Interfacial Tension-Temperature-Pressure-Salinity Relationship for the Hydrogen-Brine System under Reservoir Conditions: Integration of Molecular Dynamics and Machine Learning.

Hydrogen (H2) underground storage has attracted considerable attention as a potentially efficient strategy for the large-scale storage of H2. Nevertheless, successful execution and long-term storage and withdrawal of H2 necessitate a thorough understanding of the physical and chemical properties of H2 in contact with the resident fluids. As capillary forces control H2 migration and trapping in a subsurface environment, quantifying the interfacial tension (IFT) between H2 and the resident fluids in the subsurface is important. In this study, molecular dynamics (MD) simulation was employed to develop a data set for the IFT of H2-brine systems under a wide range of thermodynamic conditions (298-373 K temperatures and 1-30 MPa pressures) and NaCl salinities (0-5.02 mol·kg-1). For the first time to our knowledge, a comprehensive assessment was carried out to introduce the most accurate force field combination for H2-brine systems in predicting interfacial properties with an absolute relative deviation (ARD) of less than 3% compared with the experimental data. In addition, the effect of the cation type was investigated for brines containing NaCl, KCl, CaCl2, and MgCl2. Our results show that H2-brine IFT decreases with increasing temperature under any pressure condition, while higher NaCl salinity increases the IFT. A slight decrease in IFT occurs when the pressure increases. Under the impact of cation type, Ca2+ can increase IFT values more than others, i.e., up to 12% with respect to KCl. In the last step, the predicted IFT data set was used to provide a reliable correlation using machine learning (ML). Three white-box ML approaches of the group method of data handling (GMDH), gene expression programming (GEP), and genetic programming (GP) were applied. GP demonstrates the most accurate correlation with a coefficient of determination (R2) and absolute average relative deviation (AARD) of 0.9783 and 0.9767%, respectively.

Molecular insight into COF monolayers for urea sorption in artificial kidneys.

Urea removal from an aqueous solution is considered a challenge in the biological process. The state of complete kidney destruction is known as an end-stage renal disease (ESRD). Kidney transplant and hemodialysis are the most common methods for confronting ESRD. More recently, wearable artificial kidney (WAK) devices have shown a significant improvement in urea removal performance. However, low efficiency in physical adsorbents is a barrier in developing them. For the first time, the urea adsorption capacity of five types of last-generation covalent organic framework (COF) nanosheets (NSs) was investigated in this study by applying molecular dynamics (MD) simulation tools. To this end, different analyses have been performed to evaluate the performance of each nanoparticle. The MD all-atom (AA) results demonstrated that all introduced COF NSs had urea removal capacity. Among the five NSs, TPA-COF was shown to have the best outcomes. Moreover, coarse-grained (CG) and density functional theory (DFT) simulations were conducted, and the results show that the TPA-COF nanoparticle modified with -OH functional group has even better properties for urea adsorption. The present molecular study sheds new light on COF NSs as an adsorbent for urea removal.