Tackling antibiotic contaminations in wastewater with novel Modified-MOF nanostructures: A study of molecular simulations and DFT calculations.

The contamination of wastewater with antibiotics has emerged as a critical global challenge, with profound implications for environmental integrity and human well-being. Adsorption techniques have been meticulously investigated and developed to mitigate and alleviate their effects. In this study, we have investigated the adsorption behaviour of Erythromycin (ERY), Gentamicin (GEN), Levofloxacin (LEVO), and Metronidazole (MET) antibiotics as pharmaceutical contaminants (PHCs) on amide-functionalized (RC (=O)NH2)/MIL-53 (Al) (AMD/ML53A), using molecular simulations and density functional theory (DFT) calculations. Based on our DFT calculations, it becomes apparent that the adsorption tendencies of antibiotics are predominantly governed by the presence of AMD functional groups on the adsorbent surface. Specifically, hydrogen bonding (HB) and van der Waals (vdW) interactions between antibiotics and AMD groups serve as the primary mechanisms facilitating adsorption. Furthermore, we have observed that the adsorption behaviors of these antibiotics are influenced by their respective functional groups, molecular shapes, and sizes. Our molecular simulations delved into how the AMD/ML53A surfaces interact with antibiotics as PHCs. Moreover, various chemical quantum descriptors based on Frontier Molecular Orbitals (FMO) were explored to elucidate the extent of AMD/ML53A adsorption and to assess potential alterations in their electronic properties throughout the adsorption process. Monte Carlo simulation showed that ERY molecules adsorb stronger to the adsorbent in acidic and basic conditions than other contaminants, with high energies: -404.47 kcal/mol in acidic and -6375.26 kcal/mol in basic environments. Molecular dynamics (MD) simulations revealed parallel orientation for the ERY molecule's adsorption on AMD/ML53A with 80% rejection rate. In conclusion, our study highlighted the importance of modeling in developing practical solutions for removing antibiotics as PHCs from wastewater. The insights gained from our calculations can facilitate the design of more effective adsorption materials, ultimately leading to a more hygienic and sustainable ecosystem.

Integrative analysis of multi machine learning models for tetracycline photocatalytic degradation with MOFs in wastewater treatment.

This study focuses on the utilization of connectionist models, specifically Independent Component Analysis (ICA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Genetic Algorithm-Particle Swarm Optimization (GAPSO) integrated with a least-squares support vector machine (LSSVM) to forecast the degradation of tetracycline (TC) through photocatalysis using Metal-Organic Frameworks (MOFs). The primary objective of this study was to evaluate the viability and precision of these connectionist models in estimating the efficiency of TC degradation, particularly within the context of wastewater treatment. The input parameters for these models cover essential MOF characteristics, such as pore size and surface area, along with critical operational factors, such as pH, TC concentration, catalyst dosage, and illumination duration, all of which are linked to the photocatalytic performance of MOFs. Sensitivity analysis revealed that the illumination duration is the primary influencer of TC photodegradation with MOF photocatalysts, while the MOFs' surface area is the second crucial parameter shaping the efficiency and dynamics of the TC-MOF photocatalytic system. The developed LSSVM models display impressive predictive capabilities, effectively forecasting the experimental degradation of TC with high accuracy. Among these models, the GAPSO-LSSVM model excels as the top performer, achieving notable evaluation metrics, including STD, RMSE, MSE, MRE, and R2 at values of 3.09, 3.42, 11.71, 5.95, and 0.986, respectively. In comparison, the PSO-LSSVM, ICA-LSSVM, and GA-LSSVM models yield mean relative errors of 6.18%, 7.57%, and 11.37%, respectively. These outcomes highlight the exceptional predictive capabilities of the GAPSO-LSSVM model, solidifying its position as the most accurate and dependable model for predicting TC photodegradation in this study. This study contributes to advancing photocatalytic research and effectively reinforces the importance of leveraging machine learning methodologies for tackling environmental challenges.

Molecular dynamics simulation of anticancer drug delivery from carbon nanotube using metal nanowires.

In this study, we have investigated delivery of cisplatin as the anticancer drug molecules in different carbon nanotubes (CNTs) in the gas phase using molecular dynamics simulation. We examined the shape and composition of the releasing agent by using the different nanowires and nanoclusters. We also investigated the doping effect on the drug delivery process using N-, Si, B-, and Fe-doped CNTs. Different thermodynamics, structural, and dynamical properties have been studied by using the pure and different doped CNTs in this study. Our results show that the doping of the CNT has significant effect on the rate of the drug releasing process regardless of the composition of the releasing agent. © 2019 Wiley Periodicals, Inc.

A simple guiding principle for the temperature dependence of the solubility of light gases in imidazolium-based ionic liquids derived from molecular simulations.

We have determined the temperature dependence of the solvation behavior of a large collection of important light gases in imidazolium-based ionic liquids with the help of extensive molecular dynamics simulations. The motivation of our study is to unravel common features of the temperature dependent solvation under well controlled conditions, and to provide a guidance for cases, where experimental data from different sources disagree significantly. The solubility of molecular hydrogen, oxygen, nitrogen, methane, krypton, argon, neon and carbon dioxide in the imidazolium based ionic liquids of type 1-n-alkyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide ([Cnmim][NTf2]) with varying alkyl side chain lengths n = 2, 4, 6, 8 is computed for a temperature range between 300 K and 500 K at 1 bar. By applying Widom's particle insertion technique and Bennet's overlapping distribution method, we are able to determine the temperature dependent solvation free energies of those selected light gases in simulated imidazolium based ionic liquids with high statistical accuracy. Our simulations demonstrate that the magnitude of the solvation free energy of a gas molecule at a chosen reference temperature and that of its temperature-derivatives are intimately related to one another. We conclude that this "universal" behavior is rooted in a solvation entropy-enthalpy compensation effect, which seems to be a defining feature of the solvation of small molecules in ionic liquids. The observations lead to simple analytical relations, determining the temperature dependence of the solubility data based on the absolute solubility at a certain reference temperature. By comparing our results with available experimental data from many sources, we can show that our approach is particularly helpful for providing reliable estimates for the solvation behavior of very light gases, such as hydrogen, where conflicting experimental data exist.