Universal Quasichemical Functional Group Activity Research Articles

In-depth analysis of the acetylcholinesterase inhibitors of Ganoderma amboinense based receptor-ligand affinity coupled with complex chromatography

Given the abundance and intricate nature of the chemical components found in natural food ingredients, functional food research has long grappled with two significant technical challenges: rapid and precise screening of active substances and targeted preparation. This study outlines a novel methodology to enhance the efficiency of preparing natural products, presenting new extraction processes, biological activity screening, and the preparation of acetylcholinesterase inhibitors from Ganoderma amboinense. acetylcholinesterase inhibitors were rapidly screened using ultrafiltration coupled with liquid chromatography and mass spectrometry. The inhibitory effect of active compounds on acetylcholinesterase was investigated via enzymatic reaction kinetics, with the mechanism also explored. Additionally, molecular docking and molecular dynamics simulations were used to verify the anti-alzheimer's disease activity of active compounds. Following this, the biological activity of ligands identified were separated and purified using complex chromatography (consecutive-CCC and semi-preparative HPLC). We used n-hexane: ethyl acetate: methanol: water (4.0:6.0:4.0:6.0, v/v/v/v), which optimized by a universal quasichemical functional group activity coefficient-based mathematical model and semi-preparative HPLC, 25–60% A (0–50 min), 60–100% A (50–60 min). Twelve active ingredients viz. ganoderic acids I, C2, G, B, K, A, D, F, J, ganoderenic acid A, deacetyl ganoderic acid F, and lucidenic acid F with the binding affinity for AChE were identified and isolated from natural foods. We enhance the efficiency and purity of active compounds preparation by incorporating enzyme inhibition assays and computerized virtual screening with compound chromatography. With this approach, we seamlessly incorporate fast discovery, screening, and focused preparation of active ingredients, as well as delving into the mechanisms of action of active monomers and proteases, and the molecular-level interactions between active ingredients and proteases. This approach effectively mitigates the occurrence of false positives attributable to a single technique, thereby providing valuable data to underpin future advances in enzyme inhibitor screening and isolation.

Surface tension of binary and ternary mixtures mapping with ASP and UNIFAC models based on machine learning

Modeling predictions of surface tension for binary and ternary liquid mixtures is difficult. In this work, we propose a machine learning model to accurately predict the surface tension of binary mixtures of organic solvents-ionic liquids and ternary mixtures of organic solvents-ionic liquids–water and analytically characterize the proposed model. In total, 1593 binary mixture data points and 216 ternary mixture data points were collected to develop the machine learning model. The model was developed by combining machine learning algorithms, UNIFAC (UNIversal quasi-chemical Functional group Activity Coefficient) and ASP (Abraham solvation parameter). UNIFAC parameters are used to describe ionic liquids, and ASP is used to describe organic solvents. The effect of each parameter on the surface tension is characterized by SHAP (SHapley Additive exPlanation). We considered support vector regression, artificial neural network, K nearest neighbor regression, random forest regression, LightGBM (light gradient boosting machine), and CatBoost (categorical boosting) algorithms. The results show that the CatBoost algorithm works best, MAE = 0.3338, RMSE = 0.7565, and R2 = 0.9946. The SHAP results show that the surface tension of the liquid decreases as the volume and surface area of the anion increase. This work not only accurately predicts the surface tension of binary and ternary mixtures, but also provides illuminating insight into the microscopic interactions between physical empirical models and physical and chemical properties.

Predicting viscosity of ionic liquids - water mixtures by bridging UNIFAC modeling with interpretable machine learning

Predicting the viscosity of ionic liquids (ILs)-water mixtures precisely is considerable for diversity applications in chemical industries. In this work, interpretable machine learning models incorporate physics information developed to link the UNIversal quasi-chemical Functional group Activity Coefficient (UNIFAC) models as new predictive tools for the viscosity of ILs-water mixtures. Machine learning algorithms including Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), K-Nearest-Neighbour Regression (KNNR) and Random Forest (RF) are compared, with the Catboost model achieving the best accuracies (RMSE = 0.0084, MSE = 0.0001, MAE = 0.0020, R2 = 0.9941). We demonstrate that the UNIFAC model and the Stokes-Einstein relation assisted to reduce the feature dimensionality, and improve the predictive power of the viscosity of ILs-water mixtures. The describe of the Shapley’s additive explanations (SHAP) and Partial dependence plots (PDP) are giveing expression to the features importance of the UNIFAC model. Our work created a new paradigm to hyperlink the machine learning models with ILs-related properties, the UNIFAC model contains prior physical information to optimize the features of the experiment, and fill the gap of the input features and the viscosity of ILs-water mixtures.

Designing and Characterization of Low-Temperature Eutectic Phase Change Materials Based on Alkanes

The research presented here focuses on investigating the relationship between the structure of components and the eutectic temperature of binary systems designed as phase change material (PCM). For two-component systems containing long-chain alkanes (n-tetradecane, n-pentadecane, n-hexadecane, or n-heptadecane) and decanoic acid or 1-decanol, experimental data of the solid–liquid equilibrium are presented. The collected data were used to determine the composition and temperature of eutectic mixtures that are promising PCMs with a narrow melting point range. An essential aspect of this work is to confirm the possibility of determining eutectics of similar systems using predictive models, mainly the modified Dortmund universal quasichemical functional group activity coefficients method, without the need to perform expensive and time-consuming experimental research. The experiments were supplemented with the physicochemical characteristics (density and viscosity) of the liquid mixtures. The obtained PCM was closed in a polymer matrix to prevent material leakage into the environment, and the obtained composite materials were characterized by using scanning electron microscopy and differential scanning calorimetry techniques.

Vapor-liquid equilibrium in methanol + water system and modeling from 298.15 to 373.15 K

Vapor-liquid equilibrium (VLE) relationships are needed in the solution of many engineering problems. The required data can be found by performing an experiment. But such measurements are not so easy, even for binary systems. The fundamental idea of a solution- of- groups model is to utilize existing phase equilibrium data for predicting phase equilibria of systems for which no experimental data are available. The popular predictive method of industries called the UNIFAC (Universal Quasi-Chemical Functional Group Activity Coefficient) group contribution model has been found to be successful to represent the available experimental data of VLE for wide variety of mixtures at various values of temperatures and pressures. In this work, the vapour-liquid equilibrium (VLE) for the methanol (1) + water (2) system has been investigated at temperatures ranging from 298.15 K to 373.15 K. The VLE data of methanol (1) + water (2) mixture for this temperature range have been also regressed using UNIFAC model available in data pro ii. The thermodynamic consistency test of experimental data such as point test has been also performed. The phase equilibrium data generated in this work are relatively good in agreement with previous data reported in the literature.

Modeling, simulation, and optimization for producing ultra-low sulfur and high-octane number gasoline by separation and conversion of fluid catalytic cracking naphtha

Contradiction between the desulfurization and loss of octane number (ON) for fluid catalytic cracking (FCC) naphtha is the long-term problem of clean gasoline production. Moreover, ON loss is substantially aggravated during deep hydrodesulfurization because of the global limited supply of sulfur for gasoline. This paper introduced a novel process for ultra-clean gasoline production that solves the above problem by using a unique separation approach. The components were separated by the coupling of distillation and solvent extraction, which the separation efficiency depends on the relationship between solvent and feedstock components. However, the compositions of FCC naphtha are complicated and always changing due to different processing technologies. Therefore, changes in the material composition must be considered during production. In this study, ternary liquid–liquid equilibrium (LLE) was explored to investigate the interaction of olefin, sulfide, and sulfolane solvent. A universal quasi-chemical functional group activity coefficient (UNIFAC) group contribution model was selected to predict the product composition when the FCC naphtha was treated by this process. The lack and necessity of group interaction parameters for the prediction were obtained through the regressive calculation of LLE results. A simulation method was established by Aspen Plus, and the reliability was well illustrated. Finally, the high olefin content of FCC naphtha critical components was analyzed by this simulation method. The sulfur and olefin levels of the product were 9.0 mg/kg and 18.6 wt%, respectively. The operation conditions simulated using the proposed technique met the requirements for the latest commercial gasoline worldwide.

Desulfurization of Fluid Catalytic Cracking Naphtha by Extractive Distillation: A Universal Simulation Method Established Using the Universal Quasi-Chemical Functional Group Activity Coefficient Model

The extractive distillation process is gradually being used for the desulfurization of fluid catalytic cracking (FCC) naphtha because of its excellent octane number protection performance during desulfurization. The key to this process is the separation of sulfides. However, the composition of FCC naphtha is complicated and easily changes, which seriously affect the separation performance. The operating conditions need to be adjusted frequently with the change in the different properties of feedstocks, which cost time, manpower, and investment. In this study, we introduce a simulation method using the universal quasi-chemical functional group activity coefficient (UNIFAC) model to predict the process of desulfurization. In the simulation, the group binary parameters for the UNIFAC model seriously affect the reliability of the method. Therefore, the lacking parameters of the solvent and sulfides were first regressively calculated by the experimental results of the binary vapor–liquid equilibrium (VLE). All the values of (D–J) by the Herington integral method were under 10, which means that the parameters obtained in this work were well correlated to those of the VLE experiments. Based on this, the prediction simulation method was established and the maximum error of the compositions of the products between simulation and real-oil experiment was only 0.5 wt %. According to the simulation, 98% of sulfides could be extracted under the optimal conditions (113 °C at the extraction column, 178 °C at the solvent recycle column, etc.). The rule of the extraction of sulfides was thiophene sulfur > mercaptan sulfur > thioether sulfur, which is consistent with the VLE study results. The sulfur content of the raffinate oil was decreased to 9.3 mg/kg. It well met the latest gasoline standard and is appropriate for the prediction of the desulfurization of FCC naphtha by the extractive distillation process.

Hygroscopic growth of aerosol particles consisted of oxalic acid and its internal mixture with ammonium sulfate for the relative humidity ranging from 80% to 99.5%

The hygroscopicity of atmospheric aerosol particles plays an important role in their effects on the environment and the public health. Oxalic acid (OA) has been recognized as the dominant dicarboxylic acid in the urban and the remote aerosol particles, so a number of studies have investigated the hygroscopicity of aerosols consisted of pure OA and its mixture with inorganic salt. However, few experimental studies have focused on the hygroscopicity of nanoscale particles at high relative humidity (RH) below saturation of water vapor (i.e., RH = 90–100%) due to the limitation of traditional measuring instruments. In this work, the hygroscopic growth factor (GF) of the aerosol particles composed of pure OA and internally mixed OA-ammonium sulfate (AS) at RH = 80–99.5% were studied using a high humidity tandem differential mobility analyzer (HHTDMA). The experimental results were used to verify the applicability of models including the ideal solution model, the extended aerosol inorganics model (E-AIM) combining with the revised universal quasi-chemical functional group activity coefficients (UNIFAC-Peng) model, the Aerosol Inorganic–Organic Mixtures Functional groups Activity Coefficients (AIOMFAC) model and the Zdanovskii–Stokes–Robinson (ZSR) model. According to the experimental results, the effect of RH, the composition and the initial dry particle diameter (D0) on the hygroscopic growth factor (GF) and the effective hygroscopicity parameter (κ) of particles at RH = 80–99.5% were analyzed in details. It has been found that for pure OA particles with D0 = 80/100 nm, the prediction of the UNIFAC-Peng model and the AIOMFAC model becomes more accurate than the ideal solution model with the increasing of RH, while all models cannot describe the GF of smaller particles at RH = 80–99.5%. For mixed OA-AS aerosol particles, the chemical reaction is an important reason for the discrepancies between the HHTDMA results and the model prediction. For the pure OA aerosol particles at RH ≤ 96% and particles containing AS at RH ≤ 99.5%, the sensitivity of GF to D0 is positively and negatively correlated with RH and D0, respectively. However, for all aerosol types in this study, the sensitivity of GF to RH is positively correlated with both D0 and RH. In addition, the κ based on the measurement is clearly dependent on D0 (at RH = 95–99.5%), RH and the particle composition (at RH = 80–99.5%). The measured hygroscopicity of aerosol particles at high RH might be useful in addressing challenges in solving the discrepancies between the κ measured under subsaturated and supersaturated conditions.

Artificial neural network approach for predicting blood brain barrier permeability based on a group contribution method

Background and ObjectiveThe purpose of this study was to develop a quantitative structure-activity relationship (QSAR) model for the prediction of blood brain barrier (BBB) permeability by using artificial neural networks (ANN) in combination with molecular structure and property descriptors. MethodsUsing a database composed of 300 compounds, 52 structure descriptors obtained based on the universal quasichemical functional group activity coefficients (UNIFAC) group contribution method and the selected 8 molecular property descriptors were used as the network inputs, whereas logBB values of compounds constituted its output. ResultsThe correlation coefficient R of the constructed prediction model, the relative error (RE) and the root mean square error (RMSE) was 0.956, 0.857, and 0.171, respectively. These indicators reflected the feasibility, robustness and accuracy of the prediction model. Compared with the previously published results, a significant improvement in the predictions of the proposed ANN model was observed. ConclusionsANN model based on the group contribution method could achieve a satisfactory performance for logBB prediction.

Isolation of potential α-glucosidase inhibitor from Inonotus obliquus by combining ultrafiltration-liquid chromatography and consecutive high-speed countercurrent chromatography.

Inonotus obliquus is a rare medicinal fungus that contains several potential therapeutic ingredients. In this study, the α-glucosidase inhibitory activity of I. obliquus was examined, and a potential α-glucosidase inhibitor, (E)-4-(3,4-dihydroxyphenyl)but-3-en-2-one, was isolated from the I. obliquus extract through ultrafiltration-liquid chromatography (UF-LC). Consecutive high-speed countercurrent chromatography (HSCCC) was used for separation to obtain large quantities of the target compound. The universal quasi-chemical functional group activity coefficient (UNIFAC) model was utilized to prepare a two-phase solvent system, n-hexane/ethyl acetate/ethanol/water (4 : 4.5 : 3.5 : 5, v/v/v/v), wherein the proportions of n-hexane/ethyl acetate/ethanol/water in the stationary and mobile phases were 19.8 : 19.7 : 7.9 : 2.2 (v/v/v/v) and 1 : 16.4 : 57.5 : 136.6 (v/v/v/v), respectively. A flow rate of 2.5 mL min-1 and a column speed of 860 rpm were maintained. Consequently, 10.3 mg of the target compound (95.9% purity) was obtained from 900 mg (6 × 150 mg) of the I. obliquus extract. The use of the UNIFAC model, in combination with consecutive HSCCC separations, allows the purification of large quantities of samples over a short time. Furthermore, the volume of the organic solvent required is reduced. Thus, UF-LC is an effective technique for screening potential α-glucosidase inhibitors isolated from I. obliquus. This can ultimately aid in the discovery of bioactive compounds for the prevention and treatment of diabetes.

Transesterification of triolein and methanol with Novozym 435 using co-solvents

To enhance the reaction rate and yield during biodiesel synthesis with Novozym 435 (an immobilized lipase), a co-solvent method was applied, after which the relationships between reaction time and reaction yield during the single-phase enzymatic transesterification of triolein and methanol in the presence of co-solvents such as tetrahydrofuran, acetone, and hexane were investigated. The addition of hexane led to inactivation of Novozym 435, whereas in the presence of acetone or tetrahydrofuran, the reaction was accelerated and the yield was higher than with a conventional solvent-free enzymatic reaction. There was a clear difference in the dispersion of Novozym 435 particles between with and without co-solvents. The co-solvents prevented particle aggregation from occurring during the reaction without co-solvent. This is the likely reason for the enhanced reaction yield in the presence of co-solvents. To better understand the difference among the co-solvents employed, we estimated phase compositions during transesterification using the LLE (liquid-liquid equilibrium)-UNIFAC (UNIversal quasichemical Functional group Activity Coefficients) model, and examined the effect of these compositions on the reaction and dispersion of the glycerin by-product phase. Using the model, we also accounted for why hexane led to inactivation by estimating the transfer free energy of methanol, glycerin, and water between the initial bulk reaction solution and the water layer on the enzyme surface.

Flexible heuristic algorithm for automatic molecule fragmentation: application to the UNIFAC group contribution model

A priori calculation of thermophysical properties and predictive thermodynamic models can be very helpful for developing new industrial processes. Group contribution methods link the target property to contributions based on chemical groups or other molecular subunits of a given molecule. However, the fragmentation of the molecule into its subunits is usually done manually impeding the fast testing and development of new group contribution methods based on large databases of molecules. The aim of this work is to develop strategies to overcome the challenges that arise when attempting to fragment molecules automatically while keeping the definition of the groups as simple as possible. Furthermore, these strategies are implemented in two fragmentation algorithms. The first algorithm finds only one solution while the second algorithm finds all possible fragmentations. Both algorithms are tested to fragment a database of 20,000+ molecules for use with the group contribution model Universal Quasichemical Functional Group Activity Coefficients (UNIFAC). Comparison of the results with a reference database shows that both algorithms are capable of successfully fragmenting all the molecules automatically. Furthermore, when applying them on a larger database it is shown, that the newly developed algorithms are capable of fragmenting structures previously thought not possible to fragment.

The Impact of Biodiesel Fuel on Ethanol/Diesel Blends

The interest in biofuels was stimulated by the fossil fuel depletion and global warming. This work focuses on the impact of biodiesel fuel on ethanol/diesel (ED) fuel blends. The soybean methyl ester was used as a representative composition of typical biodiesel fuels. The heating and evaporation of ethanol–biodiesel–diesel (EBD) blends were investigated using the Discrete–Component (DC) model. The Cetane Number (CN) of the EBD blends was predicted based on the individual hydrocarbon contributions in the mixture. The mixture viscosity was predicted using the Universal Quasi-Chemical Functional group Activity Coefficients and Viscosity (UNIFAC–VISCO) method, and the lower heating value of the mixture was predicted based on the volume fractions and density of species and blends. Results revealed that a mixture of up to 15% biodiesel, 5% ethanol, and 80% diesel fuels had led to small variations in droplet lifetime, CN, viscosity, and heating value of pure diesel, with less than 1.2%, 0.2%, 2%, and 2.2% reduction in those values, respectively.

Blended E85–Diesel Fuel Droplet Heating and Evaporation

The multidimensional quasi-discrete (MDQD) model is applied to the analysis of heating and evaporation of mixtures of E85 (85 vol % ethanol and 15 vol % gasoline) with diesel fuel, commonly known as “E85–diesel” blends, using the universal quasi-chemical functional group activity coefficients model for the calculation of vapor pressure. The contribution of 119 components of E85–diesel fuel blends is taken into account, but replaced with smaller number of components/quasi-components, under conditions representative of diesel engines. Our results show that high fractions of E85–diesel fuel blends have a significant impact on the evolutions of droplet radii and surface temperatures. For instance, droplet lifetime and surface temperature for a blend of 50 vol % E85 and 50 vol % diesel are 23.2% and up to 3.4% less than those of pure diesel fuel, respectively. The application of the MDQD model has improved the computational efficiency significantly with minimal sacrifice to accuracy. This approach leads to a s...

Screening Monoethanolamine As Solvent to Extract Phenols from Alkane

Coal tar is a byproduct of low temperature coal carbonization. The separation of the compounds has great significance since its main component is the mixture of phenols and hydrocarbons. In this paper, the separation of specific phenolic compounds from model coal tar (phenols + hexane) was studied. The solvent was screened by empirical analysis, the universal quasichemical functional group activity coefficient (UNIFAC), and conductor-like screening model COSMO-SAC (segment activity coefficient). COSMO-SAC was used to calculate the capacity, selectivity, and performance index of solvents. Finally, the monoethanolamine (MEA) was selected as the solvent to extract the phenols. The liquid–liquid equilibrium for the ternary mixture of phenols + hexane + MEA was measured at 303.15 K and 323.15 K under atmospheric pressure, and the results showed that MEA provided a high distribution coefficient, efficiency, and selectivity for phenols. Meanwhile, the extraction process of phenols was simulated based on the nonrandom two-liquid (NRTL) model, for which binary interaction parameters were obtained through calculations of phase equilibrium. The results showed that the purity of phenols can achieve more than 99.5 wt % using MEA as a solvent.

Thermodynamics of enzyme-catalyzed esterifications: I. Succinic acid esterification with ethanol.

Succinic acid (SA) was esterified with ethanol using Candida antarctica lipase B immobilized on acrylic resin at 40 and 50°C. Enzyme activity in the reaction medium was assured prior to reaction experiments. Reaction-equilibrium experiments were performed for varying initial molalities of SA and water in the reaction mixtures. This allowed calculating the molality-based apparent equilibrium constant K m as function of concentration and temperature. K m was shown to depend strongly on the molality of water and SA as well as on temperature. It could be concluded that increasing the molality of SA shifted the reaction equilibrium towards the products. Water had a strong effect on the activity of the enzyme and on K m . The concentration dependence of K m values was explained by the activity coefficients of the reacting agents. These were predicted with the thermodynamic models Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT), NRTL, and Universal Quasichemical Functional Group Activity Coefficients (UNIFAC), yielding the ratio of activity coefficients of products and reactants K γ . All model parameters were taken from literature. The models yielded K γ values between 25 and 115. Thus, activity coefficients have a huge impact on the consistent determination of the thermodynamic equilibrium constants K th. Combining K m and PC-SAFT-predicted K γ allowed determining K th and the standard Gibbs energy of reaction as function of temperature. This value was shown to be in very good agreement with results obtained from group contribution methods for Gibbs energy of formation. In contrast, inconsistencies were observed for K th using K γ values from the classical gE-models UNIFAC and NRTL. The importance of activity coefficients opens the door for an optimized reaction setup for enzymatic esterifications.

Influence of organic compound functionality on aerosol hygroscopicity: dicarboxylic acids, alkyl-substituents, sugars and amino acids

Abstract. Hygroscopicity data for 36 organic compounds, including amino acids, organic acids, alcohols and sugars, are determined using a comparative kinetics electrodynamic balance (CK-EDB). The CK-EDB applies an electric field to trap-charged aqueous droplets in a chamber with controlled temperature and relative humidity (RH). The dual micro dispenser set-up allows for sequential trapping of probe and sample droplets for accurate determination of droplet water activities from 0.45 to > 0.99. Here, we validate and benchmark the CK-EDB for the homologous series of straight-chain dicarboxylic acids (oxalic–pimelic) with measurements in better agreement with Universal Quasichemical Functional Group Activity Coefficients (UNIFAC) predictions than the original data used to parametrise UNIFAC. Furthermore, a series of increasingly complex organic compounds, with subtle changes to molecular structure and branching, are used to rigorously assess the accuracy of predictions by UNIFAC, which does not explicitly account for molecular structure. We show that the changes in hygroscopicity that result from increased branching and chain length are poorly represented by UNIFAC, with UNIFAC under-predicting hygroscopicity. Similarly, amino acid hygroscopicity is under-predicted by UNIFAC predictions, a consequence of the original data used in the parametrisation of the molecular subgroups. New hygroscopicity data are also reported for a selection of alcohols and sugars and they show variable levels of agreement with predictions.

Hygroscopic behavior of multicomponent organic aerosols and their internal mixtures with ammonium sulfate

Abstract. Water-soluble organic compounds (WSOCs) are important components of organics in the atmospheric fine particulate matter. Although WSOCs play an important role in the hygroscopicity of aerosols, knowledge on the water uptake behavior of internally mixed WSOC aerosols remains limited. Here, the hygroscopic properties of single components such as levoglucosan, oxalic acid, malonic acid, succinic acid, phthalic acid, and multicomponent WSOC aerosols mainly involving oxalic acid are investigated with the hygroscopicity tandem differential mobility analyzer (HTDMA). The coexisting hygroscopic species including levoglucosan, malonic acid, and phthalic acid have a strong influence on the hygroscopic growth and phase behavior of oxalic acid, even suppressing its crystallization completely during the drying process. The phase behaviors of oxalic acid/levoglucosan mixed particles are confirmed by infrared spectra. The discrepancies between measured growth factors and predictions from Extended Aerosol Inorganics Model (E-AIM) with the Universal Quasi-Chemical Functional Group Activity Coefficient (UNIFAC) method and Zdanovskii–Stokes–Robinson (ZSR) approach increase at medium and high relative humidity (RH) assuming oxalic acid in a crystalline solid state. For the internal mixture of oxalic acid with levoglucosan or succinic acid, there is enhanced water uptake at high RH compared to the model predictions based on reasonable oxalic acid phase assumption. Organic mixture has more complex effects on the hygroscopicity of ammonium sulfate than single species. Although hygroscopic species such as levoglucosan account for a small fraction in the multicomponent aerosols, they may still strongly influence the hygroscopic behavior of ammonium sulfate by changing the phase state of oxalic acid which plays the role of "intermediate" species. Considering the abundance of oxalic acid in the atmospheric aerosols, its mixtures with hygroscopic species may significantly promote water uptake under high RH conditions and thus affect the cloud condensation nuclei (CCN) activity, optical properties, and chemical reactivity of atmospheric particles.

Estimation of Equilibrated Vapor Concentrations Using the UNIFAC Model for the Tetrachloroethylene-Chlorobenzene System.

Equilibrated vapor concentrations at 25°C of the tetrachloroethylene-chlorobenzene system were obtained in the presence of air to establish a method for estimating vapor concentrations in work environments where multicomponent organic solvents are used. The experimental data were correlated by introducing activity coefficients calculated by the UNIFAC (Universal Quasichemical Functional Group Activity Coefficient) model. There were four interaction parameters between groups in this solution system, and three had already been determined.However, the fourth parameter--the interaction parameter between ACCl and Cl-(C=C) groups--remains unknown. Therefore, this parameter was determined by a nonlinear least-squares method to obtain the best fit for the experimental data. The calculated values were found to be in good agreement with the experimental values.

The influence of differential evaporation on the structure of a three-component biofuel spray

A high-pressure spray injected under gasoline engine conditions in a constant volume chamber is studied in detail using a three-dimensional computational fluid dynamics approach. Both single-component and ternary mixture (n-alkane and alcohols) spray results are compared with the experimental data from shadowgraphy, Schlieren imaging and phase-Doppler anemometry. Activity coefficient models are used to cover non-ideal thermodynamic effects occurring when mixtures of structurally dissimilar components such as alcohols and n-alkanes are considered. Here, the non-random, two-liquid and universal quasi-chemical functional group activity coefficients methods are applied. A ternary mixture composed of ethanol, n-butanol and n-hexane representing the research octane number (RON 95) of gasoline is considered and investigated in detail. To illustrate the non-ideal mixture effects, first the binary mixtures ethanol/n-butanol, n-butanol/n-hexane and ethanol/n-hexane are studied as subsets of the ternary mixture using a zero-dimensional single-droplet evaporation solver. Introducing a differential evaporation factor for multicomponent mixtures, the mixture segregation is characterized. Additionally, a separation factor indicating which component depletes first (volatility) is presented for both binary and ternary mixtures. The zero-dimensional results are then correlated with the three-dimensional computational fluid dynamics simulation results. Looking at the vapor–liquid equilibria of the different binary mixtures, the non-ideal effects are identified. The evaporation behavior of the binary mixtures is compared with the ternary mixtures in terms of droplet lifetimes, temperature and mixture decomposition, where the latter is characterized with the differential evaporation factor and separation factor. These results are afterwards compared with the three-dimensional computational fluid dynamics simulations, which are validated against the experimental data for single-component sprays regarding the liquid and vapor penetration depths. Finally, in the ternary mixture, n-butanol is replaced with iso-octane, the most widely used gasoline surrogate, to illustrate the differential evaporation behavior for a mixture with a higher alkane content than the ternary mixture used for the experiments. Strong non-ideal effects causing significant changes in the vapor composition are presented, as well as changes in the characterization factors, differential evaporation factor and the separation factor, respectively. These results are compared with the single-droplet simulations.