196 results found
Muller E, Xu W, fayaz-torshizi M, et al., 2022, On the Use of Boundary Driven Non-Equilibrium Molecular Dynamics for Determining Transport Diffusivities of Multicomponent Mixtures in Nanoporous Materials, The Journal of Physical Chemistry B: Biophysical Chemistry, Biomaterials, Liquids, and Soft Matter, ISSN: 1520-5207
Muller E, Fayaz-Torshizi M, 2021, Coarse-grained molecular simulation of polymers supported by the use of the SAFT-γ mie equation of state, Macromolecular Theory and Simulations, ISSN: 1022-1344
A framework to self-consistently combine a classical equation of state (EoS) and a molecular force field to model polymers and polymer mixtures is presented. The statistical associating fluid theory (SAFT-γ Mie) model is used to correlate the thermophysical properties of oligomers and generate robust and transferrable coarse-grained (CG) molecular parameters which can be used both in particle based molecular simulations and in EoS calculations. Examples are provided for polyethylene, polypropylene, polyisobutyleneatactic polystyrene, 1,4-cis-butadiene, polyisoprene, their blends and mixtures with low molecular weight solvents. Different types ofliquid-liquid phase behaviour are quantitatively captured both by the EoS and by direct molecular dynamics simulations. The use ofCG models following this top-down approach extends the time and length scales accessible to molecular simulation while retainingquantitative accuracy as compared to experimental results.
Muller E, Schran C, Thiemann F, et al., 2021, Machine learning potentials for complex aqueous systems made simple, Proceedings of the National Academy of Sciences of USA, Vol: 38, Pages: 1-8, ISSN: 0027-8424
Simulation techniques based on accurate and efficient representations of potential energy surfaces are urgently needed for the understanding of complex aqueous systems such as solid-liquid interfaces. Here, we present a machine learning framework that enablesthe efficient development and validation of models for complex aqueous systems. Instead of trying to deliver a globally-optimal machinelearning potential, we propose to develop models applicable to specific thermodynamic state points in a simple and user-friendly process. After an initial ab initio simulation, a machine learning potential is constructed with minimum human effort through a data-drivenactive learning protocol. Such models can afterwards be applied inexhaustive simulations to provide reliable answers for the scientificquestion at hand, or systematically explore the thermal performanceof ab initio methods. We showcase this methodology on a diverseset of aqueous systems comprising bulk water with different ionsin solution, water on a titanium dioxide surface, as well as waterconfined in nanotubes and between molybdenum disulfide sheets.Highlighting the accuracy of our approach with respect to the underlying ab initio reference, the resulting models are evaluated in detailwith an automated validation protocol that includes structural anddynamical properties and the precision of the force prediction of themodels. Finally, we demonstrate the capabilities of our approach forthe description of water on the rutile titanium dioxide (110) surface toanalyze the structure and mobility of water on this surface. Such machine learning models provide a straightforward and uncomplicatedbut accurate extension of simulation time and length scales for complex systems.
Graphene’s intrinsically corrugated and wrinkled topology fundamentally influences its electronic, mechanical, and chemical properties. Experimental techniques allow the manipulation of pristine graphene and the controlled production of defects which allows one to control the atomic out-of-plane fluctuations and thus tune graphene’s properties. Here, we perform large scale machine learning-driven molecular dynamics simulations to understand the impact of defects on the structure of graphene. We find that defects cause significantly higher corrugation leading to a strongly wrinkled surface. The magnitude of this structural transformation strongly depends on the defect concentration and specific type of defect. Analyzing the atomic neighborhood of the defects reveals that the extent of these morphological changes depends on the preferred geometrical orientation and the interactions between defects. While our work highlights that defects can strongly affect graphene’s morphology, it also emphasizes the differences between distinct types by linking the global structure to the local environment of the defects.
Mejía A, Cartes M, Chaparro G, et al., 2021, Phase equilibria and interfacial properties of selected methane + n-alkane binary mixtures, Journal of Molecular Liquids, Vol: 341, Pages: 1-13, ISSN: 0167-7322
Experimental determination, theoretical modeling, and molecular simulation have been combined to describe the bulk phase equilibria (i.e., pressure, liquid, and vapor saturated mass densities) and interfacial properties (i.e., interfacial concentration, adsorption, and interfacial tension) for methane + n-decane, n-dodecane, n-tetradecane and n-hexadecane binary mixtures at 344.15 K and in a pressure range between 0.1 and 30 MPa. Experimental determinations are carried out using a combined apparatus that includes a high-pressure vibrating tube densimeter and a high-pressure pendant drop tensiometer. The theoretical approach is based on the van der Waals gradient theory coupled to the Statistical Associating Fluid Theory of Variable Range employing a Mie potential (SAFT-VR-Mie) equation of state, where the fluids are described as Coarse Grained (CG) atoms. Molecular dynamics simulation for the same systems based on the CG Mie potential are reported. The three approaches are able to indepen independently predict phase equilibrium and interfacial properties, showing a very good agreement amongst themselves. For the systems and conditions studied here, the vapor mass density increases; the liquid mass density and interfacial tensions decrease as the pressure increases, and with a fixed temperature and pressure, the liquid mass density and interfacial tensions increase as the n-alkane molecular chain length increases. It is observed that methane is adsorbed along the interfacial region, whereas the n-alkanes (n-decane, n-dodecane, n-tetradecane, n-hexadecane) do not exhibit surface activity. The relative Gibbs adsorption of methane increases significantly with pressure until it reaches a maximum denoting the adsorption saturation limit. It is also observed that the adsorption of methane only slightly increases with the chain length of the n-alkane.
Muller E, Fayaz Torshizi M, 2021, Coarse-grained molecular dynamics study of the self- assembly of polyphilic bolaamphiphiles using the SAFT- γ mie force field, Molecular Systems Design & Engineering, Vol: 6, Pages: 594-608, ISSN: 2058-9689
A methodology is outlined to parametrize coarse grained molecular models for the molecular dynamics simulation of liquid crystalline bolaamphiphiles (BAs). We employ a top down approach based on the use of the Statistical Associating Fluid Theory (SAFT) that provides a robust and transferable set of building blocks from the fitting of thermophysical properties of smaller molecules. The model is employed to characterise symmetric and asymmetric swallow-tailed BAs and to compare them with an isomeric T-shaped BA. Branching of the side chain of the BAs, leading to the swallow-tailed geometry generates a richness in the number and morphology of liquid crystal mesophases. The simulations elucidate some of the intriguing results observed in experiments.
Mejia A, Muller E, Chaparro Maldonado G, 2021, SGTPy: A Python code for calculating the interfacial properties of fluids based on the Square Gradient Theory using the SAFT-VR Mie equation of state, Journal of Chemical Information and Modeling, Vol: 61, Pages: 1244-1250, ISSN: 1549-9596
In this work, we showcase SGTPy, a Python open-source code developed to calculate interfacial properties (interfacial concentration profiles and interfacial or surface tension) for pure fluids and fluid mixtures. SGTPy employs the Square Gradient Theory (SGT) coupled to the Statistical Associating Fluid Theory of Variable Range employing a Mie potential (SAFT-VR-Mie). SGTPy uses standard Python numerical packages (i.e., NumPy, SciPy) and can be used under Jupyter notebooks. Its features are the calculation of phase stability, phase equilibria, interfacial properties, and the optimization of the SGT and SAFT parameters for vapor–liquid, liquid–liquid and vapor–liquid–liquid equilibria for pure fluids and multicomponent mixtures. Phase equilibrium calculations include two-phase and multiphase flash, bubble and dew points, and the tangent plane distance. For the computation of interfacial properties, SGTPy incorporates several options to solve the interfacial concentration, such as the path technique, an auxiliary time function, and orthogonal collocation. Additionally, the SGTPy code allows the inclusion of subroutines from other languages (e.g., Fortran, and C++) through Cython and f2py Python tools, which opens the possibility for future extensions or recycling tested and optimized subroutines from other codes. Supporting Information includes a review of the theoretical expressions required to couple SAFT-VR-Mie equation of state with the SGT. The use and capabilities of SGTPy are illustrated through step by step examples written on Jupyter notebooks for the cases of pure fluids and binary and ternary mixtures in bi- and three- phasic equilibria. The SGTPy code can be downloaded from https://github.com/gustavochm/SGTPy.
, 2021, Chemical Engineering Research, Publisher: Department of Chemical Engineering, ISBN: 978-1-9160050-2-0
Reports of the 4th year research projects in the Department of Chemical Engineering at Imperial College London.
Antonio Estévez L, Colpas FJ, Müller EA, 2020, A simple thermodynamic model for the solubility of thermolabile solids in supercritical fluids, Chemical Engineering Science, Vol: 232, Pages: 1-10, ISSN: 0009-2509
An equation-of-state (EoS) scheme to correlate the solubility of solids in supercritical fluids is presented. The solute fugacity coefficient is obtained using the pure-solvent compressibility factor, and empirical solute-to-solvent parameter ratios of cohesion factors and covolumes. The proposed method is simpler than EoS conventional calculations since no iteration is required. We retain the link to classical cubic EoS and mixing rules and showcase the application employing both the Redlich-Kwong and Peng-Robinson EoS. The method uses two adjustable parameters, which are computed from experimental data for several binary systems and used to predict solubilities. The results have been favorably compared to those computed by other methods. The advantage of EoS-based models over empirical ones has been emphasized for cases where the solubilities are extrapolated beyond the range of experimental data. The proposal is advantageous for correlating solubility of thermolabile solids in supercritical fluids since no critical properties of the solute are required.
Thiemann F, Rowe P, Muller E, et al., 2020, A machine learning potential for hexagonal boron nitride applied to thermally and mechanically induced rippling, The Journal of Physical Chemistry C: Energy Conversion and Storage, Optical and Electronic Devices, Interfaces, Nanomaterials, and Hard Matter, Vol: 124, Pages: 22278-22290, ISSN: 1932-7447
We introduce an interatomic potential for hexagonal boron nitride (hBN) based on the Gaussian approximation potential (GAP) machine learning methodology. The potential is based on a training set of configurations collected from density functional theory (DFT) simulations and is capable of treating bulk and multilayer hBN as well as nanotubes of arbitrary chirality. The developed force field faithfully reproduces the potential energy surface predicted by DFT while improving the efficiency by several orders of magnitude. We test our potential by comparing formation energies, geometrical properties, phonon dispersion spectra, and mechanical properties with respect to benchmark DFT calculations and experiments. In addition, we use our model and a recently developed graphene-GAP to analyze and compare thermally and mechanically induced rippling in large scale two-dimensional (2D) hBN and graphene. Both materials show almost identical scaling behavior with an exponent of η ≈ 0.85 for the height fluctuations agreeing well with the theory of flexible membranes. On the basis of its lower resistance to bending, however, hBN experiences slightly larger out-of-plane deviations both at zero and finite applied external strain. Upon compression, a phase transition from incoherent ripple motion to soliton-ripples is observed for both materials. Our potential is freely available online at [http://www.libatoms.org].
Zhu K, Muller E, 2020, Generating a machine-learned equation of state for fluid properties, The Journal of Physical Chemistry B: Biophysical Chemistry, Biomaterials, Liquids, and Soft Matter, Vol: 124, Pages: 8628-8639, ISSN: 1520-5207
Equations of state (EoS) for fluids have been a staple of engineering design and practice for over a century. Available EoS are based on the fitting of a closed-form analytical expression to suitable experimental data. The mathematical structure and the underlying physical model significantly restrain the applicability and accuracy of the resulting EoS. This contribution explores the issues surrounding the substitution of machine-learned models for analytical EoS. In particular, we describe, as a proof of concept, the effectiveness of a machine-learned model to replicate the statistical associating fluid theory (SAFT-VR Mie) EoS for pure fluids. To quantify the effectiveness of machine-learning techniques, a large set of pseudodata is obtained from the EoS and used to train the machine-learning models. We employ artificial neural networks and Gaussian process regression to correlate and predict thermodynamic properties such as critical pressure and temperature, vapor pressures, and densities of pure model fluids; these are performed on the basis of molecular descriptors. The comparisons between the machine-learned EoS and the surrogate data set suggest that the proposed approach shows promise as a viable technique for the correlation and prediction of thermophysical properties of fluids.
Cardenas H, Muller E, 2020, How does the shape and surface energy of pores affect the adsorption of nanoconfined fluids?, AIChE Journal, Vol: 67, Pages: 1-11, ISSN: 0001-1541
We report a systematic molecular simulation study of the behavior of Lennard‐Jones fluids inside nanopores of diverse shapes, focusing on the effect that the pore geometry and the local energetic environment have on the adsorption isotherms. Infinitely long pores with polygon (triangle, square, pentagon, hexagon, octagon, decagon and circle) cross sections are considered. Three different pore sizes commensurate with the molecular diameters along with three different values of fluid‐solid energy interactions are chosen to perform Grand Canonical Monte Carlo simulations at a subcritical temperature. Overall, the effect of nanoconfinement on the adsorption of fluids is seen to be a delicate balance between the geometric packing restrictions imposed by the hard cores of the molecules and the surfaces, the excess adsorption induced by the presence (or absence) of energetically favored “hot spots” and the overall ratio of surface/bulk fluid volume present in the pore.
Bhatia N, Müller EA, Matar O, 2020, A GPU accelerated Lennard-Jones system for immersive molecular dynamics simulations in virtual reality, International Conference on Human-Computer Interaction, Publisher: Springer International Publishing, Pages: 19-34, ISSN: 0302-9743
Interactive tools and immersive technologies make teaching more engaging and complex concepts easier to comprehend are designed to benefit training and education. Molecular Dynamics (MD) simulations numerically solve Newton’s equations of motion for a given set of particles (atoms or molecules). Improvements in computational power and advances in virtual reality (VR) technologies and immersive platforms may in principle allow the visualization of the dynamics of molecular systems allowing the observer to experience first-hand elusive physical concepts such as vapour-liquid transitions, nucleation, solidification, diffusion, etc. Typical MD implementations involve a relatively large number of particles N = O( 104 ) and the force models imply a pairwise calculation which scales, in case of a Lennard-Jones system, to the order of O( N2 ) leading to a very large number of integration steps. Hence, modelling such a computational system over CPU along with a GPU intensive virtual reality rendering often limits the system size and also leads to a lower graphical refresh rate. In the model presented in this paper, we have leveraged GPU for both data-parallel MD computation and VR rendering thereby building a robust, fast, accurate and immersive simulation medium. We have generated state-points with respect to the data of real substances such as CO 2 . In this system the phases of matter viz. solid liquid and gas, and their emergent phase transition can be interactively experienced using an intuitive control panel.
Bernet T, Mueller EA, Jackson G, 2020, A tensorial fundamental measure density functional theory for the description of adsorption in substrates of arbitrary three-dimensional geometry, JOURNAL OF CHEMICAL PHYSICS, Vol: 152, ISSN: 0021-9606
Zheng L, Rucker M, Bultreys T, et al., 2020, Surrogate models for studying the wettability of nanoscale natural rough surfaces using molecular dynamics, Energies, Vol: 13, ISSN: 1996-1073
A molecular modeling methodology is presented to analyze the wetting behavior of natural surfaces exhibiting roughness at the nanoscale. Using atomic force microscopy, the surface topology of a Ketton carbonate is measured with a nanometer resolution, and a mapped model is constructed with the aid of coarse-grained beads. A surrogate model is presented in which surfaces are represented by two-dimensional sinusoidal functions defined by both an amplitude and a wavelength. The wetting of the reconstructed surface by a fluid, obtained through equilibrium molecular dynamics simulations, is compared to that observed by the different realizations of the surrogate model. A least-squares fitting method is implemented to identify the apparent static contact angle, and the droplet curvature, relative to the effective plane of the solid surface. The apparent contact angle and curvature of the droplet are then used as wetting metrics. The nanoscale contact angle is seen to vary significantly with the surface roughness. In the particular case studied, a variation of over 65° is observed between the contact angle on a flat surface and on a highly spiked (Cassie–Baxter) limit. This work proposes a strategy for systematically studying the influence of nanoscale topography and, eventually, chemical heterogeneity on the wettability of surfaces.
alonso G, chaparro G, cartes M, et al., 2020, Probing the interfacial behavior of Type IIIa binary mixtures along the three-phase line employing molecular thermodynamics, Molecules, Vol: 25, ISSN: 1420-3049
Interfacial properties such as interfacial profiles, surface activity, wetting transitions, and interfacial tensions along the three-phase line are described for a Type IIIa binary mixture. The methodological approach combines the square gradient theory coupled to the statistical associating fluid theory for Mie potentials of variable range, and coarse-grained molecular dynamics simulations using the same underlying potential. The water + n-hexane mixture at three-phase equilibrium is chosen as a benchmark test case. The results show that the use of the same molecular representation for both the theory and the simulations provides a complementary picture of the aforementioned mixture, with an excellent agreement between the molecular models and the available experimental data. Interfacial tension calculations are extended to temperatures where experimental data are not available. From these extrapolations, it is possible to infer a first order wetting transition at 347.2 K, where hexane starts to completely wet the water/vapor interface. Similarly, the upper critical end point is estimated at 486.3 K. Both results show a very good agreement to the available experimental information. The concentration profiles confirm the wetting behavior of n-hexane along with a strong positive surface activity that increases with temperature, contrasting the weak positive surface activity of water that decreases with temperature.
Muller E, Trusler J, Bresme F, et al., 2020, Employing SAFT coarse grained force fields for the molecular simulation of thermophysical and transport properties of CO2 – n-alkane mixtures, Journal of Chemical and Engineering Data, Vol: 65, Pages: 1159-1171, ISSN: 0021-9568
We report an assessment of the predictive and correlative capability of the SAFT coarse-grained force field as applied to mixtures of CO2 with n-decane and n-hexadecane. We obtain the pure and cross-interaction parameters by matching simulations to experimental phase equilibrium behavior and transfer these parameters to predict shear viscosities. We apply both equilibrium (based on the Green–Kubo formulation) and nonequilibrium (based on the application of an external force to generate an explicit velocity field) algorithms. Single- and two-site models are explored for CO2, and while for volumetric properties both models provide good results, only the model that aligns with the molecular shape is found to be robust when describing highly asymmetric binary mixtures over wide ranges of temperature and pressure. While the models provide good quantitative predictions of viscosity, deviations among the algorithms and with experimental data are encountered for binary mixtures involving longer chain fluids, and in particular at high-pressure and low-temperature states.
Aasen A, Hammer M, Müller EA, et al., 2020, Equation of state and force fields for Feynman-Hibbs-corrected Mie fluids. II. Application to mixtures of helium, neon, hydrogen, and deuterium., Journal of Chemical Physics, Vol: 152, Pages: 074507-1-074507-18, ISSN: 0021-9606
We extend the statistical associating fluid theory of quantum corrected Mie potentials (SAFT-VRQ Mie), previously developed for pure fluids [Aasen et al., J. Chem. Phys. 151, 064508 (2019)], to fluid mixtures. In this model, particles interact via Mie potentials with Feynman-Hibbs quantum corrections of first order (Mie-FH1) or second order (Mie-FH2). This is done using a third-order Barker-Henderson expansion of the Helmholtz energy from a non-additive hard-sphere reference system. We survey existing experimental measurements and ab initio calculations of thermodynamic properties of mixtures of neon, helium, deuterium, and hydrogen and use them to optimize the Mie-FH1 and Mie-FH2 force fields for binary interactions. Simulations employing the optimized force fields are shown to follow the experimental results closely over the entire phase envelopes. SAFT-VRQ Mie reproduces results from simulations employing these force fields, with the exception of near-critical states for mixtures containing helium. This breakdown is explained in terms of the extremely low dispersive energy of helium and the challenges inherent in current implementations of the Barker-Henderson expansion for mixtures. The interaction parameters of two cubic equations of state (Soave-Redlich-Kwong and Peng-Robinson) are also fitted to experiments and used as performance benchmarks. There are large gaps in the ranges and properties that have been experimentally measured for these systems, making the force fields presented especially useful.
, 2020, Chemical Engineering Research: Reports of the 4th year research projects in the Department of Chemical Engineering at Imperial College London. Volume 2, London, UK, Publisher: Department of Chemical Engineering, Imperial College London, ISBN: 9781916005013
This volume of Chemical Engineering Research collects the unedited research project reportswritten by 4th year undergraduates (Class of 2020) of the M.Eng. course on ChemicalEngineering in the Department of Chemical Engineering at Imperial College London. Theresearch project spans for one term (Autumn) during the last year of the career and has anemphasis on independence, ability to plan and pursue original project work for an extendedperiod, to produce a high quality report, and to present the work to an audience usingappropriate visual aids. Students are also expected to produce a literature survey and to placetheir work in the context of prior art. The papers presented showcase the diversity and depth ofsome of the research streams in the department, but obviously only touch on a small numberof research groups and interests. For a full description of the research at the department, thereader is referred to the departmental website https://www.imperial.ac.uk/chemical-engineering.
Theodorakis PE, Smith ER, Muller EA, 2019, Spreading of aqueous droplets with common and superspreading surfactants. A molecular dynamics study, COLLOIDS AND SURFACES A-PHYSICOCHEMICAL AND ENGINEERING ASPECTS, Vol: 581, ISSN: 0927-7757
Muller E, Cárdenas H, 2019, Extension of the SAFT-VR-Mie equation of state for adsorption, Journal of Molecular Liquids, Vol: 294, Pages: 1-12, ISSN: 0167-7322
An empirical extension of the Statistical Associating Fluid Theory (SAFT-VR-Mie) is presented to take intoaccount the effect of confinement of fluids within cylindrical nanopores. The modification of the equationof state retains the bulk phase limit presented in the original formulation and adds a term corresponding tothe contribution to the Helmholtz energy of the confined fluid. The resulting expression employs the fluidfluid parameters obtained from fitting bulk fluid behaviour and adds two additional adjustable parametersreflecting the strength of the solid-fluid energy and the range of the surface attraction. The capability ofthe theoretical model is showcased by fitting adsorption isotherms of methane and n−nonane on activatedcarbons; ethane, n−hexane and benzene on MCM-41, and methane and carbon dioxide on carbon surrogatemodels of shale rocks; providing for an accurate correlation of the data with parameters that are temperatureindependent and robust. The physical nature of the underlying model allows it to be mapped to fluid-solidmolecular models which can then be resolved employing classical molecular simulation methods, providing foran avenue into probing not only the adsorption behaviour but also the transport and interfacial properties.
Theodorakis PE, Smith ER, Craster RV, et al., 2019, Molecular dynamics simulation of the super spreading of surfactant-laden droplets. A review, Fluids, Vol: 4, Pages: 1-23, ISSN: 2311-5521
Superspreading is the rapid and complete spreading of surfactant-laden droplets on hydrophobic substrates. This phenomenon has been studied for many decades by experiment, theory, and simulation, but it has been only recently that molecular-level simulation has provided significant insights into the underlying mechanisms of superspreading thanks to the development of accurate force-fields and the increase of computational capabilities. Here, we review the main advances in this area that have surfaced from Molecular Dynamics simulation of all-atom and coarse-grained models highlighting and contrasting the main results and discussing various elements of the proposed mechanisms for superspreading. We anticipate that this review will stimulate further research on the interpretation of experimental results and the design of surfactants for applications requiring efficient spreading, such as coating technology.
Jimenez-Serratos G, Cardenas H, Muller E, 2019, Extension of the effective solid-fluid Steele potential for Mie force fields, Molecular Physics: An International Journal at the Interface Between Chemistry and Physics, Vol: 117, Pages: 3840-3851, ISSN: 0026-8976
Molecular simulation of fluid systems in the presence of surfaces require computationally expen-sive calculations due to the large number of solid–fluid pair interactions involved. Representingthe explicit solid as a continuous wall with an effective potential can significantly reduce thecomputational time and allows exploring larger temporal and spatial scales. Different ana-lytical expressions can be found in the literature depending on the structural characteristicsof the solid and the approximations adopted in the derivation. The well-known (10-4-3) Steelepotential is one such analytic expression that faithfully represents the effective solid–fluid inter-actions for homonuclear crystalline solids with hexagonal lattice symmetry. However, this andmost of the effective potentials found in the literature have been developed for fluids and solidsinteracting exclusively through Lennard-Jones potentials. In this work, we extend the Steelemodel to obtain the effective wall–fluid potentials for Mie force fields. We perform moleculardynamics simulations of coarse-grained fluids modelled via the SAFT force field approach inthe presence of explicit and implicit surfaces to compare structural and dynamic properties inboth representations. Also, we study the adsorption of ethane into slit-like pores with explicitand implicit surfaces via grand canonical Monte Carlo simulations. We explore the validityand the improvement in the simulation performance as well as the limitations of the proposedexpression.
Muller E, Law J, Headen T, et al., 2019, A catalogue of plausible molecular models for the molecular dynamics of asphaltenes and resins obtained from quantitative molecular representation, Energy and Fuels, Vol: 33, Pages: 9779-9795, ISSN: 0887-0624
Computer simulation studies aimed at elucidating the phase behavior of crude oils inevitably require atomistically-detailed models of representative molecules. For the lighter fractions of crudes, such molecules are readily available, as the chemical composition can be resolved experimentally. Heavier fractions pose a challenge, on one hand due to their polydispersity and on the other due to poor description of the morphology of the molecules involved. The Quantitative Molecular Representation (QMR) approach is used here to generate a catalogue of 100 plausible asphaltene and resin structures based on elemental analysis and 1H – 13C NMR spectroscopy experimental data. The computer-generated models are compared in the context of a review of previously proposed literature structures and categorized by employing their molecular weights, double bond equivalents (DBE) and hydrogen to carbon (H/C) ratios. Sample atomistic molecular dynamics simulations were carried out for two of the proposed asphaltene structures with contrasting morphologies, one island-type and one archipelago-type, at 7 wt% in either toluene or heptane. Both asphaltene models, which shared many characteristics in terms of average molecular weight, chemical composition and solubility parameters showed marked differences in their aggregation behavior. The example showcases the importance of considering diversity and polydispersity when considering molecular models of heavy fractions.
Zheng L, Trusler JPM, Bresme F, et al., 2019, Predicting the pressure dependence of the viscosity of 2,2,4-trimethylhexane using the SAFT coarse-grained force field, Fluid Phase Equilibria, Vol: 496, Pages: 1-6, ISSN: 0378-3812
This work is framed within AIChE's 10th Industrial Fluid Properties Simulation Challenge, with the aim of assessing the capability of molecular simulation methods and force fields to accurately predict the pressure dependence of the shear viscosity of 2,2,4-trimethylhexane at 293.15 K (20 °C) at pressures up to 1 GPa. In our entry for the challenge, we employ coarse-grained intermolecular models parametrized via a top-down technique where an accurate equation of state is used to link the experimentally-observed macroscopic volumetric properties of fluids to the force-field parameters. The state-of-the-art version of the statistical associating fluid theory (SAFT) for potentials of variable range as reformulated in the Mie incarnation is employed here. The potentials are used as predicted by the theory, with no fitting to viscosity data. Viscosities are calculated by molecular dynamics (MD) employing two independent methods; an equilibrium-based procedure based on the analysis of the pressure fluctuations through a Green-Kubo formulation and a non-equilibrium method where periodic perturbations of the boundary conditions are employed to simulate experimental shear stress conditions. There is an indication that, at higher pressures, the model predicts a solid phase (freezing) which we believe to be an artefact of the simplified molecular geometry used in the modelling. A comparison (made after disclosure of the experimental data) show that the model consistently underpredicts the viscosity by about 30%, but follows the pressure dependency accurately.
Kaimaki D-M, Haire B, Ryan H, et al., 2019, Multiscale approach linking self-aggregation and surface interactions of synthesized foulants to fouling mitigation strategies, Energy & Fuels, Vol: 33, Pages: 7216-7224, ISSN: 0887-0624
Fouling of oil-exposed surfaces remains a crucial issue as a result of the continued importance of oil as the world’s primary energy source. The key perpetrators in crude oil fouling have been identified as asphaltenes, a poorly described mixture of diverse polyfunctional molecules that form part of the heaviest fractions of oil. Asphaltenes are responsible for a decrease in oil production and energy efficiency and an increase in the risk of environmental hazards. Hence, understanding and managing systems that are prone to fouling is of great value but constitutes a challenge as a result of their complexity. In an effort to reduce that complexity, a study of a synthesized foulant of archipelago structure is presented. A critical perspective on previously described solubility and aggregation mechanisms (e.g., critical nanoaggrerate concentration and critical clustering concentration) is offered because the characterized system favors a continuous distribution of n-mers instead. A battery of experimental and modeling techniques have been employed to link the bulk and interfacial behavior of a representative foulant monomer to effective fouling mitigation strategies. This systematic approach defines a new multiscale methodology in the investigation of fouling systems.
Aasen A, Hammer M, Ervik Å, et al., 2019, Equation of state and force fields for Feynman–Hibbs-corrected Mie fluids. I. Application to pure helium, neon, hydrogen and deuterium, Journal of Chemical Physics, Vol: 151, ISSN: 0021-9606
We present a perturbation theory that combines the use of a third-order Barker–Henderson expansion of theHelmholtz energy with Mie-potentials that include first (Mie-FH1) and second-order (Mie-FH2) Feynman–Hibbs corrections. The resulting equation of state (SAFT-VRQ Mie) is compared to molecular simulations,and is seen to reproduce the thermodynamic properties of generic Mie-FH1 and Mie-FH2 fluids accurately.SAFT-VRQ Mie is exploited to obtain optimal parameters for the potentials of neon, helium, deuterium,ortho-, para- and normal-hydrogen for the Mie-FH1 and Mie-FH2 formulations. For helium, hydrogen anddeuterium, the use of either the first or second-order corrections yields significantly higher accuracy in therepresentation of supercritical densities, heat capacities and speed of sounds when compared to classical Miefluids, although the Mie-FH2 is slightly more accurate than Mie-FH1 for supercritical properties. The MieFH1 potential is recommended for most of the fluids since it yields a more accurate representation of thepure-component phase equilibria and extrapolates better to low temperatures. Notwithstanding, for helium,where the quantum effects are largest, we find that none of the potentials give an accurate representation ofthe entire phase envelope, and its thermodynamic properties are represented accurately only at temperaturesabove 20 K. Overall, supercritical heat capacities are well represented, with some deviations from experimentsseen in the liquid phase region for helium and hydrogen.
Joss L, Muller E, 2019, Machine learning for fluid property correlations: Classroom examples with MATLAB, Journal of Chemical Education, Vol: 96, Pages: 697-703, ISSN: 0021-9584
Recent advances in computer hardware and algorithms are spawning an explosive growth in the use of computer-based systems aimed at analyzing and ultimately correlating large amounts of experimental and synthetic data. As these machine learning tools become more widespread, it is becoming imperative that scientists and researchers become familiar with them, both in terms of understanding the tools and the current limitations of artificial intelligence, and more importantly being able to critically separate the hype from the real potential. This article presents a classroom exercise aimed at first-year science and engineering college students, where a task is set to produce a correlation to predict the normal boiling point of organic compounds from an unabridged data set of >6000 compounds. The exercise, which is fully documented in terms of the problem statement and the solution, guides the students to initially perform a linear correlation of the boiling point data with a plausible relevant variable (the molecular weight) and to further refine it using multivariate linear fitting employing a second descriptor (the acentric factor). Finally, the data are processed through an artificial neural network to eventually provide an engineering-quality correlation. The problem statements, data files for the development of the exercise, and solutions are provided within a MATLAB environment but are general in nature.
Wand CR, Fayaz-Torshizi M, Jimenez-Serratos G, et al., 2019, Solubilities of pyrene in organic solvents: Comparison between chemical potential calculations using a cavity-based method and direct coexistence simulations, The Journal of Chemical Thermodynamics, Vol: 131, Pages: 620-629, ISSN: 0021-9614
In this paper, we benchmark a cavity-based simulation method for calculating the relative solubility of large molecules in explicit solvents. The essence of the procedure is the accounting of the Gibbs energy change associated with an alchemical thermodynamic cycle where, in sequence, a cavity is created in a solvent, a solute is inserted in the cavity and the cavity is annihilated. The Gibbs energy change is equated to the excess chemical potential allowing the comparison of solubilities in different solvents. The results obtained using the cavity-based method are compared to direct large-scale molecular dynamics simulations performed using coarse-grained models for calculating the partition coefficient of pyrene between heptane and toluene. We demonstrate the applicability of this cavity-based technique under high pressure/temperature conditions.
Jiménez-Serratos G, Totton TS, Jackson G, et al., 2019, Aggregation behavior of model asphaltenes revealed from large-scale coarse-grained molecular simulations, Journal of Physical Chemistry B, Vol: 123, Pages: 2380-2396, ISSN: 1520-5207
Fully atomistic simulations of models of asphaltenes in simple solvents have allowed the study of trends in aggregation phenomena and the understanding of the role that molecular structure plays therein. However, the detail included at this scale of molecular modeling is at odds with the required spatial and temporal resolution needed to fully understand the asphaltene aggregation. The computational cost required to explore the relevant scales can be reduced by employing coarse-grained (CG) models, which consist of lumping a few atoms into a single segment that is characterised by effective interac- tions. In this work CG force fields developed via the SAFT-γ [Müller, E.A., Jackson, G. (2014) Annu. Rev. Chem. Biomolec. Eng., 5, 405–427] equation of state (EoS) provide a reliable pathway to link the molecular description with macroscopic thermophysical data. A recent modification of the SAFT-VR EoS [Müller, E.A. and Mejía, A. (2017) Langmuir, 33, 11518–11529], that allows parametrizing homonuclear rings, is selected as the starting point to propose CG models for polycyclic aromatic hydrocarbons (PAHs). The new aromatic-core parameters, along with others published for simpler organic molecules, are adopted for the construction of asphaltene models by combining different chemical moieties in a group-contribution fashion. We apply the procedure to two previously reported asphaltene models and perform Molecular Dynamics simulations to validate the coarse-grained representation against benchmark systems of 27 asphaltenes in pure solvent (toluene or heptane) described in a fully atomistic fashion. An excellent match between both levels of description is observed for cluster size, radii of gyration, and relative-shape-anisotropy-factor distributions. We exploit the advantages of the CG representation by simulating systems containing up to 2000 asphaltene molecules in explicit solvent investigating the effect of asphaltene concentration, so
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