Significant attempts have been made to improve the production of ion-selective membranes (ISMs) with higher efficiency and lower prices, while the traditional methods have drawbacks of limitations, high cost of experiments, and time-consuming computations. One of the best approaches to remove the experimental limitations is artificial intelligence (AI). This review discusses the role of AI in materials discovery and ISMs engineering. The AI can minimize the need for experimental tests by data analysis to accelerate computational methods based on models using the results of ISMs simulations. The coupling with computational chemistry makes it possible for the AI to consider atomic features in the output models since AI acts as a bridge between the experimental data and computational chemistry to develop models that can use experimental data and atomic properties. This hybrid method can be used in materials discovery of the membranes for ion extraction to investigate capabilities, challenges, and future perspectives of the AI-based materials discovery, which can pave the path for ISMs engineering.
Microfluidic synthesis methods are among the most promising approaches for controlling the size and morphology of polymeric nanoparticles (NPs). In this work, for the first time, atomistic mechanisms involved in morphological changes of polybenzimidazole (PBI) NPs in microfluidic media are investigated. The multiscale molecular dynamic (MD) simulations are validated with the literature modeling and experimental data. A good agreement is obtained between the molecular modeling results and experimental data. The effects of mixing time, solvent type, dopant, and simulation box size at the molecular level are investigated. Mixing time has a positive impact on the morphology of the PBI NPs. Microfluidic technology can control the mixing time well and engineer the morphology of the NPs. In the process of morphological changes, at the optimum time (about 11.5 ms), the attraction energy between the polymer molecules is at the highest level (−37.65 kJ/mol). The size of the polymer NPs is minimal (2.3 nm), and the aspect ratio and entropy are at the lowest level, equal to 1.07 and 11.024 kJ/mol•K, respectively. It was found that the presence of water leads to the precipitation of polymeric NPs owing to the dominance of hydrophobic forces. Both dimethylacetamide (DMA) and phosphoric acid (PA) improve the control of the size and morphology of NPs. However, the addition of PA has a greater impact; PA acts as a cross-linker, making PBI NPs finer and more spherical. In addition, MD simulation reveals that PA increases the proton diffusion coefficient in PBI and enhances its efficiency in fuel cells. This study paves a new efficient way for morphological engineering of polymeric NPs using microfluidic technology.
Lithium ions play a crucial role in the energy storage industry. Finding suitable lithium-ion-conductive membranes is one of the important issues of energy storage studies. Hence, a perovskite-based membrane, Lithium Lanthanum Titanate (LLTO), was innovatively implemented in the presence and absence of solvents to precisely understand the mechanism of lithium ion separation. The ion-selective membrane’s mechanism and the perovskite-based membrane’s efficiency were investigated using Molecular Dynamic (MD) simulation. The results specified that the change in the ambient condition, pH, and temperature led to a shift in LLTO pore sizes. Based on the results, pH plays an undeniable role in facilitating lithium ion transmission through the membrane. It is noticeable that the hydrogen bond interaction between the ions and membrane led to an expanding pore size, from (1.07 Å) to (1.18–1.20 Å), successfully enriching lithium from seawater. However, this value in the absence of the solvent would have been 1.1 Å at 50. It was found that increasing the temperature slightly impacted lithium extraction. The charge analysis exhibited that the trapping energies applied by the membrane to the first three ions (Li+, K+, and Na+) were more than the ions’ hydration energies. Therefore, Li+, K+, and Na+ were fully dehydrated, whereas Mg2+ was partially dehydrated and could not pass through the membrane. Evaluating the membrane window diameter, and the combined effect of the three key parameters (barrier energy, hydration energy, and binding energy) illustrates that the required energy to transport Li ions through the membrane is higher than that for other monovalent cations.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.