How Does a Microfluidic Platform Tune the Morphological Properties of Polybenzimidazole Nanoparticles?

Mechanism Understanding of Li-ion Separation Using A Perovskite-Based Membrane

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 °C. 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.

Materials discovery of ion-selective membranes using artificial intelligence

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 of -NH2- and -COOH-Functionalized Magnetite Nanoparticles

Microfluidics has emerged as a promising alternative for the synthesis of nanoparticles, which ensures precise control over the synthesis parameters, high uniformity, reproducibility, and ease of integration. Therefore, the present study investigated a one-step synthesis and functionalization of magnetite nanoparticles (MNPs) using sulfanilic acid (SA) and 4-sulfobenzoic acid (SBA). The flows of both the precursor and precipitating/functionalization solutions were varied in order to ensure the optimal parameters. The obtained nanoparticles were characterized through dynamic light scattering (DLS) and zeta potential, X-ray diffraction (XRD), selected area electron diffraction (SAED), transmission electron microscopy (TEM) and high-resolution TEM (HR-TEM), Fourier transform infrared spectroscopy (FT-IR), thermogravimetry and differential scanning calorimetry (TG-DSC), and vibrating sample magnetometry (VSM). The results demonstrated the successful synthesis of magnetite as the unique mineralogical phase, as well as the functionalization of the nanoparticles. Furthermore, the possibility to control the crystallinity, size, shape, and functionalization degree by varying the synthesis parameters was further confirmed. In this manner, this study validated the potential of the microfluidic platform to develop functionalized MNPs, which are suitable for biomedical and pharmaceutical applications.