Fluid Phase Equilibria最新文献_第4页

Solubility, solvent effects, correlation, and thermodynamic properties of glutaric anhydride in ten individual organic solvents from 278.15 to 313.15 K 戊二酸酐在278.15 ~ 313.15 K十种有机溶剂中的溶解度、溶剂效应、相关性和热力学性质

IF 2.7 3区工程技术 Q3 CHEMISTRY, PHYSICAL

Fluid Phase Equilibria

Pub Date : 2026-04-01 Epub Date: 2025-11-28 DOI: 10.1016/j.fluid.2025.114643

Yanyan Hou, Zhentao Zhang, Ziyue Zhang, Yi Yu, Li Xu

The solubility of glutaric anhydride (GA) in ten single organic solvents containing methyl acetate, ethyl acetate, n-propyl acetate, butyl acetate, acetone, acetonitrile, dichloromethane, 1,2-dichloroethane, DMF, DMAC, was determined by static method. In the experimental temperature range (278.15 ∼ 313.15 K), the solubility of the measured GA in each solvent increased with the increase of temperature. Among the solvents selected, GA had the highest solubility in DMAC and the lowest solubility in butyl acetate. The solubility of GA in DMAC was about 2 ∼ 3 times higher than in butyl acetate. Solvent effects in different solvents were explained by the Hansen solubility parameter (HSP) and the physicochemical properties of the solvents (polarity, hydrogen bond donor-acceptor tendency, and cohesion energy density). The analysis of solvation effect showed that the changes of GA solubility were the results of the combined effect of multiple factors, among which the effect of solvent polarity was more significant. Based on density functional theory (DFT), the electrostatic potential energy surface and solute-solvent interaction of GA were analyzed at the molecular level. In addition, the solubility data were fitted using the van't Hoff, modified Apelblat, Yaws, λh and Wilson models. The fitting results showed that the Yaws model had the best correlation with the smallest ARD (3.74 %) and RMSD (0.82 %). The analysis results of the apparent thermodynamic properties indicated that GA dissolution was a spontaneous, endothermic, entropy-driven process.

用静态法测定了戊二酸酐（GA）在乙酸甲酯、乙酸乙酯、乙酸正丙酯、乙酸丁酯、丙酮、乙腈、二氯甲烷、1,2-二氯乙烷、DMF、DMAC等10种单一有机溶剂中的溶解度。在278.15 ~ 313.15 K的实验温度范围内，GA在各溶剂中的溶解度随温度的升高而增大。在所选溶剂中，GA在DMAC中的溶解度最高，在乙酸丁酯中的溶解度最低。GA在DMAC中的溶解度比在乙酸丁酯中的溶解度高2 ~ 3倍。溶剂效应由汉森溶解度参数（HSP）和溶剂的理化性质（极性、氢键供体-受体倾向和内聚能密度）来解释。溶剂化效应分析表明，GA溶解度的变化是多种因素综合作用的结果，其中溶剂极性的影响更为显著。基于密度泛函理论（DFT），从分子水平分析了GA的静电势能面和溶质-溶剂相互作用。此外，利用van't Hoff、修正Apelblat、Yaws、λh和Wilson模型对溶解度数据进行了拟合。拟合结果表明，雅司模型与最小ARD（3.74%）和RMSD（0.82%）的相关性最好。表观热力学性质分析结果表明，GA溶解是一个自发的、吸热的、熵驱动的过程。

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引用次数: 0

Efficient removal of bisphenol A and methyl paraben from water using hydrophobic deep eutectic solvents: Experimental and molecular dynamics insights 使用疏水深共晶溶剂从水中有效去除双酚A和对羟基苯甲酸甲酯：实验和分子动力学见解

IF 2.7 3区工程技术 Q3 CHEMISTRY, PHYSICAL

Fluid Phase Equilibria

Pub Date : 2026-04-01 Epub Date: 2025-11-28 DOI: 10.1016/j.fluid.2025.114644

Neha Sawant , Noor Alomari , Kendall Nore , Niah Thurman , James Springstead , Santiago Aparicio , Mert Atilhan

This study investigates ten hydrophobic deep eutectic solvent (HDES) systems for removing methyl paraben (MP) and bisphenol A (Bis-A) from contaminated water. The HDES were synthesized using various combinations of terpenes, fatty acids, and quaternary ammonium salts, including Cineole-Oleic Acid, Cineole-Decanoic Acid, Carvone-Menthol, Cineole-Linoleic Acid, Geraniol-Linoleic Acid, Geraniol-Oleic Acid, Trihexyl (tetradecyl)phosphonium Chloride-Linoleic Acid, Geraniol-Decanoic Acid, Tetra-n-octyl ammonium Bromide-Linoleic Acid, and Tetrabutylammonium Chloride-Linoleic Acid. Extraction efficiency was determined by analyzing the change in contaminant concentration after treatment with HDES, measured using UV-Vis spectroscopy. Classical molecular dynamics (MD) simulations were also conducted to elucidate the molecular interactions between the HDES and contaminants. Radial and spatial distribution functions, and hydrogen bonding distributions were analyzed to understand the extraction mechanisms.

Experimental and computational studies demonstrated the potential of HDES as an efficient for personal care product treatment in water. P66614Cl-LnA and N8888Br-LnA were the most effective, removing up to 95.44% of Bis-A and 99.30% of MP. Cin-LnA, Cin-OleA, and Ger-DeA also showed high removal efficiencies, exceeding 90% for Bis-A and 92% for MP in most cases. In contrast, CAR-MEN and N4444Cl-LnA showed lower efficiencies. These findings highlight the potential of HDES, particularly P66614Cl-LnA and N8888Br-LnA, for water purification applications.

研究了十种疏水深度共晶溶剂（HDES）体系去除污染水中的对羟基苯甲酸甲酯（MP）和双酚A （Bis-A）。采用萜烯、脂肪酸和季铵盐的不同组合，包括桉树油-油酸、桉树油-癸酸、香芹醇-薄荷醇、桉树油-亚油酸、香叶油-油酸、三己基（十四烷基）氯化磷-亚油酸、香叶油-癸酸、四正辛基溴化铵-亚油酸和四丁基氯化铵-亚油酸，合成了HDES。通过分析HDES处理后污染物浓度的变化来确定萃取效率，并用紫外可见光谱测量。经典分子动力学（MD）模拟也阐明了HDES与污染物之间的分子相互作用。通过分析其径向分布函数和空间分布函数，以及氢键分布，了解其萃取机理。实验和计算研究表明，HDES作为一种有效的个人护理产品在水中的处理潜力。P66614Cl-LnA和N8888Br-LnA对bi - a和MP的去除率分别为95.44%和99.30%。Cin-LnA， Cin-OleA和Ger-DeA也显示出很高的去除效率，在大多数情况下，Bis-A的去除率超过90%，MP的去除率超过92%。相比之下，CAR-MEN和N4444Cl-LnA的效率较低。这些发现突出了HDES，特别是P66614Cl-LnA和N8888Br-LnA在水净化应用中的潜力。

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引用次数: 0

MEAM potential optimization and vapor-liquid equilibrium modeling for Pb-Au and Pb-Sn alloys Pb-Au和Pb-Sn合金MEAM电位优化及气液平衡建模

IF 2.7 3区工程技术 Q3 CHEMISTRY, PHYSICAL

Fluid Phase Equilibria

Pub Date : 2026-04-01 Epub Date: 2025-11-30 DOI: 10.1016/j.fluid.2025.114647

Yizhao Wang , Li Zhu , Hongwei Yang

This study proposes an optimization strategy for the modified embedded atom method (MEAM) potentials of Pb-Au and Pb-Sn alloys and develops an approach for calculating binary alloy vapor-liquid equilibrium (VLE). A sensitivity of the potential parameters to the properties of the metal reveals that the potential parameters A and β⁽⁰⁾ of the unary MEAM have a significant impact on the melting point (T_m). The binary MEAM potential parameters ρ^A:ρ^B, C_min(A-B-B) and C_min(A-B-A) are dominant of mixing enthalpy (ΔH_m) for binary alloy. Based on this, the MEAM potentials of the Pb-Au and Pb-Sn systems are developed. These potentials accurately predict the T_m of pure Pb, Au, and Sn, though slight deviations are observed in the predictions of isobaric heat capacities (C_p). Regarding the Pb-Au and Pb-Sn binary systems, the calculated mixing enthalpies for both systems exhibit the mean absolute percentage errors (MAPE) within 15 %, indicating acceptable agreement with experimental data. To bridge atomic-scale simulation and macroscopic phase equilibrium, this study combines MEAM with the Wilson equation. The component activities in both the Pb-Au and Pb-Sn systems and the VLE under vacuum conditions are predicted based on this method. The VLE diagram reveals that reduced system pressure significantly lowers the temperature required in vacuum distillation. Furthermore, the method quantifies the optimal conditions for achieving high-purity metal extraction by vacuum distillation.

本研究提出了一种优化Pb-Au和Pb-Sn合金的修饰嵌入原子法（MEAM）电位的策略，并开发了一种二元合金气液平衡（VLE）的计算方法。电位参数对金属性质的敏感性表明，一元MEAM的电位参数A和β(0)对熔点（Tm）有显著影响。二元合金的混合焓（ΔHm）以二元MEAM电位参数ρA:ρB、Cmin（A-B-B）和Cmin（A-B-A）为主。在此基础上，推导了Pb-Au和Pb-Sn体系的MEAM电位。这些电位准确地预测了纯Pb、Au和Sn的Tm，尽管在等压热容（Cp）的预测中观察到轻微的偏差。对于Pb-Au和Pb-Sn二元体系，计算得到的混合焓值的平均绝对百分比误差（MAPE）在15%以内，与实验数据符合得很好。为了在原子尺度模拟和宏观相平衡之间架起桥梁，本研究将MEAM与Wilson方程结合起来。在此基础上预测了真空条件下Pb-Au和Pb-Sn体系的组分活度和VLE。VLE图显示，系统压力的降低显著降低了真空蒸馏所需的温度。此外，该方法还量化了真空蒸馏提取高纯度金属的最佳条件。

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引用次数: 0

Excess volumes calculated from UNIQUAC model using the example of methanol - water mixtures 以甲醇-水混合物为例，用UNIQUAC模型计算了过量体积

IF 2.7 3区工程技术 Q3 CHEMISTRY, PHYSICAL

Fluid Phase Equilibria

Pub Date : 2026-04-01 Epub Date: 2025-12-02 DOI: 10.1016/j.fluid.2025.114650

Adina Werner, Jongmin Kim, Fabian Mauss

Excess volumes can be calculated generally via equations of state. In this work, the excess volumes are obtained using the UNIQUAC model with two approaches of a temperature- and pressure-dependent binary interaction parameter. The pressure dependency is required as the excess volume is derived from the pressure dependency of the excess free enthalpy. Both UNIQUAC approaches are successfully able to predict the vapor-liquid equilibrium as well as the excess volume of methanol-water mixtures over a temperature range between 288.15–473 K and a pressure range between 0.1519–134 bar using a single optimized parameter set.

过剩体积一般可以通过状态方程来计算。在这项工作中，使用具有温度和压力相关二元相互作用参数两种方法的UNIQUAC模型获得了多余体积。压力依赖关系是必需的，因为多余体积是由多余自由焓的压力依赖关系推导出来的。这两种UNIQUAC方法都能够成功地预测温度范围为288.15-473 K，压力范围为0.1519-134 bar的汽液平衡以及甲醇-水混合物的过量体积，使用单个优化参数集。

引用次数: 0

Theophylline solubility in water-alcohol mixtures: Novel experimental data and predictive modeling with UNIFAC-Dortmund, ASOG, and PSRK from 283 K to 323 K 茶碱在水-酒精混合物中的溶解度：新的实验数据和预测模型与unfacd - dortmund， ASOG和PSRK从283 K到323 K

IF 2.7 3区工程技术 Q3 CHEMISTRY, PHYSICAL

Fluid Phase Equilibria

Pub Date : 2026-04-01 Epub Date: 2025-11-19 DOI: 10.1016/j.fluid.2025.114633

Alessandro Cazonatto Galvão , Amanda Taruhn Mioto , Raquel Bordignon , Igor Gabriel Kaiser , Pedro Felipe Arce , Weber da Silva Robazza

This study explores the solubility of theophylline in binary liquid mixtures of water with methanol, ethanol, or 2-propanol, across all mole fractions and temperatures from 283.2 K to 323.2 K. The composition of the liquid phase was determined using a gravimetric method. Solubility increases with temperature and shows a weak dependence on the solvent mixture’s dielectric constant, driven more by hydrogen bonding. Solubility trends across all isotherms show two patterns: an initial rise with water content, followed by a decline after a solubility peak. This suggests an endothermic dissolution and a hydration effect at specific water levels. This work provides novel solubility data for theophylline in water-ethanol mixtures, absent in the literature, and generates 32 new interaction parameters for UNIFAC-Dortmund and ASOG models. The UNIFAC-Dortmund and ASOG activity coefficient models were fitted to the experimental data, generating new parameters. The UNIFAC-Dortmund parameters were used to predict theophylline solubility with the PSRK equation of state. Model performance was assessed with average relative deviation (ARD), root mean square deviation (RMSD), and Bayesian Information Criterion (BIC). The ASOG showed the best performance, followed by UNIFAC-Dortmund and PSRK.

本研究探讨了茶碱在水与甲醇、乙醇或2-丙醇的二元液体混合物中的溶解度，所有摩尔分数和温度从283.2 K到323.2 K。用重量法测定了液相的组成。溶解度随温度升高而增加，对溶剂混合物介电常数的依赖性较弱，主要受氢键的驱动。在所有等温线上的溶解度趋势显示出两种模式：最初随着含水量的增加而上升，随后在溶解度峰值后下降。这表明吸热溶解和水合作用在特定的水位。这项工作为茶碱在水-乙醇混合物中的溶解度提供了新的数据，这在文献中是不存在的，并为unifacd - dortmund和ASOG模型生成了32个新的相互作用参数。将UNIFAC-Dortmund和ASOG活度系数模型拟合到实验数据中，得到新的参数。采用UNIFAC-Dortmund参数，用PSRK状态方程预测茶碱溶解度。采用平均相对偏差（ARD）、均方根偏差（RMSD）和贝叶斯信息准则（BIC）评估模型性能。ASOG表现最好，其次是unifa - dortmund和PSRK。

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引用次数: 0

Gas hydrate phase equilibria for aqueous solutions of NaBr, CaBr2, and ZnBr2 at high salinity and pressures up to 2000 bar NaBr， CaBr2和ZnBr2的水溶液在高盐度和高达2000bar的压力下的天然气水合物相平衡

IF 2.7 3区工程技术 Q3 CHEMISTRY, PHYSICAL

Fluid Phase Equilibria

Pub Date : 2026-04-01 Epub Date: 2025-11-27 DOI: 10.1016/j.fluid.2025.114645

Yohan Lee , Sivakumar Subramanian , Douglas Estanga , Amadeu K. Sum

Gas hydrates are an ever-present concern in oil and gas production, as their undesirable formation results in significant operational loss in addition to creating potentially hazardous conditions. Although predictions of gas hydrate phase equilibria to prevent flowline blockage are mostly reliable, the data for hydrate phase equilibria in the literature are mostly limited to mild production conditions in flowlines. However, uncertainties in the predictions are significant when considering more severe (high pressures) and unusual systems (high salinity) with less common salts, such as Zinc salts. This study assesses the reliability and accuracy of hydrate dissociation conditions for brine blends to be used in completion fluids containing ZnBr_2, with measurements of hydrate dissociation and comparison among common prediction tools. Hydrate phase equilibria for four brine blends containing NaBr, CaBr₂, and ZnBr₂ with a synthetic gas mixture were determined at pressures up to 2000 bar and compared with different prediction tools. The predictions with commercial software showed unreliable results for the brine blends, whereas the HLS correlation provided reliable and accurate predictions of the hydrate phase equilibrium boundary for the ZnBr₂ containing brine blends with consideration of water activity in the brine blends. These results establish a more robust basis for predicting hydrate risks in completion fluids under extreme field conditions.

天然气水合物是油气生产中一直存在的问题，因为它们的不良地层除了会造成潜在的危险外，还会导致重大的作业损失。尽管预测天然气水合物相平衡以防止管道堵塞的方法大多是可靠的，但文献中水合物相平衡的数据大多局限于管道中温和的生产条件。然而，当考虑到更严重的（高压）和不寻常的系统（高盐度）与不常见的盐，如锌盐时，预测中的不确定性是显著的。本研究通过水合物解离测量和常用预测工具的比较，评估了用于含ZnBr2完井液的盐水混合物水合物解离条件的可靠性和准确性。在高达2000 bar的压力下，测定了含NaBr、CaBr2和ZnBr2的四种卤水混合物与合成气混合物的水合物相平衡，并与不同的预测工具进行了比较。商业软件的预测结果不可靠，而HLS相关性为含ZnBr2的盐水混合物的水合物相平衡边界提供了可靠和准确的预测，并考虑了盐水混合物中的水活度。这些结果为预测极端现场条件下完井液中的水合物风险奠定了更坚实的基础。

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引用次数: 0

Solubility prediction of BCS class II drugs through combining machine learning and molecular descriptor 结合机器学习和分子描述子的BCSⅱ类药物溶解度预测

IF 2.7 3区工程技术 Q3 CHEMISTRY, PHYSICAL

Fluid Phase Equilibria

Pub Date : 2026-04-01 Epub Date: 2025-12-03 DOI: 10.1016/j.fluid.2025.114651

Yi Zhang , Xiaowei Cheng , Shiyu Sun , Baoshu Liu , Hua Sun

The solubility of active pharmaceutical ingredients is vital throughout the drug design, development processes and manufacture. However, solubility prediction remains a challenging task in the pharmaceutical field. Therefore, BCS class II drugs solubility prediction model was developed on the basis of the machine learning algorithms and molecular descriptors through Bayesian Optimization, cosine similarity and sparse principal component analyses, revealing XGBoost model exhibited the better accuracy and suitability. Besides, the generalization of XGBoost model was confirmed by the solubility data prediction in the uncommon solvents and unseen solutes. Influences of molecular descriptors on the predicted solubility data were evaluated through Shapley Additive Explanations analysis, exposing the temperature exhibited a positive effect on the predicted solubility and the double bonds number of the solvent molecule presented a negative effect on the predicted solubility data. The various molecular descriptor contributions to the solubility prediction of XGBoost model were analyzed through feature importance, exposing the molecular descriptor contributions followed the order: Chi0 > SMR_VSA1 > MolMR > ExactMolWt > T > NumValenceElectrons > fr_C_O. In addition, it revealed the studied molecular descriptors must synergistically contribute to the solubility data prediction of XGBoost model according to prediction results comparison of simple and original XGBoost models.

活性药物成分的溶解度在整个药物设计、开发过程和制造过程中至关重要。然而，在制药领域，溶解度预测仍然是一项具有挑战性的任务。因此，在机器学习算法和分子描述符的基础上，通过贝叶斯优化、余弦相似度和稀疏主成分分析，建立了BCS II类药物溶解度预测模型，结果表明XGBoost模型具有更好的准确性和适用性。此外，XGBoost模型在不常见溶剂和不可见溶质中的溶解度数据预测也证实了模型的泛化性。通过Shapley Additive explanation分析评价了分子描述符对预测溶解度数据的影响，发现温度对预测溶解度有正向影响，溶剂分子双键数对预测溶解度数据有负向影响。通过特征重要性分析了各种分子描述符对XGBoost模型溶解度预测的贡献，发现分子描述符的贡献顺序为：Chi0 >； SMR_VSA1 > MolMR > ExactMolWt > T > NumValenceElectrons > fr_C_O。此外，通过对简单XGBoost模型和原始XGBoost模型预测结果的比较，揭示了所研究的分子描述符对XGBoost模型溶解度数据预测具有协同作用。

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引用次数: 0

Science-guided transfer learning for molecular dynamics of confined fluids in shale nanopores 页岩纳米孔中受限流体分子动力学的科学导向迁移学习

IF 2.7 3区工程技术 Q3 CHEMISTRY, PHYSICAL

Fluid Phase Equilibria

Pub Date : 2026-04-01 Epub Date: 2025-11-28 DOI: 10.1016/j.fluid.2025.114646

Nikhil Muralidhar , Mohamed Mehana , Naren Ramakrishnan , Anuj Karpatne , Nicholas Lubbers

The phase behavior and properties of confined fluids play a critical role in subsurface energy and environmental operations. Predicting these behaviors in porous media typically relies on Molecular Dynamics (MD) simulations, which, while accurate, are prohibitively expensive for large-scale applications. Deep learning (DL) has recently emerged as a promising alternative for developing surrogate models of such processes. However, conventional DL architectures require large volumes of training data—an impractical requirement given the high cost of generating MD datasets. To address this challenge, transfer learning can be employed: models are first trained on related, lower-cost tasks and subsequently adapted to the target task with limited data. This strategy has been highly effective in domains such as natural language processing and computer vision, but its application to confined fluid modeling remains underexplored.

In this work, we present NanoSG, a science-guided deep learning framework for emulating MD simulations of fluid mixtures in confinement. NanoSG integrates domain knowledge with pre-trained representations to enhance learning efficiency and physical consistency. Through extensive experimentation, we show that NanoSG achieves robust generalization, with a minimum performance improvement of 16.26% over baseline models, while maintaining consistency with established scientific principles despite being trained on limited MD data. Our results highlight the potential of science-guided transfer learning to accelerate predictive modeling of confined fluids under data-scarce conditions, opening new avenues for scalable simulation in energy and subsurface applications.

承压流体的相行为和性质在地下能源和环境作业中起着至关重要的作用。在多孔介质中预测这些行为通常依赖于分子动力学（MD）模拟，这种模拟虽然准确，但对于大规模应用来说成本过高。深度学习（DL）最近成为开发这些过程的代理模型的一个有前途的替代方案。然而，传统的深度学习架构需要大量的训练数据，这是一个不切实际的要求，因为生成深度学习数据集的成本很高。为了解决这一挑战，可以采用迁移学习：首先在相关的低成本任务上训练模型，然后在有限的数据下适应目标任务。该策略在自然语言处理和计算机视觉等领域非常有效，但其在受限流体建模中的应用仍有待探索。在这项工作中，我们提出了NanoSG，一个科学指导的深度学习框架，用于模拟禁闭中流体混合物的MD模拟。NanoSG集成了领域知识与预训练的表示，以提高学习效率和物理一致性。通过大量的实验，我们表明NanoSG实现了稳健的泛化，比基线模型的性能提高了16.26%，尽管在有限的MD数据上进行了训练，但仍保持了与既定科学原理的一致性。我们的研究结果突出了科学指导迁移学习在数据稀缺条件下加速受限流体预测建模的潜力，为能源和地下应用的可扩展模拟开辟了新的途径。

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引用次数: 0

Modeling the thermodynamic properties of imidazolium ionic liquids in water, methanol, and ethanol using SAFT-VRE Mie and eSAFT-VR Mie equations of state 利用SAFT-VRE Mie和eSAFT-VR Mie状态方程模拟咪唑类离子液体在水、甲醇和乙醇中的热力学性质

IF 2.7 3区工程技术 Q3 CHEMISTRY, PHYSICAL

Fluid Phase Equilibria

Pub Date : 2026-04-01 Epub Date: 2025-11-19 DOI: 10.1016/j.fluid.2025.114632

Cleiton S. Beraldo , Xiaodong Liang , Georgios M. Kontogeorgis , Luis A. Follegatti-Romero

We evaluate the performance of two electrolyte variants of the Statistical Associating Fluid Theory for Variable Range interactions (SAFT-VR) in the generic Mie form, the SAFT-VRE Mie and eSAFT-VR Mie equations of state, in predicting liquid-phase densities and the speed of sound for imidazolium-based ionic liquids (ILs) and their mixtures with water, methanol, and ethanol. A strictly predictive modeling strategy was employed: only pure-component IL densities were used to derive ion-specific molecular parameters (segment length, size, and energy) for imidazolium-based cations and anions, while solvent parameters were taken from the literature. Ion-solvent and ion-ion pair interactions were calculated via a simplified Hudson-McCoubrey combining rule, assuming equal ionization potentials and avoiding any binary parameter fitting. Six formulations of the relative static permittivity (constant, temperature‐dependent, linear in composition, and a volumetric‐composition model) were evaluated within both the SAFT-VRE Mie and eSAFT-VR Mie frameworks. We introduce a novel Born size interpretation that explicitly accounts for hydrogen‐bonding in IL ions, yielding improved agreement with experimental data. Furthermore, we identify quantitative correlations between these Born size and the underlying SAFT parameters, enabling predictive parametrization of related IL systems. The electrolyte models enhance performance over the SAFT-VR Mie, particularly in mixed‐solvent regions, though further refinement is needed near the pure‐IL limit. All calculations were conducted using the open‐source Clapeyron.jl toolkit, ensuring full reproducibility and extensibility.

我们评估了通用Mie形式的变范围相互作用统计关联流体理论（SAFT-VR）的两种电解质变体，即SAFT-VRE Mie和eSAFT-VR Mie状态方程，在预测咪唑基离子液体（ILs）及其与水、甲醇和乙醇的混合物的液相密度和声速方面的性能。采用了严格的预测建模策略：仅使用纯组分IL密度来推导咪唑基阳离子和阴离子的离子特异性分子参数（片段长度、大小和能量），而溶剂参数则取自文献。离子-溶剂和离子对相互作用通过简化的Hudson-McCoubrey组合规则计算，假设电离势相等，避免任何二元参数拟合。在SAFT-VRE Mie和eSAFT-VR Mie框架中评估了六种相对静态介电常数公式（恒定、温度相关、线性组成和体积组成模型）。我们引入了一种新颖的Born尺寸解释，明确地解释了IL离子中的氢键，产生了与实验数据更好的一致性。此外，我们确定了这些Born大小与潜在SAFT参数之间的定量相关性，从而实现了相关IL系统的预测参数化。电解质模型提高了SAFT-VR Mie的性能，特别是在混合溶剂区域，尽管在纯IL极限附近需要进一步改进。所有的计算都使用开源的Clapeyron进行。Jl工具包，确保完全可再现性和可扩展性。

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引用次数: 0

Corrigendum to “A Review of Experimental Solubilities and a General Correlation between the Temperature-Dependent Solubility and Solute and Solvent Molar Masses for Binary n-Alkane Mixtures” [Fluid Phase Equilibria (2022) 113380] “对二元正构烷烃混合物的实验溶解度的回顾和温度依赖性溶质和溶剂摩尔质量之间的一般相关性”的更正[流体相平衡（2022）113380]

IF 2.7 3区工程技术 Q3 CHEMISTRY, PHYSICAL

Fluid Phase Equilibria

Pub Date : 2026-04-01 Epub Date: 2025-12-30 DOI: 10.1016/j.fluid.2025.114655

Sverre Gullikstad Johnsen

引用次数: 0