Mechanisms and factors affecting the removal of minocycline from aqueous solutions using graphene-modified resorcinol formaldehyde aerogels

Dual-Loaded Chitosan-Based Nanoparticles: A Novel approach for treating polymicrobial osteomyelitis

Developing innovative approaches to target osteomyelitis caused by polymicrobial infections remains a significant therapeutic challenge. In this study, monodispersed chitosan nanoparticles co-loaded with antibacterial (minocycline) and antifungal (voriconazole) agents were successfully prepared. Minocycline presented higher encapsulation efficiency as compared to voriconazole. Thermostability analysis suggested interactions between the co-loaded drugs within the dual-delivery system, potentially limiting voriconazole release. The dual-loaded chitosan nanoparticles exhibited significant in vitro anti-biofilm activity, achieving up to a 90% reduction in polymicrobial biofilms of S. aureus and C. albicans. Additionally, the nanoparticles showed cytocompatibility with a human osteoblast cell line. These findings highlight the potential of this dual-delivery chitosan-based nanoparticle system to address a critical gap in osteomyelitis treatment by targeting both bacterial and fungal pathogens.

Exploring the potential of beta-cyclodextrin-based MIL-101(Cr) for pharmaceutical removal from wastewater: A combined density functional theory and molecular simulations study

Pharmaceutical contaminants pose significant risks to ecosystems and human health, necessitating effective removal strategies. This research focuses on developing advanced adsorbents for removing pharmaceutical pollutants from the environment. Metal-organic frameworks (MOFs), specifically MIL-101(Cr) functionalized with biodegradable beta-cyclodextrin (β-CDex), were investigated as potential nanocomposite adsorbents for the removal of ketorolac (KTRK), naproxen (NPXN), and tramadol (TRML). The study employed molecular simulations and density functional theory (DFT) calculations to explore the interactions between the pollutants and adsorbents. Analyses of DFT results, including electrostatic potential, ionization energy, density of states, and molecular orbital analysis, provided insights into the reactivity of pollutants and adsorbents. Additionally, the structural properties of the adsorbents, such as fractional free volume, radius of gyration, and system energies, were thoroughly examined. Molecular dynamics (MD) and Monte Carlo (MC) simulations were used to evaluate the adsorption capacities of MIL-101(Cr) for the target pharmaceutical pollutants. The results demonstrated the superior adsorption performance of the nanocomposite adsorbent, particularly for KTRK, with an adsorption energy of -1934 kcal/mol, compared to the pristine MIL-101(Cr), which had an adsorption energy of -1916 kcal/mol. This enhanced adsorption is attributed to the optimal molecular fit, guest-host solid interactions, and the selective encapsulation capabilities of β-CDex. This research highlights the potential of MOF-based nanocomposites as effective and sustainable solutions for pharmaceutical pollution. By advancing the understanding of molecular interactions through simulations, this study contributes to developing innovative adsorbents for wastewater treatment and the protection of water resources.

Recent advances and applications of stimuli-responsive nanomaterials for water treatment: A comprehensive review

The development of stimuli-responsive nanomaterials holds immense promise for enhancing the efficiency and effectiveness of water treatment processes. These smart materials exhibit a remarkable ability to respond to specific external stimuli, such as light, pH, or magnetic fields, and trigger the controlled release of encapsulated pollutants. By precisely regulating the release kinetics, these nanomaterials can effectively target and eliminate contaminants without compromising the integrity of the water system. This review article provides a comprehensive overview of the advancements in light-activated and pH-sensitive nanomaterials for controlled pollutant release in water treatment. It delves into the fundamental principles underlying these materials' stimuli-responsive behaviour, exploring the design strategies and applications in various water treatment scenarios. In particular, the article indicates how integrating stimuli-responsive nanomaterials into existing water treatment technologies can significantly enhance their performance, leading to more sustainable and cost-effective solutions. The synergy between these advanced materials and traditional treatment methods could pave the way for innovative approaches to water purification, offering enhanced selectivity and efficiency. Furthermore, the review highlights the critical challenges and future directions in this rapidly evolving field, emphasizing the need for further research and development to fully realize the potential of these materials in addressing the pressing challenges of water purification.

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.

Molecular Simulation Studies of Pharmaceutical Pollutant Removal (Rosuvastatin and Simvastatin) Using Novel Modified-MOF Nanostructures (UIO-66, UIO-66/Chitosan, and UIO-66/Oxidized Chitosan)

The ubiquitous presence of pharmaceutical pollutants in the environment significantly threatens human health and aquatic ecosystems. Conventional wastewater treatment processes often fall short of effectively removing these emerging contaminants. Therefore, the development of high-performance adsorbents is crucial for environmental remediation. This research utilizes molecular simulation to explore the potential of novel modified metal-organic frameworks (MOFs) in pharmaceutical pollutant removal, paving the way for the design of efficient wastewater treatment strategies. Utilizing UIO-66, a robust MOF, as the base material, we developed UIO-66 functionalized with chitosan (CHI) and oxidized chitosan (OCHI). These modified MOFs' physical and chemical properties were first investigated through various characterization techniques. Subsequently, molecular dynamics simulation (MDS) and Monte Carlo simulation (MCS) were employed to elucidate the adsorption mechanisms of rosuvastatin (ROSU) and simvastatin (SIMV), two prevalent pharmaceutical pollutants, onto these nanostructures. MCS calculations demonstrated a significant enhancement in the adsorption energy by incorporating CHI and OCHI into UIO-66. This increased ROSU from -14,522 to -16,459 kcal/mol and SIMV from -17,652 to -21,207 kcal/mol. Moreover, MDS reveals ROSU rejection rates in neat UIO-66 to be at 40%, rising to 60 and 70% with CHI and OCHI. Accumulation rates increase from 4 Å in UIO-66 to 6 and 9 Å in UIO-CHI and UIO-OCHI. Concentration analysis shows SIMV rejection surges from 50 to 90%, with accumulation rates increasing from 6 to 11 Å with CHI and OCHI in UIO-66. Functionalizing UIO-66 with CHI and OCHI significantly enhanced the adsorption capacity and selectivity for ROSU and SIMV. Abundant hydroxyl and amino groups facilitated strong interactions, improving performance over that of unmodified UIO-66. Surface functionalization plays a vital role in customizing the MOFs for pharmaceutical pollutant removal. These insights guide next-gen adsorbent development, offering high efficiency and selectivity for wastewater treatment.

Machine learning-powered estimation of malachite green photocatalytic degradation with NML-BiFeO3 composites

This study explores the potential of photocatalytic degradation using novel NML-BiFeO3 (noble metal-incorporated bismuth ferrite) compounds for eliminating malachite green (MG) dye from wastewater. The effectiveness of various Gaussian process regression (GPR) models in predicting MG degradation is investigated. Four GPR models (Matern, Exponential, Squared Exponential, and Rational Quadratic) were employed to analyze a dataset of 1200 observations encompassing various experimental conditions. The models have considered ten input variables, including catalyst properties, solution characteristics, and operational parameters. The Exponential kernel-based GPR model achieved the best performance, with a near-perfect R2 value of 1.0, indicating exceptional accuracy in predicting MG degradation. Sensitivity analysis revealed process time as the most critical factor influencing MG degradation, followed by pore volume, catalyst loading, light intensity, catalyst type, pH, anion type, surface area, and humic acid concentration. This highlights the complex interplay between these factors in the degradation process. The reliability of the models was confirmed by outlier detection using William's plot, demonstrating a minimal number of outliers (66-71 data points depending on the model). This indicates the robustness of the data utilized for model development. This study suggests that NML-BiFeO3 composites hold promise for wastewater treatment and that GPR models, particularly Matern-GPR, offer a powerful tool for predicting MG degradation. Identifying fundamental catalyst properties can expedite the application of NML-BiFeO3, leading to optimized wastewater treatment processes. Overall, this study provides valuable insights into using NML-BiFeO3 compounds and machine learning for efficient MG removal from wastewater.