered into force in the European Union (EU) on 1 June 2007. It is estimated that some 30,000 chemicals will need to be further assessed under REACH. Clearly, the cost of determining the toxicological and ecotoxicological effects, the distribution and fate of 30,000 chemicals would be enormous. However, the legislation makes it clear that testing need not be carried out if adequate data can be obtained through information exchange between manufacturers, from in vitro testing, and from in silico predictions. The effects of a chemical on a living organism or on its distribution in the environment is controlled by the physicochemical properties of the chemical. Important physicochemical properties in this respect are, for example, partition coefficient, aqueous solubility, vapour pressure and dissociation constant. Whilst all of these properties can be measured, it is much quicker and cheaper, and in many cases just as accurate, to calculate them by using dedicated software packages or by using (QSPRs). These in silico approaches are critically reviewed in this report.JRC.I.3-Toxicology and chemical substance
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