Exploring the Efficiency of Algerian Kaolinite Clay in the Adsorption of Cr(III) from Aqueous Solutions: Experimental and Computational Insights.

The current study comprehensively investigates the adsorption behavior of chromium (Cr(III)) in wastewater using Algerian kaolinite clay. The structural and textural properties of the kaolinite clay are extensively characterized through a range of analytical methods, including XRD, FTIR, SEM-EDS, XPS, laser granulometry, N2 adsorption isotherm, and TGA-DTA. The point of zero charge and zeta potential are also assessed. Chromium adsorption reached equilibrium within five minutes, achieving a maximum removal rate of 99% at pH 5. Adsorption equilibrium is modeled using the Langmuir, Freundlich, Temkin, Elovich, and Dubinin-Radushkevitch equations, with the Langmuir isotherm accurately describing the adsorption process and yielding a maximum adsorption capacity of 8.422 mg/g for Cr(III). Thermodynamic parameters suggest the spontaneous and endothermic nature of Cr(III) sorption, with an activation energy of 26.665 kJ/mol, indicating the importance of diffusion in the sorption process. Furthermore, advanced DFT computations, including COSMO-RS, molecular orbitals, IGM, RDG, and QTAIM analyses, are conducted to elucidate the nature of adsorption, revealing strong binding interactions between Cr(III) ions and the kaolinite surface. The integration of theoretical and experimental data not only enhances the understanding of Cr(III) removal using kaolinite but also demonstrates the effectiveness of this clay adsorbent for wastewater treatment. Furthermore, this study highlights the synergistic application of empirical research and computational modeling in elucidating complex adsorption processes.

3,4-Dimethoxy phenyl thiosemicarbazone as an effective corrosion inhibitor of copper under acidic solution: comprehensive experimental, characterization and theoretical investigations.

This study investigates the corrosion inhibition potential of 3,4-dimethoxy phenyl thiosemicarbazone (DMPTS) for copper in 1 M hydrochloric acid (HCl) solutions, aiming to disclose the mechanism behind its protective action. Through an integrative methodology encompassing electrochemical analyses-such as weight loss measurements, potentiodynamic polarization, and electrochemical impedance spectroscopy (EIS)-we quantitatively evaluate the corrosion protection efficacy of DMPTS. It was determined that the optimal concentration of DMPTS markedly boosts the corrosion resistance of copper, achieving an impressive inhibition efficiency of up to 89% at 400 ppm. The formation of a protective layer on the copper surface, a critical aspect of DMPTS's inhibitory action, was characterized using Scanning Electron Microscopy (SEM) and Atomic Force Microscopy (AFM). These techniques provided empirical evidence of surface morphology modifications and roughness changes, affirming the formation of a protective barrier against corrosion. A significant advancement in our study was the application of Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) spectroscopy, which identified chemical adsorption as the definitive mechanism of corrosion inhibition by DMPTS. The ATR-FTIR results explicitly demonstrated the specific interactions between DMPTS molecules and the copper surface, indicative of a robust protective adsorbed layer formation. This mechanistic insight, crucial to understanding the inhibitory process, aligns with the protective efficacy observed in electrochemical and surface analyses. Theoretical support, provided by the Quantum Theory of Atoms in Molecules (QTAIM) and quantum chemical computations, further validated the strong molecular interaction between DMPTS and copper, corroborating the experimental findings. Collectively, this research not only confirms the superior corrosion inhibition performance of DMPTS in an acidic setting but also elucidates the chemical adsorption mechanism as the foundation of its action, offering valuable insights for the development of effective corrosion inhibitors in industrial applications.

Development and evaluation of a pH-responsive Mimosa pudica seed mucilage/ß- cyclodextrin-co-poly(methacrylate) hydrogel for controlled drug delivery: In vitro and in vivo assessment.

In this comprehensive investigation, a novel pH-responsive hydrogel system comprising mimosa seed mucilage (MSM), ß-cyclodextrin (ß-CD), and methacrylic acid (MAA) was developed via free radical polymerization technique to promote controlled drug delivery. The hydrogel synthesis involved strategic variations in polymer, monomer, and crosslinker content in fine-tuning its drug-release properties. The resultant hydrogel exhibited remarkable pH sensitivity, selectively liberating the model drug (Capecitabine = CAP) under basic conditions while significantly reducing release in an acidic environment. Morphological, thermal, and structural analyses proved that CAP has a porous texture, high stability, and an amorphous nature. In vitro drug release experiments showcased a sustained and controlled release profile. Optimum release (85.33 %) results were recorded over 24 h at pH 7.4 in the case of MMB9. Pharmacokinetic evaluation in healthy male rabbits confirmed bioavailability enhancement and sustained release capabilities. Furthermore, rigorous toxicity evaluations and histopathological analyses ensured the safety and biocompatibility of the hydrogel. This pH-triggered drug delivery system can be a promising carrier system for drugs involving frequent administrations.

Comprehensive Investigation of Cu2+ Adsorption from Wastewater Using Olive-Waste-Derived Adsorbents: Experimental and Molecular Insights.

This study investigates the efficacy of adsorbents from locally sourced olive waste-encompassing olive skins, leaves, and pits, recovered from the initial centrifugation of olives (OWP)-and a composite with sodium alginate (OWPSA) for the removal of Cu2+ ions from synthetic wastewater. Experimental analyses conducted at room temperature, with an initial Cu2+ concentration of 50 mg/L and a solid/liquid ratio of 1 g/L, showed that the removal efficiencies were approximately 79.54% and 94.54% for OWP and OWPSA, respectively, highlighting the positive impact of alginate on adsorption capacity. Utilizing statistical physics isotherm models, particularly the single-layer model coupled to real gas (SLMRG), allowed us to robustly fit the experimental data, providing insights into the adsorption mechanisms. Thermodynamic parameters affirmed the spontaneity and endothermic nature of the processes. Adsorption kinetics were interpreted effectively using the pseudo-second-order (PSO) model. Molecular modeling investigations, including the conductor-like screening model for real solvents (COSMO-RS), density functional theory (DFT), and atom-in-molecule (AIM) analysis, unveiled intricate molecular interactions among the adsorbent components-cellulose, hemicellulose, lignin, and alginate-and the pollutant Cu2+, confirming their physically interactive nature. These findings emphasize the synergistic application of experimental and theoretical approaches, providing a comprehensive understanding of copper adsorption dynamics at the molecular level. This methodology holds promise for unraveling intricate processes across various adsorbent materials in wastewater treatment applications.

Chitosan-based intelligent polymeric networks for site-specific colon medication delivery: A comprehensive study on controlled release of diloxanide furoate and network formation dynamics.

Oral medications are prone to gastric degradation and enzymatic inactivation, diminishing their efficacy. This study investigates a solution by developing intelligent polymeric networks, incorporating chitosan, methacrylic acid, N, N, methylene bisacrylamide, and montmorillonite clay, to enable the controlled release of Diloxanide Furoate (DF), an anti-protozoal drug. Employing a swelling-assisted diffusion technique, drug loading percentages varied from 63.96 % to 76.82 % among different formulations. Increased chitosan and methacrylic acid content enhanced drug loading, while N, N, methylene bisacrylamide and montmorillonite clay demonstrated an inverse relationship affecting diffusion and swelling. Equilibrium swelling studies unveiled formulation-dependent behaviors, with chitosan reducing swelling and methacrylic acid promoting it. Higher N, N, methylene bisacrylamide concentrations decreased swelling, indicating a denser cross-linked structure, while montmorillonite clay reduced hydrophilicity and swelling capacity. Further analyses confirmed successful gel formation, particularly in formulations with higher chitosan, methacrylic acid, and N, N, methylene bisacrylamide content, while montmorillonite clay limited gel fraction due to restricted polymer chain mobility. Techniques such as Fourier transform infrared spectroscopy, Differential scanning calorimetry, and thermal gravimetric analyses supported network development, enhancing thermal stability and cross-linking density. This research underscores the flexibility of polymeric networks for precise drug delivery, offering potential advancements in targeted therapies for various medical conditions.

Quince seed mucilage/ß-cyclodextrin/Mmt-Na+-co-poly (methacrylate) based pH-sensitive polymeric carriers for controlled delivery of Capecitabine.

In current work, quince seed mucilage and ß-Cyclodextrin based pH regulated hydrogels were developed using aqueous free radical polymerization to sustain Capecitabine release patterns and to overcome its drawbacks, such as high dose frequency, short half-life, and low bioavailability. Developed networks were subjected to thermal analysis, Fourier transforms infrared spectroscopy, powder x-ray diffraction, elemental analysis, scanning electron microscopy, equilibrium swelling, and in-vitro release investigations to assess the network system's stability, complexation, morphology, and pH responsiveness. Thermally stable pH-responsive cross-linked networks were formed. Nanocomposite hydrogels were prepared by incorporating Capecitabine-containing clay into the swollen hydrogels. All the formulations exhibited equilibrium swelling ranging from 67.98 % to 92.98 % at pH 7.4. Optimum Capecitabine loading (88.17 %) was noted in the case of hydrogels, while it was 74.27 % in nanocomposite hydrogels. Excellent gel content (65.88 %-93.56 %) was noticed among developed formulations. Elemental analysis ensured the successful incorporation of Capecitabine. Nanocomposite hydrogels released 80.02 % longer than hydrogels after 30 h. NC hydrogels had higher t1/2 (10.57 h), AUC (121.52 µg.h/ml), and MRT (18.95 h) than hydrogels in oral pharmacokinetics. These findings imply that the pH-responsive carrier system may improve Capecitabine efficacy and reduce dosing frequency in cancer therapy. Toxicity profiling proved the system's safety, non-toxicity, and biocompatibility.

Molecular Guide for Selecting Green Deep Eutectic Solvents with High Monosaccharide Solubility for Food Applications.

Monosaccharides play a vital role in the human diet due to their interesting biological activity and functional properties. Conventionally, sugars are extracted using volatile organic solvents (VOCs). Deep eutectic solvents (DESs) have recently emerged as a new green alternative to VOCs. Nonetheless, the selection criterion of an appropriate DES for a specific application is a very difficult task due to the designer nature of these solvents and the theoretically infinite number of combinations of their constituents and compositions. This paper presents a framework for screening a large number of DES constituents for monosaccharide extraction application using COSMO-RS. The framework employs the activity coefficients at infinite dilution (γi∞) as a measure of glucose and fructose solubility. Moreover, the toxicity analysis of the constituents is considered to ensure that selected constituents are safe to work with. Finally, the obtained viscosity predictions were used to select DESs that are not transport-limited. To provide more insights into which functional groups are responsible for more effective monosaccharide extraction, a structure-solubility analysis was carried out. Based on an analysis of 212 DES constituents, the top-performing hydrogen bond acceptors were found to be carnitine, betaine, and choline chloride, while the top-performing hydrogen bond donors were oxalic acid, ethanolamine, and citric acid. A research initiative was presented in this paper to develop robust computational frameworks for selecting optimal DESs for a given application to develop an effective DES design strategy that can aid in the development of novel processes using DESs.

Critical Properties of Ternary Deep Eutectic Solvents Using Group Contribution with Extended Lee-Kesler Mixing Rules.

One of the most commonly used molecular inputs for ionic liquids and deep eutectic solvents (DESs) in the literature are the critical properties and acentric factors, which can be easily determined using the modified Lydersen-Joback-Reid (LJR) method with Lee-Kesler mixing rules. However, the method used in the literature is generally applicable only to binary mixtures of DESs. Nevertheless, ternary DESs are considered to be more interesting and may provide further tailorability for developing task-specific DESs for particular applications. Therefore, in this work, a new framework for estimating the critical properties and the acentric factor of ternary DESs based on their molecular structures is presented by adjusting the framework reported in the literature with an extended version of the Lee-Kesler mixing rules. The presented framework was applied to a data set consisting of 87 ternary DESs with 334 distinct compositions. For validation, the estimated critical properties and acentric factors were used to predict the densities of the ternary DESs. The results showed excellent agreement between the experimental and calculated data, with an average absolute relative deviation (AARD) of 5.203% for ternary DESs and 5.712% for 260 binary DESs (573 compositions). The developed methodology was incorporated into a user-friendly Excel worksheet for computing the critical properties and acentric factors of any ternary or binary DES, which is provided in the Supporting Information. This work promotes the creation of robust, accessible, and user-friendly models capable of predicting the properties of new ternary DESs based on critical properties, thus saving time and resources.

The curious case of polyphenols as green corrosion inhibitors: a review on their extraction, design, and applications.

Over the past century, a substantial amount of research focused on developing corrosion inhibitors, with a special focus on green "plant-based" corrosion inhibitors. Among the various types of inhibitors, polyphenols emerged as a promising candidate due to their advantageous characteristics, which include being inexpensive, biodegradable, renewable, and, most importantly, safe for both the environment and humans. Their performance as sustainable corrosion inhibitors have encouraged many electrochemical experiments as well as theoretical, mechanistic, and computational studies, with many papers reporting inhibition efficiencies of over 85%. In this review, the majority of literature contributions on the inhibition of various types of polyphenols, their natural extraction techniques, and their applications as "greener" corrosion inhibitors for metals are thoroughly described and discussed with a focus on their preparation, inhibition mechanism, and performance. Based on the reviewed literature, it can be concluded that polyphenols have a very promising potential to be used as both green and powerful corrosion inhibitors; therefore, further investigations, experimental or computational, are still required to realize higher inhibition efficiencies reaching up to ≈ 100%.

Predicting the Surface Tension of Deep Eutectic Solvents Using Artificial Neural Networks.

Studies on deep eutectic solvents (DESs), a new class of "green" solvents, are attracting increasing attention from researchers, as evidenced by the rapidly growing number of publications in the literature. One of the main advantages of DESs is that they are tailor-made solvents, and therefore, the number of potential DESs is extremely large. It is essential to have computational methods capable of predicting the physicochemical properties of DESs, which are needed in many industrial applications and research. Surface tension is one of the most important properties required in many applications. In this work, we report a relatively generalized artificial neural network (ANN) for predicting the surface tension of DESs. The database used can be considered comprehensive because it contains 1571 data points from 133 different DES mixtures in 520 compositions prepared from 18 ions and 63 hydrogen bond donors in a temperature range of 277-425 K. The ANN model uses molecular parameter inputs derived from the conductor-like screening model for real solvents (S σ-profiles). The training and testing results show that the best performing ANN architecture consisted of two hidden layers with 15 neurons each (9-15-15-1). The proposed ANN was excellent in predicting the surface tension of DESs, as R 2 values of 0.986 and 0.977 were obtained for training and testing, respectively, with an overall average absolute relative deviation of 2.20%. The proposed models represent an initiative to promote the development of robust models capable of predicting the properties of DESs based only on molecular parameters, leading to savings in investigation time and resources.