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.

Exploring the Potential of a Highly Scalable Metal-Organic Framework CALF-20 for Selective Gas Adsorption at Low Pressure

In this study, the ability of the highly scalable metal-organic framework (MOF) CALF-20 to adsorb polar and non-polar gases at low pressure was investigated using grand canonical Monte Carlo (GCMC) and molecular dynamics (MD) simulations. The results from the simulated adsorption isotherms revealed that the highest loading was achieved for SO₂ and Cl₂, while the lowest loading was found for F₂ molecules. The analysis of interaction energies indicated that SO₂ molecules were able to form the strongest adsorbent-adsorbate interactions and had a tight molecular packing due to their polarity and angular structure. Additionally, Cl₂ gas was found to be highly adsorbed due to its large van der Waals surface and strong chemical affinity in CALF-20 pores. MD simulations showed that SO₂ and Cl₂ had the lowest mobility inside CALF-20 pores. The values of the Henry coefficient and isosteric heat of adsorption confirmed that CALF-20 could selectively adsorb SO₂ and Cl₂. Based on the results, it was concluded that CALF-20 is a suitable adsorbent for SO₂ and Cl₂ but not for F₂. This research emphasizes the importance of molecular size, geometry, and polarity in determining the suitability of a porous material as an adsorbent for specific adsorbates.

Investigation of the thermal properties of phase change materials encapsulated in capped carbon nanotubes using molecular dynamics simulations

Due to the high demand for clean, economic, and recyclable energy, phase change materials (PCMs) have received significant attention in recent years. To improve the performance of PCMs, they are confined in micro- and nano-capsules composed of organic or inorganic materials. In this study, encapsulated phase change material (EPCM) systems were constructed with paraffin molecules as the core material and capped carbon nanotubes (CNTs) as the shell. We investigated the effects of different parameters including CNT diameter, length, and chirality and the length of the alkane molecule chain. We also investigated metal nanocluster-enhanced PCM systems via the addition of Cu, Ag, and Al clusters to the EPCM systems. Different thermodynamic, dynamic, and structural properties including configurational energy, melting range, mean square displacement, self-diffusion coefficient, radial distribution function (RDF), and average end-to-end distance of the confined molecules were examined. We also investigated the effect of metal doping in CNT on the different properties of the confined PCM. The results indicated that a longer CNT has a lower melting point than the normal CNT system. It was also observed that the bigger (30,0) CNT, (14,14) armchair CNT, and icosane systems have higher melting ranges than the normal (25,0) system. The metal cluster systems also have a lower starting melting point than the normal system. Furthermore, it was found that the Al cluster system has the lowest starting melting point among the studied systems.

Nanocluster enhanced PCM and metal doped CNT PCM systems have lower melting points than the normal system.

Quantum-Chemical ab initio Study of Side Chain pKa of Linear and Cyclic Lysine Dipeptides

Cyclic dipeptides show interesting biological activities such as antitumor, antiviral and so on. In biological systems, the bioavailability of drugs is determined by several parameters such as pKa values. In this study, we used DFT and thermodynamics cycle to determine pKa value of side chain of lysine in linear and cyclic dipeptides. All considered dipeptides were optimized using B3LYP and RMSD tool was used to compare the optimized structures. The calculated pKa values were compared with the available experimental data. Our results show that pKa of side chain of lysine increases for cyclic dipeptides compared to the linear ones. To justify the reason of increasing of pKa of cyclic dipeptides, we used NBO and AIM analyses. The analyses showed that a hydrogen bond in cyclic lysine dipeptides is responsible for increasing of pKa.

Highly efficient solar photocatalytic degradation of a textile dye by TiO2/graphene quantum dots nanocomposite

Herein, two sunlight responsive photocatalysts including TiO₂ nanoparticles (NPs) and TiO₂/graphene quantum dots (GQDs) nanocomposite for degrading a textile dye, Reactive Black 5 (RB5), were prepared. The results showed that 100% of 50 ppm RB5 could be degraded by TiO₂ NPs and TiO₂/GQDs within 60 and 30 min sunlight irradiation, respectively. Hence, much better photocatalytic activity in degradation of RB5 was achieved by TiO₂/GQDs under sunlight irradiation compared with pure TiO₂ NPs due to its lower band gap (2.13 eV) and electron/hole recombination rate. The photocatalytic degradation mechanism of RB5 by TiO₂ NPs was elucidated by adding some scavengers to the solution. The main reactive species contributing to RB5 degradation were surface hydroxyl radicals. The first-order solar degradation rate constant of RB5 for TiO₂/GQDs is greater than that of TiO₂ NPs under sunlight illumination.

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 ([C_nmim][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.

[Binding of 1-substituted carbazolyl-3,4-dihydro-β-carbolines with DNA: Molecular dynamics simulation and MM-GBSA analysis]

Molecular Mechanics-Generalized Born-Solvent Accessibility free energy calculations were used to analyse DNA binding affinity of 1-substituted carbazolyl-3,4-dihydro-β-carboline molecules. In this study, DNA structure with sequence of d(CGATCG)2 was used for simulations. 15 ns molecular dynamics simulations of the studied complexes were performed. The calculated free energy was compared with experimental antitumor activity (IC(50)). The predicted free energies decreased with the increase of IC(50) values. It was shown that molecules 1-6 bind to DNA via intercalation mode, while molecules 7-9 bind through groove binding mode. Also, it was found that the vdW energy term (ΔE(vdW)) and the non-polar desolvation energy (ΔG(SA)) are the favorable terms for binding energy, whereas net electrostatic energies (ΔE(ele) + ΔG(GB)) and conformational entropy energy (TΔS) are unfavorable ones.

Ultrasound-assisted green synthesis of nanocrystalline ZnO in the ionic liquid [hmim][NTf2]

For the first time, zinc oxide nanoparticles have been synthesized by the sonochemical method in an ionic liquid, 1-hexyl-3-methylimidazolium bis (trifluoromethylsulfonyl) imide, liquid [hmim][NTf(2)] as a solvent. The morphology and structure of ZnO nanoparticles have been characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), and transmission electron microscopy (TEM). A possible mechanism is proposed to explain the formation of ZnO nanostructures.