Mesoscale Simulation of Asphaltene Aggregation

Mesoscale Simulations Reveal How Salt Influences Clay Particles Agglomeration in Aqueous Dispersions

The aggregation of clay particles is an everyday phenomenon of scientific and industrial relevance. However, it is a complex multiscale process that depends delicately on the nature of the particle-particle and particle-solvent interactions. Toward understanding how to control such phenomena, a multiscale computational approach is developed, building from molecular simulations conducted at atomic resolution to calculate the potential of mean force (PMF) profiles in both pure and saline water environments. We document how it is possible to use such a model to develop a fundamental understanding concerning the mechanism of particle aggregation. For example, using molecular dynamics simulations conducted at the mesoscale in implicit solvents, it is possible to quantify the size and shape of clay aggregates as a function of system conditions. The approach is used to emphasize the role of salt concentration, which directly affects the potentials of the mean forces between kaolinite particles. While particle agglomeration in pure water yields large aggregates, the presence of sodium chloride in the aqueous brine leads instead to a large number of small aggregates. These results are consistent with macroscopic experimental observations, suggesting that the simulation protocol developed could be relevant for preventing pore blocking in heterogeneous porous matrixes.

Effects of π-π Stacking on Shale Gas Adsorption and Transport in Nanopores

The π-π interaction is a prevalent driving force in the formation of various organic porous media, including the shale matrix. The configuration of π-π stacking in the shale matrix significantly influences the properties of shale gas and plays a crucial role in understanding and exploiting gas resources. In this research, we investigate the impact of different π-π stacking configurations on the adsorption and transport of shale gas within the nanopores of the shale matrix. To achieve this, we construct kerogen nanopores using π-π stacked columns with varying stacking configurations, such as offset/parallel stacking types and different orientations of the stacked columns. Through molecular dynamics simulations, we examined the adsorption and transport of methane within these nanopores. Our findings reveal that methane exhibits stronger adsorption in smoother nanopores, with this adsorption remaining unaffected by the nanoflow. We observe a heterogeneous distribution of the 2D adsorption free energy, which correlates with the specific π-π stacking configurations. Additionally, we introduce the concept of "directional roughness" to describe the surface characteristics, finding that the nanoflow flux increases as the roughness decreases. This research contributes to the understanding of shale gas behavior in the shale matrix and provides insights into nanoflow properties in other porous materials containing π-π stackings.

Mesoscale computer modeling of asphaltene aggregation in liquid paraffin

Asphaltenes represent a novel class of carbon nanofillers that are of potential interest for many applications, including polymer nanocomposites, solar cells, and domestic heat storage devices. In this work, we developed a realistic coarse-grained Martini model that was refined against the thermodynamic data extracted from atomistic simulations. This allowed us to explore the aggregation behavior of thousands of asphaltene molecules in liquid paraffin on a microsecond time scale. Our computational findings show that native asphaltenes with aliphatic side groups form small clusters that are uniformly distributed in paraffin. The chemical modification of asphaltenes via cutting off their aliphatic periphery changes their aggregation behavior: modified asphaltenes form extended stacks whose size increases with asphaltene concentration. At a certain large concentration (44 mol. %), the stacks of modified asphaltenes partly overlap, leading to the formation of large, disordered super-aggregates. Importantly, the size of such super-aggregates increases with the simulation box due to phase separation in the paraffin-asphaltene system. The mobility of native asphaltenes is systematically lower than that of their modified counterparts since the aliphatic side groups mix with paraffin chains, slowing down the diffusion of native asphaltenes. We also show that diffusion coefficients of asphaltenes are not very sensitive to the system size: enlarging the simulation box results in some increase in diffusion coefficients, with the effect being less pronounced at high asphaltene concentrations. Overall, our findings provide valuable insight into the aggregation behavior of asphaltenes on spatial and time scales that are normally beyond the scales accessible for atomistic simulations.

Experimental and molecular dynamics studies on aggregation behaviour of salicylaldehyde azine ester

Aggregation phenomena arise predominantly due to self-organisation of molecules to form supramolecular assemblies leading to restriction of intramolecular motions. In the present study, the solvent-induced aggregation of salicylaldehyde azine ester (SAE) was comprehensively investigated through experimental techniques, and classical molecular dynamics simulations (MDS). The emission spectra and particle sizes of SAE in THF-water mixtures confirmed the formation of nanoaggregates. The interaction of SAE aggregates with the solvent mixture was studied using Fourier-transform Infrared spectroscopy. The optical microscopy images and surface morphology analysis reinforced the nanoaggregate formation of SAE in solvent mixtures with increasing water fractions. The average number of H-bonds, diffusion coefficients and trajectory density contours of the aggregates were investigated through MDS studies, which provided atomistic perceptions into the formation of rod-like SAE nanoaggregates. The combined results of experimental and theoretical studies offer deeper insights into the self-aligning tendency of SAE in THF-water mixtures.

Mesoscale Aggregation of Sulfur-Rich Asphaltenes: In Situ Microscopy and Coarse-Grained Molecular Simulation

Asphaltene aggregation is critical to many natural and industrial processes, from groundwater contamination and remediation to petroleum utilization. Despite extensive research in the past few decades, the fundamental process of sulfur-rich asphaltene aggregation still remains not fully understood. In this work, we have investigated the particle-by-particle growth of aggregates formed with sulfur-rich asphaltene by a combined approach of in situ microscopy and molecular simulation. The experimental results show that aggregates assembled from sulfur-rich asphaltene have morphologies with time-dependent structural self-similarity, and their growth rates are aligned with a crossover behavior between classic reaction-limited aggregation and diffusion-limited aggregation. Although the particle size distribution predicted using the Smoluchowski equation deviates from the observations at the initial stage, it provides a reasonable prediction of aggregate size distribution at the later stage, even if the observed cluster coalescence has an important effect on the corresponding cluster size distribution. The simulation results show that aliphatic sulfur exerts nonmonotonic effects on asphaltene nanoaggregate formation depending on the asphaltene molecular structure. Specifically, aliphatic sulfur has a profound effect on the structure of rod-like nanoaggregates, especially when asphaltene molecules have small aromatic cores. Interactions between aliphatic sulfur and the side chain of neighboring molecules account for the repulsive forces that largely explain the polydispersity in the nanoaggregates and corresponding colloidal aggregates. These results can improve our current understanding of the complex process of sulfur-rich asphaltene aggregation and sheds light on designing efficient crude oil utilization and remediation technologies.

Molecular Dynamics Simulations of Nanostructures Formed by Hydrophobins and Oil in Seawater

Classical molecular dynamics simulations using the Martini coarse-grained force field were performed to study oil nanodroplets surrounded by fungal hydrophobin (HP) proteins in seawater. The class I EAS and the class II HFBII HPs were studied along with two model oils, namely, benzene and n-decane. Both HPs exhibit free energy minima at the oil-seawater interface, which is deeper in benzene compared to the n-decane systems. Larger constraint forces are required to keep both HPs within the n-decane phase compared to inside benzene, with HFBII being more affine to benzene compared to EAS. Smaller surface tensions are observed at benzene-seawater interfaces coated with HPs compared to their n-decane counterparts. In the latter the surface tension remains unchanged upon increases in the concentration of HPs, whereas in benzene systems adding more HPs lead to decreases in surface tension. EAS has a larger tendency to cluster together in the interface compared to HFBII, with both HPs having larger coordination numbers when surrounding benzene droplets compared to when they are around n-decane nanoblobs. The HP-oil nanostructures in seawater examined have radii of gyration ranging between 2 and 12 nm, where the n-decane structures are larger and have more irregular shapes compared to the benzene systems. The n-decane molecules within the nanostructures form a compact spherical core, with the HPs partially covering its surface and clustering together, conferring irregular shapes to the nanostructures. The EAS with n-decane structures are larger and have more irregular shapes compared to their HFBII counterparts. In contrast, in the HP-benzene structures both HPs tend to penetrate the oil part of the droplet. The HFBII-benzene structures having the larger oil/HP ratios examined tend to be more compact and spherical compared to their EAS counterparts; however, some of the HFBII-benzene systems that have smaller oil/HP ratios have a more elongated structure compared to their EAS counterparts. This simulation study provides insights into HP-oil nanostructures that are smaller than the oil droplets and gas bubbles recently studied in experiments and, thus, might be challenging to examine with experimental techniques.

High resolution nanoscale chemical analysis of bitumen surface microstructures

Surface microstructures of bitumen are key sites in atmospheric photo-oxidation leading to changes in the mechanical properties and finally resulting in cracking and rutting of the material. Investigations at the nanoscale remain challenging. Conventional combination of optical microscopy and spectroscopy cannot resolve the submicrostructures due to the Abbe restriction. For the first time, we report here respective surface domains, namely catana, peri and para phases, correlated to distinct molecules using combinations of atomic force microscopy with infrared spectroscopy and with correlative time of flight-secondary ion mass spectrometry. Chemical heterogeneities on the surface lead to selective oxidation due to their varying susceptibility to photo-oxidation. It was found, that highly oxidized compounds, are preferentially situated in the para phase, which are mainly asphaltenes, emphasising their high oxidizability. This is an impressive example how chemical visualization allows elucidation of the submicrostructures and explains their response to reactive oxygen species from the atmosphere.

The Martini Model in Materials Science

The Martini model, a coarse-grained force field initially developed with biomolecular simulations in mind, has found an increasing number of applications in the field of soft materials science. The model's underlying building block principle does not pose restrictions on its application beyond biomolecular systems. Here, the main applications to date of the Martini model in materials science are highlighted, and a perspective for the future developments in this field is given, particularly in light of recent developments such as the new version of the model, Martini 3.

Estimating the asphaltene critical nanoaggregation concentration region using ultrasonic measurements and Bayesian inference

Bayesian inference and ultrasonic velocity have been used to estimate the self-association concentration of the asphaltenes in toluene using a changepoint regression model. The estimated values agree with the literature information and indicate that a lower abundance of the longer side-chains can cause an earlier onset of asphaltene self-association. Asphaltenes constitute the heaviest and most complicated fraction of crude petroleum and include a surface-active sub-fraction. When present above a critical concentration in pure solvent, asphaltene "monomers" self-associate and form nanoaggregates. Asphaltene nanoaggregates are thought to play a significant role during the remediation of petroleum spills and seeps. When mixed with water, petroleum becomes expensive to remove from the water column by conventional methods. The main reason of this difficulty is the presence of highly surface-active asphaltenes in petroleum. The nanoaggregates are thought to surround the water droplets, making the water-in-oil emulsions extremely stable. Due to their molecular complexity, modelling the self-association of the asphaltenes can be a very computationally-intensive task and has mostly been approached by molecular dynamic simulations. Our approach allows the use of literature and experimental data to estimate the nanoaggregation and its credible intervals. It has a low computational cost and can also be used for other analytical/experimental methods probing a changepoint in the molecular association behaviour.

Discovery of Self-Assembling π-Conjugated Peptides by Active Learning-Directed Coarse-Grained Molecular Simulation

Electronically active organic molecules have demonstrated great promise as novel soft materials for energy harvesting and transport. Self-assembled nanoaggregates formed from π-conjugated oligopeptides composed of an aromatic core flanked by oligopeptide wings offer emergent optoelectronic properties within a water-soluble and biocompatible substrate. Nanoaggregate properties can be controlled by tuning core chemistry and peptide composition, but the sequence-structure-function relations remain poorly characterized. In this work, we employ coarse-grained molecular dynamics simulations within an active learning protocol employing deep representational learning and Bayesian optimization to efficiently identify molecules capable of assembling pseudo-1D nanoaggregates with good stacking of the electronically active π-cores. We consider the DXXX-OPV3-XXXD oligopeptide family, where D is an Asp residue and OPV3 is an oligophenylenevinylene oligomer (1,4-distyrylbenzene), to identify the top performing XXX tripeptides within all 20³ = 8000 possible sequences. By direct simulation of only 2.3% of this space, we identify molecules predicted to exhibit superior assembly relative to those reported in prior work. Spectral clustering of the top candidates reveals new design rules governing assembly. This work establishes new understanding of DXXX-OPV3-XXXD assembly, identifies promising new candidates for experimental testing, and presents a computational design platform that can be generically extended to other peptide-based and peptide-like systems.

Toward Predictive Molecular Dynamics Simulations of Asphaltenes in Toluene and Heptane

The conventional definition of asphaltenes is based on their solubility in toluene and their insolubility in heptane. We have utilized this definition to study the influence of partial charge parametrization on the aggregation behavior of asphaltenes using classical atomistic molecular dynamics simulations performed on the microsecond time scale. Under consideration here are toluene- and heptane-based systems with different partial charges parametrized using the general AMBER force field (GAFF). Systems with standard GAFF partial charges calculated by the AM1-BCC and HF/6-31G*(RESP) methods were simulated alongside systems without partial charges. The partial charges implemented differ in terms of the resulting electrical negativity of the asphaltene polyaromatic core, with the AM1-BCC method giving the greatest magnitude of the total core charge. Based on our analysis of the molecular relaxation and orientation, and on the aggregation behavior of asphaltenes in toluene and heptane, we proposed to use the partial charges obtained by the AM1-BCC method for the study of asphaltene aggregates. A good agreement with available experimental data was observed on the sizes of the aggregates, their fractal dimensions, and the solvent entrainment for the model asphaltenes in toluene and heptane. From the results obtained, we conclude that for a better predictive ability, simulation parameters must be carefully chosen, with particular attention paid to the partial charges owing to their influence on the electrical negativity of the asphaltene core and on the asphaltenes aggregation.

Aggregation Behavior of Model Asphaltenes Revealed from Large-Scale Coarse-Grained Molecular Simulations

Fully atomistic simulations of models of asphaltenes in simple solvents have allowed the study of trends in aggregation phenomena to understand the underlying role played by molecular structure. The detail included at this scale of molecular modeling is, however, at odds with the required spatial and temporal resolution needed to fully understand 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 characterized by effective interactions. In this work, CG force fields developed via the statistical associating fluid theory (SAFT-γ) [ Müller , E. A. ; Jackson , G. Annu. Rev. Chem. Biomol. Eng. 5 , 2014 , 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. ; Mejía , A. Langmuir 33 , 2017 , 11518 - 11529 ], which allows for the parameterization of homonuclear rings, is selected as the starting point to develop CG models for polycyclic aromatic hydrocarbons. The new aromatic-core models, 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 a pure solvent (toluene or heptane) described in a fully atomistic fashion. An excellent match between both levels of description is observed for the 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 an explicit solvent investigating the effect of asphaltene concentration, solvent composition, and temperature on aggregation. By studying large systems facilitated by the use of CG models, we observe stable continuous distributions of molecular aggregates at conditions away from the two-phase precipitation point. As a further example application, a widely accepted interpretation of cluster-size distributions in asphaltenic systems is challenged by performing system-size tests, reversibility checks, and a time-dependence analysis. The proposed coarse-graining procedure is seen to be general and predictive and, hence, can be applied to other asphaltenic molecular structures.

Asphaltene Mesoscale Aggregation Behavior in Organic Solvents-A Brownian Dynamics Study

Significant advances have been achieved in understanding the main molecular mechanisms leading to asphaltene aggregation. However, the existing computational deficiency of molecular dynamics simulations did not allow full reproduction of the complex aggregation behavior of asphaltene in the past. In this work, we use the Brownian dynamics simulation to investigate asphaltene aggregation behavior on larger length and time scales that have not been previously accessed by molecular simulations. This enabled us to completely render the formation of clusters of asphaltene nanoaggregates and the resulting fractal or network of aggregates during the aggregation process. Asphaltene aggregation is studied at several volume fractions (ϕ = 1-7%) of asphaltene nanoaggregates in two solvents including heptane and heptol (i.e., a mixture of heptane and toluene). Our simulation results support the aggregation hierarchy proposed in the Yen-Mullins model (Mullins, Annu. Rev. Anal. Chem. 2011, 4, 393-418.) by demonstrating that asphaltene nanoaggregates form small clusters with an aggregation number of 7-8 and an average gyration radius of ∼4.0 nm capable of forming either fractal aggregates with a fractal dimension of 1.93-2.04 at low ϕ or percolating networks of aggregates at high ϕ. Percolating structures are observed at ϕ = 7% in both solvents. In heptol, the structures mainly percolate along two directions, whereas in heptane, they can percolate along three directions (i.e., x, y, and z). The self-diffusion coefficient ( D) significantly decreases as ϕ increases. Generally, D is larger in heptol than in heptane, but this difference diminishes as ϕ increases, approaching to almost the same value at ϕ = 7%.

Enhanced Computational Sampling of Perylene and Perylothiophene Packing with Rigid-Body Models

Molecular simulations have the potential to advance the understanding of how the structure of organic materials can be engineered through the choice of chemical components but are limited by computational costs. The computational costs can be significantly lowered through the use of modeling approximations that capture the relevant features of a system, while lowering algorithmic complexity or by decreasing the degrees of freedom that must be integrated. Such methods include coarse-graining techniques, approximating long-range electrostatics with short-range potentials, and the use of rigid bodies to replace flexible bonded constraints between atoms. To understand whether and to what degree these techniques can be leveraged to enhance the understanding of planar organic molecules, we investigate the morphologies predicted by molecular dynamic simulations using simplified molecular models of perylene and perylothiophene. Approximately, 10 000 wall-clock hours of graphics processing unit-accelerated simulations are performed using both rigid and flexible models to test their efficiency and predictive capability with the two chemistries. We characterize the 1191 resulting morphologies using simulated X-ray diffraction and cluster analysis to distinguish structural transitions, summarized by four phase diagrams. We find that the morphologies generated by the rigid model of perylene and perylothiophene match with those generated by the flexible model. We find that ordered, hexagonally packed columnar phases are thermodynamically favored over a wide range of densities and temperatures for both molecules, in qualitative agreement with experiments. Furthermore, we find the rigid model to be more computationally efficient for both molecules, providing more samples per second and shorter times to equilibrium. Owing to the structural accuracy and improved computational efficiency of modeling polyaromatic groups as rigid bodies, we recommend this modeling choice for enhancing the sampling in polyaromatic molecular simulations.

Effects of the asphaltene structure and the tetralin/heptane solvent ratio on the size and shape of asphaltene aggregates

Asphaltene molecules, which consist of differently hydrogenated polyaromatic cores grafted with side alkyl chains of different sizes and grafting densities, were simulated with a solvent mixture of heptane and tetralin using coarse-grained force fields.

Control of the hierarchical assembly of π-conjugated optoelectronic peptides by pH and flow

Coarse-grained molecular simulations reveal the influence of pH and flow on the self-assembly of DFAG-OPV3-GAFD optoelectronic peptides.