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

Simulation and Data-Driven Modeling of the Transport Properties of the Mie Fluid

This work reports the computation and modeling of the self-diffusivity (D*), shear viscosity (η*), and thermal conductivity (κ*) of the Mie fluid. The transport properties were computed using equilibrium molecular dynamics simulations for the Mie fluid with repulsive exponents (λr) ranging from 7 to 34 and at a fixed attractive exponent (λa) of 6 over the whole fluid density (ρ*) range and over a wide temperature (T*) range. The computed database consists of 17,212, 14,288, and 13,099 data points for self-diffusivity, shear viscosity, and thermal conductivity, respectively. The database is successfully validated against published simulation data. The above-mentioned transport properties are correlated using artificial neural networks (ANNs). Two modeling approaches were tested: a semiempirical formulation based on entropy scaling and an empirical formulation based on density and temperature as input variables. For the former, it was found that a unique formulation based on entropy scaling does not yield satisfactory results over the entire density range due to a divergent and incorrect scaling of the transport properties at low densities. For the latter empirical modeling approach, it was found that regularizing the data, e.g., modeling ρ*D* instead of D*, ln η* instead of η*, and ln κ* instead of κ*, as well as using the inverse of the temperature as an input feature, helps to ease the interpolation efforts of the artificial neural networks. The trained ANNs can model seen and unseen data over a wide range of density and temperature. Ultimately, the ANNs can be used alongside equations of state to regress effective force field parameters from volumetric and transport data.

The structure of liquid thiophene from total neutron scattering

The structure of pure liquid thiophene is revealed by using a combination of total neutron scattering experiments with isotopic substitution and molecular simulations via the next generation empirical potential refinement software, Dissolve. In the liquid, thiophene presents three principle local structural motifs within the first solvation shell, in plane and out of the plane of the thiophene ring. Firstly, above/below the ring plane thiophenes present a single H towards the π cloud, due to a combination of electrostatic and dispersion interactions. Secondly, around the ring plane, perpendicular thiophene molecules find 5 preferred sites driven by bifurcated C-H⋯S interactions, showing that hydrogen-sulfur bonding prevails over the charge asymmetry created by the heteroatom. Finally, parallel thiophenes sit above and below the ring, excluded from directly above the ring center and above the sulfur.

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.

Development of thermodynamically consistent machine-learning equations of state: Application to the Mie fluid

A procedure for deriving thermodynamically consistent data-driven equations of state (EoS) for fluids is presented. The method is based on fitting the Helmholtz free energy using artificial neural networks to obtain a closed-form relationship between the thermophysical properties of fluids (FE-ANN EoS). As a proof-of-concept, an FE-ANN EoS is developed for the Mie fluids, starting from a database obtained by classical molecular dynamics simulations. The FE-ANN EoS is trained using first- (pressure and internal energy) and second-order (e.g., heat capacities, Joule-Thomson coefficients) derivative data. Additional constraints ensure that the data-driven model fulfills thermodynamically consistent limits and behavior. The results for the FE-ANN EoS are shown to be as accurate as the best available analytical model while being developed in a fraction of the time. The robustness of the "digital" equation of state is exemplified by computing physical behavior it has not been trained on, for example, fluid phase equilibria. Furthermore, the model's internal consistency is successfully assessed using Brown's characteristic curves.

Biomass carbon mining to develop nature-inspired materials for a circular economy

A transition from a linear to a circular economy is the only alternative to reduce current pressures in natural resources. Our society must redefine our material sources, rethink our supply chains, improve our waste management, and redesign materials and products. Valorizing extensively available biomass wastes, as new carbon mines, and developing biobased materials that mimic nature's efficiency and wasteless procedures are the most promising avenues to achieve technical solutions for the global challenges ahead. Advances in materials processing, and characterization, as well as the rise of artificial intelligence, and machine learning, are supporting this transition to a new materials' mining. Location, cultural, and social aspects are also factors to consider. This perspective discusses new alternatives for carbon mining in biomass wastes, the valorization of biomass using available processing techniques, and the implementation of computational modeling, artificial intelligence, and machine learning to accelerate material's development and process engineering.

Molecular Dynamics Simulations of Asphaltene Aggregation: Machine-Learning Identification of Representative Molecules, Molecular Polydispersity, and Inhibitor Performance

Molecular dynamics simulations have been employed to investigate the effect of molecular polydispersity on the aggregation of asphaltene. To make the large combinatorial space of possible asphaltene blends accessible to a systematic study via simulation, an upfront unsupervised machine-learning approach (clustering) was employed to identify a reduced set of model molecules representative of the diversity of asphaltene. For these molecules, single asphaltene model simulations have shown a broad range of aggregation behaviors, driven by their structural features: size of the aromatic core, length of the aliphatic chains, and presence of heteroatoms. Then, the combination of these model molecules in a series of mixtures have highlighted the complex and diverse effects of molecular polydispersity on the aggregation process of asphaltene. Simulations yielded both antagonistic and synergistic effects mediated by the trigger or facilitator action of specific asphaltene model molecules. These findings illustrate the necessity of accounting for molecular polydispersity when studying the asphaltene aggregation process and have permitted establishing a robust protocol for the in silico evaluation of the performance of asphaltene inhibitors, as illustrated for the case of a nonylphenol resin.

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.

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.

Surrogate Models for Studying the Wettability of Nanoscale Natural Rough Surfaces Using Molecular Dynamics

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.

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.