Infinite Dilution Activity Coefficients Research Articles

Optimization of solvent cooling crystallization process for separating phenanthrene from 9-fluorenone by COSMO-RS and solubility calculation approach

Phenanthrene (Phe) and fluorene (Flu) are crucial chemical intermediates with applications in materials, medicine, and pesticides. However, conventional separation methods struggle to effectively isolate phenanthrene from fluorene due to their similar physical properties. To address this challenge, a reaction–separation coupling technique was employed to convert fluorene into 9-fluorenone (9-Fluo), followed by the separation of phenanthrene from 9-fluorenone using solvent-cooling crystallization. Initially, the relative solubilities of phenanthrene and 9-fluorenone in 2072 solvents were calculated using COSMO-RS for preliminary screening. Among various conventional short-chain aliphatic alcohols and low-carbon saturated hydrocarbons, methanol, and cyclohexane were identified as the most effective solvents based on their solubility selectivity (SS). The molecular interactions between phenanthrene or 9-fluorenone and these solvents were analyzed using theoretical methods such as σ-potential/profile, infinite dilution activity coefficients in COSMO-RS, and Hansen solubility parameters, with further confirmation from FTIR tests. The optimal liquid–solid ratios were determined using both phase diagram measurements and solubility calculations. The reaction conversion of fluorene to 9-fluorenone was complete regardless of the raw material used. Phenanthrene and 9-fluorenone were effectively separated using methanol and cyclohexane with optimum liquid–solid mass ratios of 1.6:1 and 2.8:1, respectively. In the model mixture, the contents of phenanthrene and 9-fluorenone exceeded 98 wt%, with yields above 91 wt%. For anthracene residue, the content of 9-fluorenone reached 93.98 wt% with a yield of 84.52 wt%, while its content was only 76.58 wt% due to the accumulation of other components in phenanthrene.

Predicting solvation free energies for neutral molecules in any solvent with openCOSMO-RS

In this study, we introduce openCOSMO-RS 24a, an improved version of the open-source COSMO-RS model parameterized using quantum chemical calculations from ORCA 6.0, leveraging a comprehensive dataset that includes solvation free energies, partition coefficients, and infinite dilution activity coefficients for various solutes and solvents mainly at 25 °C. This is the first version of the model also capable of predicting solvation free energies based on ORCA calculations. Additionally, we develop a Quantitative Structure-Property Relationships model to predict molar volumes of the solvents, an essential requirement for predicting solvation free energies and partition coefficients from structure alone. Our results show that openCOSMO-RS 24a achieves an average absolute deviation of 0.45 kcal mol1 for solvation free energies, 0.76 for the logarithm of the partition coefficients, and 0.51 for the logarithm of infinite dilution activity coefficients, demonstrating improvements over the previous openCOSMO-RS 22 parameterization and comparable results to COSMOtherm 24 BP-TZVP. The user interface was extended to be able use it as solvation model directly from within ORCA 6.0 or from the command line to provide researchers with a robust tool for applications in chemical and materials science.

Exploring pNIPAM lyogels: Experimental study on swelling equilibria in various organic solvents and mixtures, supported by COSMO-RS analysis

Stimuli-responsive lyogels are considered to be smart materials due to their capability of undergoing significant macroscopic changes in response to external triggers. Due to their versatile and unique properties, smart lyogels exhibit great potential in various applications such as drug delivery or actuation processes. While Poly-N-isopropylacrylamide (pNIPAM) is widely known as a thermo-responsive material, it also shows significant solvent-responsive swelling behavior. For the systems tested, polar solvents induce strong swelling due to hydrogen bonding with the amide group, nonpolar solvents lead to significant shrinkage of the lyogels. The aim of this study is to investigate and to model this behavior for future application in chemical or biochemical reactors. As a current area of research, incorporating smart lyogel technology into (bio-)chemical reactors facilitates the development of smart reactor systems. Applying thermodynamic modelling with the gE model COSMO-RS on the monomer or oligomers of pNIPAM, the correlation between solvent-polymer interactions and the degree of swelling can be observed. pNIPAM derivatives exhibit low infinite dilution activity coefficients (IDACs) in polar solvents with large degrees of swelling, while displaying an increase of IDACs in nonpolar solvents. Hydrogen bonds dominate the swelling behavior of lyogels not only in pure solvents but also in mixtures of solvents with varying polarity. Even in mixtures containing high amounts of nonpolar solvents, large degrees of swelling were observed due to the uptake of the polar solvent in the lyogel matrix. This effect can be observed in binary solvent mixtures but also in representative mixtures along an esterification reaction with varying carboxylic chain length of alcohol and carboxylic acids.

Separation of quinoline from quinoline/2-methylnaphthalene using deep eutectic solvents: Experimental and COSMO-RS prediction

The high-temperature coal tar wash oil contains two valuable components, namely, quinoline and 2-methylnaphthalene. These two components are challenging to separate during the deep processing. To address this issue, the COSMO-RS (Conductor-like Screening Mode for Real Solvent) model was utilized to identify them as a potential deep eutectic solvents (DESs) extractant. Based on the calculated infinite dilution activity coefficient, the performance indexes of DESs, including selectivity, solubility, and performance index were determined and utilized to identify high-performance extractants for the quinoline/2-methylnaphthalene system. Finally five DESs were selected based on these indexes, they are DES1 formed by Choline chloride: propionic acid with the molar ratio of 1:1, similarly, DES2 is Choline chloride:oxalic acid of 1:1, DES3 Tetraethylammonium chloride:propionic acid of 1:1, DES4 Tetraethylammonium bromide:itaconic acid of 1:2 and DES5 Tetraethylammonium bromide:propionic acid of 1:2. The liquid–liquid phase equilibrium experiments of the aforementioned five DESs at different temperatures (30 ℃, 40 ℃, and 50 ℃) under atmospheric pressure were conducted. The results showed that five kinds of DESs had good extraction effect on quinoline. The DESs formed by choline chloride and anhydrous oxalic acid had the best extraction effect. The quantum chemical single-molecule optimization of different HBDs and HBAs in DESs, intermolecular interaction energy analysis and independent gradient modeling shows that it is mainly induced by the formation of shared electron pairs by providing empty orbitals from DESs and lone pair of electrons from the quinoline molecule, and the main force is the weak hydrogen bonding force.

Thermodynamic properties of TPAC-EG deep eutectic solvents: A study based on the IGC method with molecular simulations

The study of thermodynamic properties and intermolecular interactions in deep eutectic solvents (DES) is an important innovative step in the development of DES. This research focuses on the synthesis of DES using a 1:2 M ratio of tetrapropylammonium chloride and ethylene glycol. Inverse gas chromatography (IGC) techniques are used to investigate DES of thermodynamics in the temperature range of 303.15–333.15 K. Additionally, molecular dynamics (MD) simulations and density functional theory (DFT) are used to explore intermolecular interactions in DES at both molecular and atomic levels. The research findings yield crucial insights, including the successful determination of infinite dilution activity coefficients, Flory-Huggin’s interaction parameters, solubility parameters, and Abraham solvation parameters of DES through IGC techniques. A careful study of the temperature-dependent trends in these parameters is provided. Analyses of structural properties, including radial distribution functions, hydrogen bond counts, and thermal fluctuation indices, reveal that, at experiments temperatures, the amount and nature of intermolecular hydrogen bonds in binary mixtures are the main factors influencing the thermodynamic properties of DES. Finally, based on DFT calculations, a quantitative assessment of the molecular interaction mechanisms of DES at the atomic level confirms the predominant role of hydrogen bonding in DES formation, with multicenter hydrogen bonds characterized by stronger hydrogen bond strengths emerging as the primary type.

An Interpretable Solute–Solvent Interactive Attention Module Intensified Graph-Learning Architecture toward Enhancing the Prediction Accuracy of an Infinite Dilution Activity Coefficient

An Interpretable Solute–Solvent Interactive Attention Module Intensified Graph-Learning Architecture toward Enhancing the Prediction Accuracy of an Infinite Dilution Activity Coefficient

A Prediction Model of Evaporation Behavior of Nickel-Based Superalloy in Electron Beam Melting

The electron beam melting technique has emerged as a novel refining technology extensively employed for alloy purification due to its advantages of effectively eliminating volatile impurities and non-metallic inclusions. However, a drawback associated with the melting process is the evaporation of alloy elements, which results in an uncontrollable alloy composition. In this paper, Wagner model is used to calculate and study the evaporation behavior. of nickel-based superalloy, and the database of infinite dilution activity coefficient and interaction coefficient of nickelbased superalloy has been established. The results show that the model can well predict the composition of electron beam melting superalloy, establish the relationship between electron beam melting power and temperature, and predict the composition of several groups of DZ125 alloys after melting with different power, which is in consistent with the experimental results. This model is of guiding significance for electron beam melting of nickel-based superalloy.

COSMO-RS-based assessment of thermodynamic tools in predicting the polar and non-polar solvents efficiency in vegetable oil extraction.

One of the prevalent methods for evaluating separation performance is to predict the interactions of solvent and solute molecules. The infinite dilution activity coefficient, Gibbs free energy, and sigma profiles provided insights into the solubilization of a solute and revealed the intensity of the solution's molecular interactions. The effective thermodynamic tools (infinite dilution activity coefficient, Gibbs free energy) were evaluated for predicting the efficiency of 18 polar and non-polar organic solvents in rubber seed oil (RSO) extraction. An infinite dilution activity coefficient was computed to evaluate the solubility of the rubber seed oil model compound (linoleic acid) in the organic solvents. Gibbs free energy was evaluated to show the energy change associated with the molecules mixing process and forecast the miscibility of linoleic acid molecules in the solvents. Moreover, the study examined the sigma profiles and sigma surfaces of organic solvents and linoleic acid to acquire a deeper insight into their similarities and how they interact molecularly. According to the computational prediction and experimental verification, the thermodynamic properties of Gibbs free energy and activity coefficient proved to be highly effective tools for screening polar and moderately polar solvents, predicting the molecular interactions with solute. Whereas the sigma profile and sigma surface were found to be the most efficient tools for evaluating the efficacy of non-polar solvents. Solvents with moderate polarity, such as tetrahydrofuran and diethyl ether, as well as non-polar solvents like pentane, heptane, and n-hexane, proved to be effective and favorable for oil extraction, resulting in the highest oil yields of approximately 27.0%. Overall, the COSMO-RS method demonstrates its utility in estimating the solubility of RSO in organic solvents, enabling early identification of the most effective solvent. The initial geometry optimization of every component was conducted through density functional theory (DFT) using TmoleX software. A single-point density functional theory (DFT) computation using Becke Perdew 86 (BP86) and the Triple-Zeta Valence Potential (TZVPD) was performed to produce.cosmo files. COSMO-RS calculations were performed by applying the parameterization file BP_TZVPD_FINE_19.ctd using COSMOthermX software. The practical extraction of oil from plant seeds was performed using a sonicator bath to verify the accuracy of the COSMO-RS predictions.

Current state of predictive activity models when applied to cyclic bio-compounds and biodiesel related mixtures

The rise in greenhouse gas emissions makes the research and development of renewable energy sources a crucial area, with a particular emphasis on biofuels derived from biomass. In this context, design, simulation, an optimization of such bio-based process will also pose many challenges for thermodynamic models. Classical equations of state usually fail to describe these systems and there is not enough experimental data to correlate binary parameters even for group contribution models like UNIFAC. In this case, COSMO-based models become an interesting alternative, since they do not depend directly on experimental data. Even for the well-know biodiesel production there are intermediaries and byproducts showing phase equilibria that are difficult to predict. The UNIFAC (Do) and COSMO-SAC-HB2 variant were used to predict phase equilibria for mixtures related to bio-compounds. For COSMO-SAC, the first step was the revision and extension of the sigma-LVPP public database for the related molecules. For the modified UNIFAC, literature parameters were considered. Vapor-liquid, infinite dilution activity coefficient, liquid-liquid and excess enthalpy data were collected, with a total of 135 systems were investigated. The COSMO-SAC-HB2 yielded satisfactory deviation results in equilibrium of mixtures involving biocompounds, water, and alcohols. UNIFAC (Do) demonstrated satisfactory results in mixtures involving biocompounds and esters, hydrocarbons and alcohols.

Predicting the microalgae lipid profile obtained by supercritical fluid extraction using a machine learning model.

In this study a Machine Learning model was employed to predict the lipid profile from supercritical fluid extraction (SFE) of microalgae Galdieria sp. USBA-GBX-832 under different temperature (40, 50, 60°C), pressure (150, 250bar), and ethanol flow (0.6, 0.9mLmin-1) conditions. Six machine learning regression models were trained using 33 independent variables: 29 from RD-Kit molecular descriptors, three from the extraction conditions, and the infinite dilution activity coefficient (IDAC). The lipidomic characterization analysis identified 139 features, annotating 89 lipids used as the entries of the model, primarily glycerophospholipids and glycerolipids. It was proposed a methodology for selecting the representative lipids from the lipidomic analysis using an unsupervised learning method, these results were compared with Tanimoto scores and IDAC calculations using COSMO-SAC-HB2 model. The models based on decision trees, particularly XGBoost, outperformed others (RMSE: 0.035, 0.095, 0.065 and coefficient of determination (R2): 0.971, 0.933, 0.946 for train, test and experimental validation, respectively), accurately predicting lipid profiles for unseen conditions. Machine Learning methods provide a cost-effective way to optimize SFE conditions and are applicable to other biological samples.

Molecular mechanism and experimental study of fuel oil extractive desulfurization technology based on ionic liquid

In recent years, the sulfur-containing substances produced by fossil fuel combustion have caused overwhelming pollution to the ecological environment, and fuel desulfurization research is imperative. In this paper, 16 ionic liquids (ILs), which are environmentally friendly, easily degradable and have good prospects for industrial application, were selected by combining four cations and four anions for fuel desulfurization research. The desulfurization mechanism of ILs was first investigated by molecular mechanism studies, in which quantum chemical simulations were used for preliminary screening based on the selectivity coefficient of infinite dilution activity coefficient. And the experimental study of extraction desulfurization was carried out with dibenzothiophene (DBT) and n-heptane as model oils. The above mechanism study and experiments examined the effects of temperature, initial fuel oil concentration and ILs to fuel oil feed ratio on the extraction efficiency, and the results showed that both [C2COOCH3ImC6H13][N(CN)2] and [C2COOCH3ImC6H13][NTF2] ILs have more prominent extraction properties. Among them, [C2COOCH3ImC6H13][NTF2] exhibits high desulfurization efficiency for a wide range of sulfur concentration in fuel oils at room temperature due to the advantages of simple preparation method and easier industrialization of the [C2COOCH3ImC6H13][NTF2]. The above experimental results are fully consistent with the molecular simulation predictions, indicating that the theoretical study and experimental method proposed in this paper are simple and feasible.

Separation potential of 1,5-pentanediol-based deep eutectic solvent: Infinite dilution activity coefficients and excess thermodynamic data

In the present study, the new data of the infinite dilution activity coefficient for 32 different solutes in {1-ethyl-1-methylpyrrolidinium bromide +1,5-pentanediol}, [[EMPYR] Br + 1,5-PDO] DES, were measured using the gas liquid chromatography (GLC) method with pre-saturation of the helium gas. The list of selected solutes included alkanes, alkenes, alkynes, cycloalkanes, cycloalkenes, aromatics, ketones, alcohols, and water. Because the solvents were volatile at the temperatures used for measurements, pre-saturation was deemed necessary. The measurements were taken at temperatures T = (313.15–343.15) K and atmospheric pressure. Values of partial molar properties, i.e., enthalpy, entropy, and Gibbs free energy, were computed at a reference temperature of Tref = 333.15 K. Moreover, the values of capacity and selectivity relating to [[EMPYR] Br + 1,5-PDO] DES for different sets of binary systems that are normally problematic in the separation through solvent extraction or distillation were also computed. These include cyclohexane/benzene; acetone/methanol; and hexane/benzene. The obtained data in the present work was then compared to the literature data, at similar temperatures. Thus, the thermodynamical data is important for pre-selecting solvents for industrial purposes.

COSMO-RS guided screening of ionic liquids for the separation of fluorinated greenhouse gases R-410A: Delving into anion, cation effects, and hydrogen bond dynamics

Reclamation of high-GWP near-azeotropic refrigerant R-410A (50 wt% R-32 (difluoromethane) + 50 wt% R-125 (pentafluoroethane)) can be an effective way to mitigate the greenhouse effect and achieve a circular economy. Efficient ionic liquids (ILs) as extractants needed to be found for the extractive distillation (ED) separation process of R-410A. Given the numerous combinations of cations and anions in ILs, the discovery of an efficient IL via experimental methods proves to be an exceedingly complex task. In this work, the solubilities of R-32, and R-125 in 840 conventional ILs (comprised of 20 cations and 42 anions) were analyzed based on infinite dilution activity coefficient. The absorption mechanisms of R-32 and R-125 in ILs were elucidated by analyzing excess enthalpy (HE), excess Gibbs free energy (GE)) and surface charge density distribution through COSMO-RS (Conductor-like Screening Model for Real Solvents). Results revealed that HE and GE of the mixture formed by R-125 and most ILs surpassed those of R-32, resulting in higher solubility of R-32 in most ILs compared to R-125. Structural changes of anions and cations had a greater effect on the solubility of R-125 in ILs. It is found for the first time that the existence of a strong hydrogen bond donor region in cations/anions generated intense repulsion with the hydrogen atom in R-125. Furthermore, a large area of weak polarity on the surface of cations/anions was difficult to form an effective charge shield with fluorine atoms in R-125, thus inhibiting the dissolution of R-125. Finally based on the identified interaction sites, combined with melting point and viscosity, some novel functional ILs with high selectivity for R-32 + R-125 were designed and determined for actual separation process. These findings significantly enrich the understanding of the solubility mechanism and provide theoretical guidance for designing new ILs for R-410A reclamation.

Screening of ionic liquids and deep eutectic solvents for the extraction of persistent organic pollutants from edible oils and fat

Presence of persistent organic pollutants (POPs) like polychlorinated-p-dibenzodioxins (PCDDs), polychlorinated dibenzofurans (PCDFs) and polychlorinated biphenyls (PCBs) in edible oils poses risk to human health. Ionic liquids (ILs) and deep eutectic solvents (DESs) are novel combinatorial solvents with a proven record of extraction of such persistent organic pollutants (POPs). However, the large possibility of such combinatorial solvents necessitates an a-priori screening strategy to select the best possible ILs and DESs. Therefore, a Conductor like Screening Model Segment Activity Coefficient (COSMO-SAC) based framework has been employed to extract 27 POPs (6 PCDDs, 9 PCDFs, 12 PCBs) from five types of edible oil and animal fat by ILs and DESs. The screening will create such a list and based on that experimentation can be performed. There are five different types of oils and fats such as sunflower oil, soya lecithin, rapeseed oil, fish oil, and milk fat that are selected as potential sources of PCDD/Fs and PCBs. A combination of 58 cations and 39 anions which will yield 2262 types of IL and 217 types of DESs with the proportional combination of 24 HBAs and 62 HBDs are considered. Infinite dilution activity coefficient (IDAC) and solubility are calculated based on COSMO-SAC modelling and a set of ILs and DESs are screened as a potential extractant for POPs. The study aims to select the most suitable green solvents to extract these POPs from edible oils to render them safe for consumption.

Deep learning model based on Bayesian optimization for predicting the infinite dilution activity coefficients of ionic liquid-solute systems

The infinite dilution activity coefficient (γ∞) is a crucial thermodynamic property that provides a measure of the affinity between the solute and the solvent. In this work, three deep learning models (deep neural network, convolution neural network, and convolution deep neural network) were proposed to predict the γ∞ for ionic liquid-solute systems. 52 ionic liquids and 114 organic solutes (7783 data points in total) contained in the γ∞ database over the temperature from 293.15 to 428.15 K were collected to construct the model. Molecular descriptor was generated using the RDKit. The Bayesian optimization algorithm was employed to determine the parameters for each model, and the stability of the model was increased by 10-fold cross validation. Finally, the method of SHapley Additive exPlanations was employed to explain our proposed model. The coefficient of determination, mean absolute error, mean square error, root mean square error, error, sum of squared error, relative root mean square error and the Wilcoxon signed-rank test were used to assess the model. The results indicated that the experimental values were in satisfactory agreement with the values predicted for convolution deep neural network. These predictive models can be used to predict the γ∞ for ionic liquid-solute systems to solve problems in phase equilibrium and process design.

Sustainable extraction of rice bran Oil: Assessing renewable solvents, kinetics, and thermodynamics

Rice bran (RB) oil extraction was performed by applying renewable solvents like ethanol and ethyl acetate. To assess the interaction of these solvents with the oil, the UNIFAC model was chosen to determine the infinite dilution activity coefficient. Soxhlet extraction was utilized to determine the maximum possible yield. The adequacy of the experimental data to mass transfer kinetic and thermodynamic models was investigated with response surface methodology. By the theoretical adjustment, statistical parameters (R²adj of 0.9398 for ethanol and 0.8525 for ethyl acetate) showed that the temperature and solvent/RB ratio have a notable influence on oil extraction. The experimental data agreed with the kinetic model based on solid-liquid mass transfer. The thermodynamic study showed that both solvents have endothermic, spontaneous, and irreversible processes. Interestingly, ethyl acetate had higher extraction yields at the beginning and reached its highest yield in a shorter time interval due to better compatibility with oily compounds present in rice bran and its lower viscosity.

Infinite Dilution Activity Coefficients and Gas–Liquid Partition Coefficients of Organic Compounds in Ionic Liquid 1-Butyl-2,3-dimethylimidazolium Thiocyanate: Measurements and LFER Calculations

Infinite dilution activity coefficients (γi∞) of 31 organic solutes (alkanes, cycloalkanes, olefins, aromatics, chlorinated alkanes, alcohols, and other polar organic solutes) in the ionic liquid (IL) 1-butyl-2,3-dimethylimidazole thiocyanate ([BMMIM][SCN]) were determined by gas–liquid chromatographic (GLC) method in the temperature range of 313.15–353.15 K. The infinite dilution partial molar excess enthalpies (H̅iE, ∞), entropies (TrefS̅iE, ∞), and Gibbs free energies (G̅iE, ∞) of the organic solutes at the reference temperature T = 298.15 K and the solubility parameters (δ3) of [BMMIM][SCN] were derived from γi∞. The gas–liquid partition coefficients (KL) of solutes at different temperatures were calculated by referring to the densities of [BMMIM][SCN]. The linear free energy relationship solvation model was used to fit the KL values, and the KL prediction model was obtained. The selectivity (Sij∞) and capacity (kj∞) of n-hexane (i)/benzene (j) and cyclohexane (i)/benzene (j) at infinite dilution at 323.15 K were calculated. The results were compared with the values of ILs with the thiocyanate anion in the literature.

Vapor-liquid equilibria (VLE) of the binary mixture of normal hexane and water at P = 2.5 MPa and T=(456.85 – 487.85 K)

We report the vapor-liquid equilibria (VLE) of the binary mixture of the n-C6/H2O system at P=2.5 MPa and T=(456.85 – 487.85 K). The result showed an azeotropic behavior with a co-condensation temperature of ∼456.85 K at an n-C6 mole fraction of ∼0.6029. The measured experimental data are modeled with the Cubic Plus Association Equation of State (CPA-EoS) and activity coefficient-based Non-Random Two Liquid (NRTL) model. The infinite dilution activity coefficient of n-C6 and H2O are also estimated over the temperature range of interest. The overall root mean square deviation (RMSD) of the n-C6 mole-fraction in vapor and liquid phases for the CPA EoS and NRTL models are 0.0370 and 0.0765, respectively.

Erratum for “Model Performances Evaluated for Infinite Dilution Activity Coefficients Prediction at 298.15 K”

ADVERTISEMENT RETURN TO ARTICLES ASAPPREVAddition/CorrectionNEXTORIGINAL ARTICLEThis notice is a correctionErratum for “Model Performances Evaluated for Infinite Dilution Activity Coefficients Prediction at 298.15 K”Thomas BrouwerThomas BrouwerMore by Thomas Brouwerhttps://orcid.org/0000-0002-3975-4710 and Boelo SchuurBoelo SchuurMore by Boelo Schuurhttps://orcid.org/0000-0001-5169-4311Cite this: Ind. Eng. Chem. Res. 2023, XXXX, XXX, XXX-XXXPublication Date (Web):April 3, 2023Publication History Received8 March 2023Published online3 April 2023https://doi.org/10.1021/acs.iecr.3c00757© 2023 American Chemical SocietyRIGHTS & PERMISSIONSACS AuthorChoicewith CC-BY-NC-NDlicenseArticle Views-Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (2 MB) Get e-AlertsSupporting Info (1)»Supporting Information Supporting Information Get e-Alerts

New Model to Predict Infinite Dilution Activity Coefficients Based on (∂p/∂x) T,x → 0.

Accurate prediction of infinite dilution activity coefficient (γ∞) is essential for the calculation of phase equilibria, solubility, and related properties in molecular thermodynamics. Here, we propose a new model to accurately predict the value of γ∞. It is applicable to calculate γ∞ for compounds in aqueous solution at different temperatures. The model is based on the relationship of (∂p/∂x) T,x→0 with γ∞ and temperature at low pressure. First, we introduce the new idea of using the group contribution method to estimate (∂p/∂x) T,x→0 and then obtain the activity coefficient of a solute at infinite dilution in water based on the relationship between (∂p/∂x) T,x→0 and γ∞. The accuracy of this model is verified using experimental data from 46 systems and more than 450 data points. The result shows that the total average relative deviation of the predicted values from the experimental values for training data is 4.73%. Besides, we test the applicability of the model using solutes that are not part of the training data set. The result shows that the model is satisfactory for the prediction of testing data. Compared with other models, the results prove that the developed model outperforms the UNIFAC model, the modified UNIFAC model, and previous predictive models for aqueous systems. The final equation with only simple arithmetic is more easily applied in engineering practices.