Prediction Of Phase Behavior Research Articles

Geometric Frustration Directs the Self-assembly of Nanoparticles with Crystallized Ligand Bundles.

Polymer-grafted nanoparticles are versatile building blocks that self-assemble into a diverse range of mesostructures. Coarse-grained molecular simulations have commonly accompanied experiments by resolving structure formation pathways and predicting phase behavior. Past simulations represented nanoparticles as spheres and the ligands as flexible chains of beads, isotropically tethered to the nanoparticles. Here, we investigate a different minimal coarse-grained model. The model consists of an attractive rod tethered to a repulsive sphere. The motivation of this rod-sphere model is to describe nanospheres with a partially crystallized, stretched polymeric bundle as well as other complex building blocks such as rigid surfactants and end-tethered nanorods. Varying the ratio of sphere size to rod radius stabilizes self-limited clusters and other mesostructures with reduced dimensionality. The complex phase behavior we observe is a consequence of geometric frustration.

Accelerated Prediction of Phase Behavior for Block Copolymer Libraries Using a Molecularly Informed Field Theory.

Solution formulations involving polymers are the basis for a wide range of products spanning consumer care, therapeutics, lubricants, adhesives, and coatings. These multicomponent systems typically show rich self-assembly and phase behavior that are sensitive to even small changes in chemistry and composition. Longstanding computational efforts have sought techniques for predictive modeling of formulation structure and thermodynamics without experimental guidance, but the challenges of addressing the long time scales and large length scales of self-assembly while maintaining chemical specificity have thwarted the emergence of general approaches. As a consequence, current formulation design remains largely Edisonian. Here, we present a multiscale modeling approach that accurately predicts, without any experimental input, the complete temperature-concentration phase diagram of model diblock polymers in solution, as established postprediction through small-angle X-ray scattering. The methodology employs a strategy whereby atomistic molecular dynamics simulations is used to parametrize coarse-grained field-theoretic models; simulations of the latter then easily surmount long equilibration time scales and enable rigorous determination of solution structures and phase behavior. This systematic and predictive approach, accelerated by access to well-defined block copolymers, has the potential to expedite in silico screening of novel formulations to significantly reduce trial-and-error experimental design and to guide selection of components and compositions across a vast range of applications.

Effects of the Characterization Methods of Heptane Plus Components (C7+) on the Phase Behavior of Volatile Fluids

For reservoir fluids with strong volatile tendencies, such as gas condensate and volatile oil, the determination of the characteristic parameters of the equation of state has a great influence on the fluid phase behavior and subsequent reservoir engineering, production engineering, and surface facilities design calculations. However, the process of characterizing reservoir fluid lacks a unified and standardized procedure, and the methods are largely empirical and rather complicated. In this paper, three main stages are defined for the characterization of the equation of state parameters for these volatile reservoir fluids, including (1) determining the C7+ distribution, (2) calculating the equation of state parameters for the C7+ single-carbon component, and (3) lump components. The typical calculation methods in each step and their effects on the fluid phase behavior calculations are quantitatively analyzed and compared. Finally, the workflow and the suitable characterization methods for gas condensate and volatile oil phase behavior calculations are selected. The results show that C7+ distribution and C7+ single carbon component properties have a greater influence on the phase behavior prediction of condensate gas and volatile oil, while the lumping of C7+ single carbon component has relatively little influence.

Investigation of interaction binary parameters for solubilities of the natural gas components in ionic liquids with EoS for predicting phase behavior

This paper describes multicomponent solubilities in ionic liquids using non-associative and associative equations of state (EoS). The parameterization routine was developed from liquid density and speed of sound data as an implementation procedure confirmed with the Aspen Plus simulator, which was used to evaluate the EoS’ phase equilibrium predictive performance. The [EMIM][BF4] and [BMIM][NTf2] ionic liquids were employed in this work. PC-SAFT with the 4 C associative scheme showed the best results in fitting the density and speed of sound curves. The deviation for [EMIM][BF4] was 0.12 % and 0.02 %, respectively, while for [BMIM][NTf2] was 0.05 % and 0.57 %, respectively. Regarding vapor-liquid equilibria, the CPA and PC-SAFT models presented the best predictive results, while PC-SAFT (4 C) presented, in general, a better fit for the binaries studied. The binary interaction parameter kij is near zero using PC-SAFT for C4+. So, we recommend using PC-SAFT with kij=0 between ionic liquids and C5+ or heavier hydrocarbons.

Phase behavior of the carbon dioxide/toluene/poly(ethylene glycol) ternary system

The phase behavior of a carbon dioxide (CO2)/toluene (Tol)/poly(ethylene glycol) (PEG) ternary system was investigated in this study to consider the effect of the polymer species on the phase diagram. Measurements were performed using a synthetic method combined with laser displacement and turbidity measurements. Bubble points (vapor–liquid phase separation) were determined from changes in the piston displacement and cloud points (liquid–liquid (LL) phase separation) were determined from changes in the turbidity. The phase boundaries of the CO2 mass fractions ranging from 0.113 to 0.496 were measured by varying the Tol/PEG mass ratio. The homogeneous phase area decreased when the mass ratio of PEG to Tol increased and/or the temperature decreased. These changes in the LL phase-separation behavior were explained by referring to the free volume fraction and solubility parameter estimated using the Sanchez–Lacombe equation of state. The free volume integral fraction could explain the phase diagram, but not the solubility parameter; the effect of polymer species on the Px phase diagram of the ternary system was explained by considering specific interactions between dissimilar components. These results could promote a comprehensive understanding of the phase diagram of CO2/organic solvent/polymer systems and aid the prediction of phase behavior.

Cryogenic Process Optimization for Natural Gas Purification: Predictive Modeling of Methane-CO2 Solid-Vapor Phase Equilibrium Using Response Surface Methodology.

This research combines industrial engineering principles with chemical process modeling to explore the capture of CO2 from natural gas under cryogenic conditions. The study specifically investigates the Solid-Vapor (S-V) phase equilibrium in a methane-carbon dioxide (CH4-CO2) system. The study employs Response Surface Methodology (RSM) to develop a robust model for predicting phase behavior in industrial gas separation processes. The model is validated using experimental data, offering enhanced operational insights into cryogenic CO2 capture in industrial applications. The developed RSM model is particularly valuable as it can predict the mole fractions of methane and CO2 at various temperatures and pressures in the solid-vapor region of phase equilibrium, where limited experimental data make it difficult to estimate these components accurately. The key contribution of this study is to validate the RSM model's available experimental data, and the model can further be used to predict the process conditions at which high methane composition (yCH4) can be achieved. The developed model showed good agreement when the results were compared with previous experimental studies. The utilization of chemical engineering data to forecast previously unknown conditions in gas separation processes broadens the scope of industrial process optimization in this work.

Application of volume‐translated rescaled perturbed‐chain statistical associating fluid theory equation of state to pure compounds using an expansive experimental database

AbstractThe performance of the perturbed‐chain statistical associating fluid theory‐type equations of state (PC‐SAFT‐type EOSs) is compromised in predicting properties of pure compounds in the critical region. In our previous research, we introduced an improved volume‐translated rescaled PC‐SAFT EOS (VTR‐PC‐SAFT EOS) by incorporating a dimensionless distance‐function. Such VTR‐PC‐SAFT EOS is built based on a critical point‐based PC‐SAFT EOS that can reproduce the critical temperature, critical pressure, and critical molar volume of pure compounds. VTR‐PC‐SAFT EOS is found to significantly improve the accuracy of phase behavior predictions in both critical and noncritical regions for pure compounds. In this study, we assess the performance of VTR‐PC‐SAFT EOS in reproducing the critical and noncritical properties of 251 pure compounds, encompassing 20 distinct chemical species. The testing results indicate that, compared to the other three PC‐SAFT‐type EOSs, the VTR‐PC‐SAFT EOS can consistently provide more accurate representations of critical and noncritical properties of 251 pure compounds.

Mapping the application research on machine learning in the field of ionic liquids: A bibliometric analysis

The aim was to gain a deep understanding of the research status and application of machine learning in the field of ionic liquids, and to identify the research hotspots and frontiers. Co-occurrence analysis, co-citation analysis, key literature citation temporal analysis and emerging word analysis were used. The results show that the knowledge base of applying machine learning in the field of ionic liquids can be divided into three parts: fundamental properties and application research, thermodynamics and phase equilibrium research, and the combination of machine learning with computational chemistry methods. The research hotspots mainly include the prediction and optimization of properties, the prediction of phase behavior, and research on machine learning algorithms. The current research frontiers in applying machine learning in the field of ionic liquids include the prediction of ionic liquid performance, the structure-property relationships of ionic liquids, and the optimization and design of ionic liquid processes.

Colloid and nanoparticle-driven phase behavior in weakly perturbed nematic liquid crystals

We study theoretically weakly colloidal particle or nano-particle perturbed nematic liquid crystal (LC) order. We consider cases where the particles are essentially homogeneously dispersed in LC matrix. A mesoscopic Landau-de Gennes approach is used where we focus on the temperature variations in the amplitude order parameter field for different particle volume concentrations ϕ. In the dilute regime, we describe the nematic order in terms of interface and volume amplitude order fields. This modeling suggests that the temperature-driven order-disorder crossover exhibits a two-step transformation. In the case that particles locally support LC order, on decreasing temperature firstly the interface order is established, followed by the build-up of volume order. The latter appears via the 1st order phase transition. On the other hand, the interface order growth is formed either gradually or via the 1st order interfacial transition. In the regime of relatively high values of ϕ the system can be effectively described in terms of a single amplitude order parameter field. In this case, the temperature-driven phase behaviour is qualitatively similar to the interface order temperature response in the diluted regime. We analytically express phase transition temperatures and determine critical points in the temperature-interface interaction phase space. Key novelties of the paper are the determination of conditions for particle-LC interface-driven discontinuous change of nematic ordering and nanoparticle-enabled critical point condition in pressure-driven order-disorder phase transformations. Experimental evidences supporting the predicted phase behavior are presented.

Abstract 1303 Coarse grained models require recalibration to predict phase behavior of highly-charged biomolecular coacervation

Abstract 1303 Coarse grained models require recalibration to predict phase behavior of highly-charged biomolecular coacervation

Machine learning with active pharmaceutical ingredient/polymer interaction mechanism: Prediction for complex phase behaviors of pharmaceuticals and formulations

The high throughput prediction of the thermodynamic phase behavior of active pharmaceutical ingredients (APIs) with pharmaceutically relevant excipients remains a major scientific challenge in the screening of pharmaceutical formulations. In this work, a developed machine learning model efficiently predicts the solubility of APIs in polymers by learning the phase equilibrium principle and using a few molecular descriptors. Under the few-shot learning framework, thermodynamic theory (Perturbed-Chain Statistical Associating Fluid Theory, PC-SAFT) was employed for data augmentation, and computational chemistry was applied for molecular descriptors screening. The results showed that the developed machine learning model can predict the API-polymer phase diagram accurately, broaden the solubility data of APIs in polymers, and reproduce the relationship between API solubility and the interaction mechanisms between API and polymer successfully, which provided efficient guidance for the development of pharmaceutical formulations.

Microfluidic experiments and numerical modeling of pore-scale Asphaltene deposition: Insights and predictive capabilities

Pore-scale asphaltene deposition not only affects reservoir permeability and hydrocarbon production efficiency but also impacts reservoirs performance used in other applications, such as geothermal and CO2 storage. The limited availability of comprehensive experimental data has impeded the development of precise predictive models for deposition behavior. This study employs real-time microfluidic experiments and a Lattice Boltzmann-Immersed Boundary numerical model to investigate pore-scale asphaltene deposition. Experimental results directly observe porosity reduction in diverse pore geometries, offering a detailed explanation of asphaltene deposition phenomena. The LB-IB model accurately replicates complex deposition behavior with over 93% agreement with experimental results across various geometries, using only two adjustable parameters. This enables precise predictions of asphaltene phase behavior in porous media. Sensitivity analyses explore the influence of reservoir geometry, flow rates, and asphaltene concentrations on deposition behavior, underscoring their significant impact on porosity reduction and emphasizing the importance of understanding asphaltene deposition. Additionally, the study compares the proposed model to the Wang-Civan model, showcasing its superior accuracy in capturing deposition mechanisms, and suggests modifications to include an upper limit for deposition when throats are entirely filled with asphaltene. This study improves our understanding of pore-scale asphaltene deposition, aiding in optimizing hydrocarbon extraction practices and industry efficiency.

Modeling and prediction of thermodynamic phase behaviors of oxaprozin and irbesartan in biorelevant media

Modeling and prediction of thermodynamic phase behaviors of oxaprozin and irbesartan in biorelevant media

Prediction of the liquid-crystal phase behavior of hard right triangles from fourth-virial density-functional theories.

We have used an extended scaled-particle theory that incorporates four-body correlations through the fourth-order virial coefficient to analyze the orientational properties of a fluid of hard right isosceles triangles. This fluid has been analyzed by computer simulation studies, with clear indications of strong octatic correlations present in the liquid-crystal phase, although the more symmetric order tetratic phase would seem to be the most plausible candidate. Standard theories based on the second virial coefficient are unable to reproduce this behavior. Our extended theory predicts that octatic correlations, associated to a symmetry under global rotations of the oriented fluid by 45^{∘}, are highly enhanced, but not enough to give rise to a thermodynamically stable phase with strict octatic symmetry. We discuss different scenarios to improve the theoretical understanding of the elusive octatic phase in this intriguing fluid.

Predicting the properties of NiO with density functional theory: Impact of exchange and correlation approximations and validation of the r2SCAN functional

Transition metal oxide materials are of great utility, with a diversity of topical applications ranging from catalysis to electronic devices. Because of their widespread importance in materials science, there is increasing interest in developing computational tools capable of reliable prediction of transition metal oxide phase behavior and properties. The workhorse of materials theory is density functional theory (DFT). Accordingly, we have investigated the impact of various correlation and exchange approximations on their ability to predict the properties of NiO using DFT. We have chosen NiO as a particularly challenging representative of transition metal oxides in general. In so doing, we have provided validation for the use of the r2SCAN density functional for predicting the materials properties of oxides. r2SCAN yields accurate structural properties of NiO and a local spin moment that notably persists under pressure, consistent with experiment. The outcome of our study is a pragmatic scheme for providing electronic structure data to enable the parameterization of interatomic potentials using state-of-the-art artificial intelligence (AI) and machine learning (ML) methodologies. The latter is essential to allow large scale molecular dynamics simulations of bulk and surface materials phase behavior and properties with ab initio accuracy.

Can Machine Learning Predict the Phase Behavior of Surfactants?

We explore the prediction of surfactant phase behavior using state-of-the-art machine learning methods, using a data set for twenty-three nonionic surfactants. Most machine learning classifiers we tested are capable of filling in missing data in a partially complete data set. However, strong data bias and a lack of chemical space information generally lead to poorer results for entire de novo phase diagram prediction. Although some machine learning classifiers perform better than others, these observations are largely robust to the particular choice of algorithm. Finally, we explore how de novo phase diagram prediction can be improved by the inclusion of observations from state points sampled by an analogy to commonly used experimental protocols. Our results indicate what factors should be considered when preparing for machine learning prediction of surfactant phase behavior in future studies.

Improvement of compressible regular solution model using Sanchez-Lacombe equation of state for phase behavior prediction of polymer blends

Improvement of compressible regular solution model using Sanchez-Lacombe equation of state for phase behavior prediction of polymer blends

Critical temperature shift modeling of confined fluids using pore-size-dependent energy parameter of potential function

The behavior and critical properties of fluids confined in nanoscale porous media differ from those of bulk fluids. This is well known as critical shift phenomenon or pore proximity effect among researchers. Fundamentals of critical shift modeling commenced with developing equations of state (EOS) based on the Lennard–Jones (L–J) potential function. Although these methods have provided somewhat passable predictions of pore critical properties, none represented a breakthrough in basic modeling. In this study, a cubic EOS is derived in the presence of adsorption for Kihara fluids, whose attractive term is a function of temperature. Accordingly, the critical temperature shift is modeled, and a new adjustment method is established in which, despite previous works, the bulk critical conditions of fluids are reliably met with a thermodynamic basis and not based on simplistic manipulations. Then, based on the fact that the macroscopic and microscopic theories of corresponding states are related, an innovative idea is developed in which the energy parameter of the potential function varies with regard to changes in pore size, and is not taken as a constant. Based on 94 available data points of critical shift reports, it is observed that despite L–J, the Kihara potential has sufficient flexibility to properly fit the variable energy parameters, and provide valid predictions of phase behavior and critical properties of fluids. Finally, the application of the proposed model is examined by predicting the vapor–liquid equilibrium properties of a ternary system that reduced the error of the L–J model by more than 6%.

Comparison of Biomolecular Condensate Localization and Protein Phase Separation Predictors

Research in the field of biochemistry and cellular biology has entered a new phase due to the discovery of phase separation driving the formation of biomolecular condensates, or membraneless organelles, in cells. The implications of this novel principle of cellular organization are vast and can be applied at multiple scales, spawning exciting research questions in numerous directions. Of fundamental importance are the molecular mechanisms that underly biomolecular condensate formation within cells and whether insights gained into these mechanisms provide a gateway for accurate predictions of protein phase behavior. Within the last six years, a significant number of predictors for protein phase separation and condensate localization have emerged. Herein, we compare a collection of state-of-the-art predictors on different tasks related to protein phase behavior. We show that the tested methods achieve high AUCs in the identification of biomolecular condensate drivers and scaffolds, as well as in the identification of proteins able to phase separate in vitro. However, our benchmark tests reveal that their performance is poorer when used to predict protein segments that are involved in phase separation or to classify amino acid substitutions as phase-separation-promoting or -inhibiting mutations. Our results suggest that the phenomenological approach used by most predictors is insufficient to fully grasp the complexity of the phenomenon within biological contexts and make reliable predictions related to protein phase behavior at the residue level.

Improved predictions of phase behaviour of intrinsically disordered proteins by tuning the interaction range.

The formation and viscoelastic properties of condensates of intrinsically disordered proteins (IDPs) is dictated by amino acid sequence and solution conditions. Because of the involvement of biomolecular condensates in cell physiology and disease, advancing our understanding of the relationship between protein sequence and phase separation (PS) may have important implications in the formulation of new therapeutic hypotheses. Here, we present CALVADOS 2, a coarse-grained model of IDPs that accurately predicts conformational properties and propensities to undergo PS for diverse sequences and solution conditions. In particular, we systematically study the effect of varying the range of the nonionic interactions and use our findings to improve the temperature scale of the model. We further optimize the residue-specific model parameters against experimental data on the conformational properties of 55 proteins, while also leveraging 70 hydrophobicity scales from the literature to avoid overfitting the training data. Extensive testing shows that the model accurately predicts chain compaction and PS propensity for sequences of diverse length and charge patterning, as well as at different temperatures and salt concentrations.