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

CO2-philicity to CO2-phobicity Transition on Smooth and Stochastic Rough Cu-like Substrate Surfaces

CO2 on metal substrates is essential to CO2 liquefaction and transportation of CO2, yet the manipulation of the wettability of the CO2 and the elucidation of its underlying mechanism have not been fully achieved. Here, using molecular dynamics simulations, we report CO2 wetting characteristics on both smooth and stochastic rough Cu-like substrate surfaces. The results indicate that the apparent contact angle (CA) of the CO2 droplet on the smooth surface decreases from 180° to 0° as the CO2-solid characteristic interaction energy increases from 0.002 to 0.016 eV. In addition, the CAs become greater with increasing the density of surface asperities, regardless of the intrinsic surface wettability. This is attributed to the capillary drying-out of liquid CO2 molecules in gaps between surface asperities at the three-phase contact line of the droplet, which is usually overlooked in previous theoretical studies. Notably, the intrinsically CO2-philic surface transforms to the CO2-phobic due to an increase in the density of surface rugosity. Moreover, we verify the range of applicability of the CA prediction models concerning the nanoscale asperities. This work is beneficial for fully understanding the influence of nanoscale surface topography on CO2 wettability and shedding light on the design of functionalized and patterned surfaces to manipulate CO2 wettability.

Structure and Contact Angle in Sessile Droplets of Binary Mixtures of Lennard-Jones Chains: A Molecular Dynamics Study

Molecular dynamics simulations were carried out to investigate cylindrical droplets consisting of binary mixtures of Lennard-Jones (LJ) fluids in contact with a solid substrate. The droplets are composed of mixtures of the monomeric LJ fluid plus linear-tangent chains of 2, 10, 20, and 30 segments per chain that interact through a harmonic potential and the spherically truncated and shifted potential Lennard-Jones. The solid surface was modeled as a semi-infinite platinum substrate with an FCC structure that interacts with the fluid by means of a LJ 9-3 potential. We place emphasis on the effect of mixing a monomeric LJ fluid with heavy components on the contact angle and on the droplet structure, especially in the liquid-solid region. The density profiles of the droplets reveal a strong discrete layering of the fluid in the vicinity of the solid-liquid interface. The layering is more pronounced at low temperatures and for mixtures of short chains (symmetric mixtures). The ordering of the fluid was much less intense for fluids of long chains (asymmetric mixtures), and some cases even show gas enrichment at the solid-liquid interface. Enrichment at the vapor-liquid interfaces and density inversion can also be observed. However, these effects are not as marked as in planar interfaces. The contact angle between the droplet and the substrate is calculated by fitting an ellipse to the vapor-liquid interface defined by the Gibbs dividing surface. In general, an increment in the concentration of the heavy component and a reduction of the temperature resulted in an increase of the contact angle, which in turn disfavored the wetting of the droplet.

Molecular Origin of Wettability Alteration of Subsurface Porous Media upon Gas Pressure Variations

Upon extraction/injection of a large quantity of gas from/into a subsurface system in shale gas production or carbon sequestration, the gas pressure varies remarkably, which may significantly change the wettability of porous media involved. Mechanistic understanding of such changes is critical for designing and optimizing a related subsurface engineering process. Using molecular dynamics simulations, we have calculated the contact angle of a water droplet on various solid surfaces (kerogen, pyrophyllite, calcite, gibbsite, and montmorillonite) as a function of CO₂ or CH₄ gas pressure up to 200 atm at a temperature of 300 K. The calculation reveals a complex behavior of surface wettability alteration by gas pressure variation depending on surface chemistry and structure, and molecular interactions of fluid molecules with surfaces. As the CO₂ gas pressure increases, a partially hydrophilic kerogen surface becomes highly hydrophobic, while a calcite surface becomes more hydrophilic. Considering kerogen and calcite being the major components of a shale formation, we postulate that the wettability alteration of a solid surface induced by a gas pressure change may play an important role in fluid flows in shale gas production and geological carbon sequestration.

Predictive Analysis of Wettability of Al-Si Based Multiphase Alloys and Aluminum Matrix Composites by Machine Learning and Physical Modeling

Wetting of multiphase alloys and their composites depends on multiple parameters, and these relationships are difficult to predict from first principles only. We study correlations between the composition, surface finish, and microstructure of Al-Si alloys (Si content 7-50%) and Al metal matrix composites (MMCs) with graphite (Gr), NiAl₃, and SiC and the water contact angle (CA) experimentally, theoretically, and with machine learning (ML) techniques. Their surface properties were modified by mechanical abrasion, etching, and addition of alloying elements. An ML approach was developed to investigate correlations between the predictor variables (properties of the materials) and the CA. Theoretical models of wetting of rough surfaces (Wenzel, Cassie-Baxter, and their modifications) do not fully capture the CA, while ML models follow the experimental values. A full factorial design is utilized with combinations of all levels of the predictor factors (grit size, silicon percentage, droplet size, elapsed time, etching, reinforcing particles). To map the predictor variables to the response variables, 409 experimental data points were applied to train and test various supervised ML models, namely, regression, artificial neural network (ANN), chi-square automatic interaction detection (CHAID), extreme gradient boosting (XGBoost), and random forest. The correlations between the most significant factors and CA are explored through visualization techniques. The most accurately trained model shows a strong positive linear correlation (r > 0.9) between predicted and observed CA values in the test set, indicating the robustness of the model. The experimental measurements and artificial intelligence results demonstrate that CA increases following mechanically abrading the surface, etching, and adding Gr to the surface. The ML methods are promising to predict wetting properties and to provide a deeper understanding of the physical phenomena associated with the wettability of metallic alloys and their metal matrix composites.