Simultaneous doxorubicin encapsulation and in-situ microfluidic micellization of bio-targeted polymeric nanohybrids using dichalcogenide monolayers : A molecular in-silico study

The rate of Riboflavin (RF) consumption in cancerous cells is interestingly high and this might imply the use of RF ligand in nanocarriers in order to target anticancer drugs into cancer cells. This study aimed to develop a hybrid drug carrier of Doxorubicin (DOX) loaded on RF targeted micelles composed of hydrophobic polylacticglycolic acid (PLGA) and hydrophilic polyethylene glycol (PEG). In this regard, a simultaneous encapsulation of DOX and in-situ micellization as well as the self-assembly of PLGA-PEG-RF molecules were investigated. Moreover, the effects of microfluidic environment and transition metal dichalcogenide (TMD) nanolayers on the micellization properties (e.g., stability, size, and self-assembly interaction energies) of nanocarriers were simulated for the first time. To this purpose, the simulations were performed using two non-microfluidic methods as well as a novel microfluidic one. The molecular simulations revealed that all of the selected TMDs, especially MoSe2, had a great impact on the stability and size of nanocarriers. MoSe2 significantly enhanced the loading capacity as well as the stability of RF-targeted micelles and reduced the size of nanocarriers. Likewise, the results of various analyses demonstrated that the microfluidic method is the most effective way to synthesize nano carriers with higher stability and smaller particle size. Hence, the use of MoSe2 monolayer, micelle containing RF, and microfluidic method were believed to be the best approach in order to improve the quality of micelles. The present work sheds new light on the use of TMDs in the synthesis of smart carriers for cancer treatment.Peer reviewe

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

Molecular insight into COF monolayers for urea sorption in artificial kidneys

Abstract 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