Prediction of Electrical Conductivity of Deep Eutectic Solvents Using COSMO-RS Sigma Profiles as Molecular Descriptors: A Quantitative Structure–Property Relationship Study

Mapping the Flammability Space of Sustainable Refrigerant Mixtures through an Artificial Neural Network Based on Molecular Descriptors

As the EU's mandates to phase out high-GWP refrigerants come into effect, the refrigeration industry is facing a new, unexpected reality: the introduction of more flammable yet environmentally compliant alternatives. This paradigm shift amplifies the need for a rapid, reliable screening methodology to assess the propensity for flammability of emerging fourth generation blends, offering a pragmatic alternative to laborious and time-intensive traditional experimental assessments. In this study, an artificial neural network (ANN) is meticulously constructed, evaluated, and validated to address this emerging challenge by predicting the normalized flammability index (NFI) for an extensive array of pure, binary, and ternary mixtures, reflecting a substantial diversity of compounds like CO2, hydrofluorocarbons (HFCs), hydrofluoroolefins (HFOs), six saturated hydrocarbons (sHCs), hydroolefins (HOs), and others. The optimal configuration ([61 (I) × 14 (HL1) × 24 (HL2) × 1 (O)]) demonstrated a profound fit to the data, with metrics like R 2 of 0.999, root-mean-square error (RMSE) of 0.1735, average absolute relative deviation (AARD)% of 0.8091, and SDav of ±0.0434. Exhaustive assessments were conducted to ensure the most efficient architecture without compromising the accuracy. Additionally, the analysis of the standardized residuals (SDR) and applicability domain (AD) exhibited fine control and consistency over the data points. External validation using quaternary mixtures further attested to the model's adaptability and predictive capability. The exploration into the relative contribution of descriptors led to the identification of 23 significant sigma descriptors derived from conductor-like screening model (COSMO), responsible for 90.98% of the total contribution, revealing potential avenues for model simplification without a substantial loss in predictive power. Moreover, the model successfully predicted the behavior of prospective industry-relevant mixtures, reinforcing its reliability and opening the door to experimentation with untested blends. The results collectively manifest the developed ANN's efficiency, robustness, and adaptability in modeling flammability, catering to the demands of industry standards, environmental concerns, and safety requirements.

Mimosa/quince seed mucilage-co-poly (methacrylate) hydrogels for controlled delivery of capecitabine: Simulation studies, characterization and toxicological evaluation

This research focused on developing pH-regulated intelligent networks using quince and mimosa seed mucilage through aqueous polymerization to sustain Capecitabine release while overcoming issues like short half-life, high dosing frequency, and low bioavailability. The resulting MSM/QSM-co-poly(MAA) hydrogel was evaluated for several parameters, including complex structure formation, stability, pH sensitivity, morphology, and elemental composition. FTIR, DSC, and TGA analyses confirmed the formation of a stable, complex cross-linked network, demonstrating excellent stability at elevated temperatures. SEM analysis revealed the hydrogels' smooth, fine texture with porous surfaces. PXRD and EDX results indicated the amorphous dispersion of Capecitabine within the network. The QMM9 formulation achieved an optimal Capecitabine loading of 87.17 %. The gel content of the developed formulations ranged from 65.21 % to 90.23 %. All formulations exhibited excellent swelling behavior, with ratios between 65.91 % and 91.93 % at alkaline pH. In vitro dissolution studies indicated that up to 98 % of Capecitabine was released after 24 h at pH 7.4, demonstrating the potential for sustained release. Furthermore, toxicological evaluation in healthy rabbits confirmed the system's safety, non-toxicity, and biocompatibility.

Rational design of deep eutectic solvents for the stabilization of dehydrogenases: an artificial neural network prediction approach

Stabilized enzymes are crucial for the industrial application of biocatalysis due to their enhanced operational stability, which leads to prolonged enzyme activity, cost-efficiency and consequently scalability of biocatalytic processes. Over the past decade, numerous studies have demonstrated that deep eutectic solvents (DES) are excellent enzyme stabilizers. However, the search for an optimal DES has primarily relied on trial-and-error methods, lacking systematic exploration of DES structure-activity relationships. Therefore, this study aims to rationally design DES to stabilize various dehydrogenases through extensive experimental screening, followed by the development of a straightforward and reliable mathematical model to predict the efficacy of DES in enzyme stabilization. A total of 28 DES were tested for their ability to stabilize three dehydrogenases at 30°C: (S)-alcohol dehydrogenase from Rhodococcus ruber (ADH-A), (R)-alcohol dehydrogenase from Lactobacillus kefir (Lk-ADH) and glucose dehydrogenase from Bacillus megaterium (GDH). The residual activity of these enzymes in the presence of DES was quantified using first-order kinetic models. The screening revealed that DES based on polyols serve as promising stabilizing environments for the three tested dehydrogenases, particularly for the enzymes Lk-ADH and GDH, which are intrinsically unstable in aqueous environments. In glycerol-based DES, increases in enzyme half-life of up to 175-fold for Lk-ADH and 60-fold for GDH were observed compared to reference buffers. Furthermore, to establish the relationship between the enzyme inactivation rate constants and DES descriptors generated by the Conductor-like Screening Model for Real Solvents, artificial neural network models were developed. The models for ADH-A and GDH showed high efficiency and reliability (R2 > 0.75) for in silico screening of the enzyme inactivation rate constants based on DES descriptors. In conclusion, these results highlight the significant potential of the integrated experimental and in silico approach for the rational design of DES tailored to stabilize enzymes.

Investigating the synergistic effects of apple vinegar and deep eutectic solvent as natural antibiotics: an experimental and COSMO-RS analysis

The present investigation examines the antimicrobial and antifungal characteristics of natural deep eutectic solvents (NADES) and apple vinegar in relation to a diverse array of bacterial and fungal strains. The clinical bacterial strains, including gram-negative and gram-positive, and the fungal pathogen Candida albicans, were subjected to solid medium diffusion to determine the inhibitory effects of these compounds. The results show that NADES has superior antimicrobial and antifungal action compared to apple vinegar. The observed inhibitory zones for apple vinegar and NADES varied in length from 16.5 to 24.2 and 16 to 52.5 mm, respectively. The results obtained indicate that no synergy is observed for this mixture (50% AV + 50% NADES). The range of values for bactericidal concentrations (MBC) and minimal inhibitory concentrations (MIC) was 0.0125 to 0.2 and 0.0125 to 0.4 µl/ml, respectively. Antibacterial and antifungal chemicals may be found in apple vinegar and NADES, with NADES offering environmentally safe substitutes for traditional antibiotics. Additional investigation is suggested to refine these compounds for a wide range of bacteria, which could create antimicrobial solutions that are both highly effective and specifically targeted, thereby offering extensive potential in medicine and the environment.

Assessing Viscosity in Sustainable Deep Eutectic Solvents and Cosolvent Mixtures: An Artificial Neural Network-Based Molecular Approach

Deep eutectic solvents (DESs) are gaining recognition as environmentally friendly solvent alternatives for diverse chemical processes. Yet, designing DESs tailored to specific applications is a resource-intensive task, which requires an accurate estimation of their physicochemical properties. Among them, viscosity is crucial, as it often dictates a DES's suitability as a solvent. In this study, an artificial neural network (ANN) is introduced to accurately describe the viscosity of DESs and their mixtures with cosolvents. The ANN utilizes molecular parameters derived from σ-profiles, computed using the conductor-like screening model for the real solvent segment activity coefficient (COSMO-SAC). The data set comprises 1891 experimental viscosity measurements for 48 DESs based on choline chloride, encompassing 279 different compositions, along with 1618 data points of DES mixtures with cosolvents as water, methanol, isopropanol, and dimethyl sulfoxide, covering a wide range of viscosity measurements from 0.3862 to 4722 mPa s. The optimal ANN structure for describing the logarithmic viscosity of DESs is configured as 9-19-16-1, achieving an overall average absolute relative deviation of 1.6031%. More importantly, the ANN shows a remarkable extrapolation capacity, as it is capable of predicting the viscosity of systems including solvents (ethanol) and hydrogen bond donors (2,3-butanediol) not considered in the training. The ANN model also demonstrates an extensive applicability domain, covering 94.17% of the entire database. These achievements represent a significant step forward in developing robust, open source, and highly accurate models for DESs using molecular descriptors.

COSMO-RS-based assessment of thermodynamic tools in predicting the polar and non-polar solvents efficiency in vegetable oil extraction

CONTEXT

One of the prevalent methods for evaluating separation performance is to predict the interactions of solvent and solute molecules. The infinite dilution activity coefficient, Gibbs free energy, and sigma profiles provided insights into the solubilization of a solute and revealed the intensity of the solution's molecular interactions. The effective thermodynamic tools (infinite dilution activity coefficient, Gibbs free energy) were evaluated for predicting the efficiency of 18 polar and non-polar organic solvents in rubber seed oil (RSO) extraction. An infinite dilution activity coefficient was computed to evaluate the solubility of the rubber seed oil model compound (linoleic acid) in the organic solvents. Gibbs free energy was evaluated to show the energy change associated with the molecules mixing process and forecast the miscibility of linoleic acid molecules in the solvents. Moreover, the study examined the sigma profiles and sigma surfaces of organic solvents and linoleic acid to acquire a deeper insight into their similarities and how they interact molecularly. According to the computational prediction and experimental verification, the thermodynamic properties of Gibbs free energy and activity coefficient proved to be highly effective tools for screening polar and moderately polar solvents, predicting the molecular interactions with solute. Whereas the sigma profile and sigma surface were found to be the most efficient tools for evaluating the efficacy of non-polar solvents. Solvents with moderate polarity, such as tetrahydrofuran and diethyl ether, as well as non-polar solvents like pentane, heptane, and n-hexane, proved to be effective and favorable for oil extraction, resulting in the highest oil yields of approximately 27.0%. Overall, the COSMO-RS method demonstrates its utility in estimating the solubility of RSO in organic solvents, enabling early identification of the most effective solvent.

METHODS

The initial geometry optimization of every component was conducted through density functional theory (DFT) using TmoleX software. A single-point density functional theory (DFT) computation using Becke Perdew 86 (BP86) and the Triple-Zeta Valence Potential (TZVPD) was performed to produce.cosmo files. COSMO-RS calculations were performed by applying the parameterization file BP_TZVPD_FINE_19.ctd using COSMOthermX software. The practical extraction of oil from plant seeds was performed using a sonicator bath to verify the accuracy of the COSMO-RS predictions.

Computer-Aided Molecular Design of Ionic Liquids as Advanced Process Media: A Review from Fundamentals to Applications

The unique physicochemical properties, flexible structural tunability, and giant chemical space of ionic liquids (ILs) provide them a great opportunity to match different target properties to work as advanced process media. The crux of the matter is how to efficiently and reliably tailor suitable ILs toward a specific application. In this regard, the computer-aided molecular design (CAMD) approach has been widely adapted to cover this family of high-profile chemicals, that is, to perform computer-aided IL design (CAILD). This review discusses the past developments that have contributed to the state-of-the-art of CAILD and provides a perspective about how future works could pursue the acceleration of the practical application of ILs. In a broad context of CAILD, key aspects related to the forward structure-property modeling and reverse molecular design of ILs are overviewed. For the former forward task, diverse IL molecular representations, modeling algorithms, as well as representative models on physical properties, thermodynamic properties, among others of ILs are introduced. For the latter reverse task, representative works formulating different molecular design scenarios are summarized. Beyond the substantial progress made, some future perspectives to move CAILD a step forward are finally provided.

Physicochemical Properties of Choline Chloride/Acetic Acid as a Deep Eutectic Solvent and Its Binary Solutions with DMSO at 298.15 to 353.15 K

Deep eutectic solvents (DESs) are considered to play an important role in green chemistry and other technological fields as an alternative to organic solvents. The present study reports measurements of density (ρ), speed of sound (u), dynamic viscosity (η), and electrical conductivity (κ) and investigates physicochemical properties of choline chloride/acetic acid (ChCl/AcA DES) and its binary mixtures with dimethyl sulfoxide (DMSO) over the entire composition and temperature (298.15-353.15 K) range. The density data are well fitted by a second-degree polynomial equation in T. DES/DMSO mixtures exhibit negative excess molar volume and isentropic compressibility deviation with a minimum in respective curves at x1 ≈ 0.15 (x1 is the mole fraction of DES in the mixture), which became deeper with increasing temperature. The ChCl/AcA DES and DMSO curves for excess partial molar volume cross each other at x1 ≈ 0.15, showing that the packing effect is dominant over specific interactions. A similar behavior is observed for excess molar viscosity, showing the minima at x1 ≈ 0.62, and substantiates volumetric results. The temperature dependence of viscosity and conductivity is well described by the Vogel-Fulcher-Tammann (VFT) equation.

Magnetic superhydrophobic melamine sponges for crude oil removal from water

This paper proposes the preparation of a new sorbent material based on melamine sponges (MS) with superhydrophobic, superoleophilic, and magnetic properties. This study involved impregnating the surface of commercially available MS with eco-friendly deep eutectic solvents (DES) and Fe3O4 nanoparticles. The DES selection was based on the screening of 105 eutectic mixtures using COSMO-RS modeling. Other parameters affecting the efficiency and selectivity of oil removal from water were optimized using the Box-Bhenken model. Menthol:Thymol (1:1)@Fe3O4-MS exhibited the highest sorption capacity for real crude oils (101.7-127.3 g/g). This new sponge demonstrated paramagnetic behavior (31.06 emu/g), superhydrophobicity (151°), superoleophobicity (0°), low density (15.6 mg/cm3), high porosity (99 %), and excellent mechanical stability. Furthermore, it allows multiple regeneration processes without losing its sorption capacity. Based on these benefits, Menthol:Thymol (1:1)@Fe3O4-MS shows promise as an efficient, cost-effective, and eco-friendly substitute for the existing sorbents.

Boosting Graph Neural Networks with Molecular Mechanics: A Case Study of Sigma Profile Prediction

Sigma profiles are quantum-chemistry-derived molecular descriptors that encode the polarity of molecules. They have shown great performance when used as a feature in machine learning applications. To accelerate the development of these models and the construction of large sigma profile databases, this work proposes a graph convolutional network (GCN) architecture to predict sigma profiles from molecule structures. To do so, the usage of molecular mechanics (force field atom types) is explored as a computationally inexpensive node-level featurization technique to encode the local and global chemical environments of atoms in molecules. The GCN models developed in this work accurately predict the sigma profiles of assorted organic and inorganic compounds. The best GCN model here reported, obtained using Merck molecular force field (MMFF) atom types, displayed training and testing set coefficients of determination of 0.98 and 0.96, respectively, which are superior to previous methodologies reported in the literature. This performance boost is shown to be due to both the usage of a convolutional architecture and node-level features based on force field atom types. Finally, to demonstrate their practical applicability, we used GCN-predicted sigma profiles as the input to machine learning models previously developed in the literature that predict boiling temperatures and aqueous solubilities. Using the predicted sigma profiles as input, these models were able to compute both physicochemical properties using significantly less computational resources and displayed only a slight decrease in performance when compared with sigma profiles obtained from quantum chemistry methods.

Deep Eutectic Solvents: An Eco-friendly Design for Drug Engineering

In the spirit of circular economy and sustainable chemistry, the use of environmentally friendly chemical products in pharmacy has become a hot topic. In recent years, organic solvents have been the subject of a great range of restriction policies due to their harmful effects on the environment and toxicity to human health. In parallel, deep eutectic solvents (DESs) have emerged as suitable greener solvents with beneficial environmental impacts and a rich palette of physicochemical advantages related to their low cost and biocompatibility. Additionally, DESs can enable remarkable solubilizing effect for several active pharmaceutical ingredients (APIs), thus forming therapeutic DESs (TheDESs). In this work, special attention is paid to DESs, presenting a precise definition, classification, methods of preparation, and characterization. A description of natural DESs (NaDESs), i. e., eutectic solvents present in natural sources, is also reported. Moreover, the present review article is the first one to detail the different approaches for judiciously selecting the constituents of DESs in order to minimize the number of experiments. The role of DESs in the biomedical and pharmaceutical sectors and their impact on the development of successful therapies are also discussed.

Untapped Potential of Deep Eutectic Solvents for the Synthesis of Bioinspired Inorganic-Organic Materials

Since the discovery of deep eutectic solvents (DESs) in 2003, significant progress has been made in the field, specifically advancing aspects of their preparation and physicochemical characterization. Their low-cost and unique tailored properties are reasons for their growing importance as a sustainable medium for the resource-efficient processing and synthesis of advanced materials. In this paper, the significance of these designer solvents and their beneficial features, in particular with respect to biomimetic materials chemistry, is discussed. Finally, this article explores the unrealized potential and advantageous aspects of DESs, focusing on the development of biomineralization-inspired hybrid materials. It is anticipated that this article can stimulate new concepts and advances providing a reference for breaking down the multidisciplinary borders in the field of bioinspired materials chemistry, especially at the nexus of computation and experiment, and to develop a rigorous materials-by-design paradigm.

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 (γ_i^∞) 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.

New molecular structure based models for estimation of the CO2 solubility in different choline chloride-based deep eutectic solvents (DESs)

In this study, CO₂ solubility in different choline chloride-based deep eutectic solvents (DESs) has been investigated using the Quantitative Structure-Property Relationship (QSPR). In this regard, the effect of different structures of the hydrogen bond donor (HBD) in choline chloride (ChCl) based deep eutectic solvents (DESs) has been studied in different temperatures and different molar ratios of ChCl as hydrogen bond acceptor (HBA) to HBD. 12 different datasets with 390 data on the CO₂ solubility were chosen from the literature for the model development. Eight predictive models, which contain the pressure and one structural descriptor, have been developed at the fixed temperature (i.e. 293, 303, 313, or 323 K), and the constant molar ratio of ChCl to HBD equal to 1:3 or 1:4. Moreover, two models were also introduced, which considered the effects of pressure, temperature, and HBD structures, simultaneously in the molar ratios equal to 1:3 or 1:4. Two additional datasets were used only for the further external validation of these two models at new temperatures, pressures, and HBD structures. It was identified that CO₂ solubility depends on the "EEig02d" descriptor of HBD. "EEig02d" is a molecular descriptor derived from the edge adjacency matrix of a molecule that is weighted by dipole moments. This descriptor is also related to the molar volume of the structure. The statistical evaluation of the proposed models for the unfixed and fixed temperature datasets confirmed the validity of the developed models.

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%.

COSMO Models for the Pharmaceutical Development of Parenteral Drug Formulations

The aqueous solubility of active pharmaceutical ingredients is one of the most important features to be considered during the development of parenteral formulations in the pharmaceutical industry. Computational modelling has become in the last years an integral part of pharmaceutical development. In this context, ab initio computational models, such as COnductor-like Screening MOdel (COSMO), have been proposed as promising tools for the prediction of results without the effective use of resources. Nevertheless, despite the clear evaluation of computational resources, some authors had not achieved satisfying results and new calculations and algorithms have been proposed over the years to improve the outcomes. In the development and production of aqueous parenteral formulations, the solubility of Active Pharmaceutical Ingredients (APIs) in an aqueous and biocompatible vehicle is a decisive step. This work aims to study the hypothesis that COSMO models could be useful in the development of new parenteral formulations, mainly aqueous ones.

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.

Molecular dynamics, MMGBSA, and docking studies of natural products conjugated to tumor-targeted peptide for targeting BRAF V600E and MERTK receptors

Recent studies have revealed that MERTK and BRAF V600E receptors have been found to be over-expressed in several types of cancers including melanoma, making these receptors targets for drug design. In this study, we have designed novel peptide conjugates with the natural products vanillic acid, thiazole-2-carboxylic acid, cinnamic acid, theanine, and protocatechuic acid. Each of these compounds was conjugated with the tumor targeting peptide sequence TAASGVRSMH, known to bind to NG2 and target tumor neovasculature. We examined their binding affinities and stability with MERTK and BRAF V600E receptors using molecular docking and molecular dynamics studies. Compared to the neat compounds, the peptide conjugates displayed higher binding affinity toward both receptors. In the case of MERTK, the most stable complexes were formed with di-theaninate-peptide, vanillate-peptide, and thiazole-2-amido peptide conjugates and binding occurred in the hinge region. Additionally, it was discovered that the peptide alone also had high binding ability and stability with the MERTK receptor. In the case of BRAF V600E, the peptide conjugates of protocatechuate, vanillate and thiazole-2-amido peptide conjugates showed the formation of the most stable complexes and binding occurred in the ATP binding cleft. Further analysis revealed that the number of hydrogen bonds and hydrophobic interactions played a critical role in enhanced stability of the complexes. Docking studies also revealed that binding affinities for NG2 were similar to MERTK and higher for BRAF V600E. MMGBSA studies of the trajectories revealed that the protocatechuate-peptide conjugate showed the highest binding energy with BRAF V600E while the peptide-TAASGVRSMH showed the highest binding energy with MERTK. ADME studies revealed that each of the compounds showed medium to high permeability toward MDCK cells and were not hERG blockers. Furthermore, the conjugates were not CYP inhibitors or substrates, but they were found to be Pgp substrates. Our results indicated that the protocatechuate-TAASGVRSMH, thiazole-2-amido-TAASGVRSMH, and vanillate-TAASGVRSMH conjugates may be furthered developed for in vitro and in vivo studies as novel tumor targeting compounds for tumor cells over-expressing BRAF V600E, while di-theaninate-amido-TAASGVRSMH and thiazole-2-amido-TAASGVRSMH conjugates may be developed for targeting MERTK receptors. These studies provide insight into the molecular interactions of natural product-peptide conjugates and their potential for binding to and targeting MERTK and BRAF V600E receptors in developing new therapeutics for targeting cancer.

Biosorption of zinc (II) from synthetic wastewater by using Inula Viscosa leaves as a low-cost biosorbent: Experimental and molecular modeling studies

The use of biosorption as a strategy for lowering the amount of pollution caused by heavy metals is particularly encouraging. In this investigation, a low-cost and efficient biosorbent, Inula Viscosa leaves were used to remove zinc ions (Zn²⁺) from synthetic wastewater. A Fourier transform infrared spectroscopy experiment, a scanning electron microscopy experiment, and an energy dispersive X-ray spectroscopy experiment were used to describe the support. Several different physicochemical factors, such as the beginning pH value, contact duration, initial zinc concentration, biosorbent dose, and temperature, were investigated in this study. When the Langmuir, Freundlich, Temkin, Toth, and Redlich-Peterson models were used to match the data from the Inula Viscosa leaves biosorption isotherms, it was found that the biosorption isotherms correspond most closely with the Langmuir isotherm. On the other hand, the kinetic biosorption process was investigated using pseudo-first-order, pseudo-second-order (PS2), and Elovich models. The PS2 model was the one that provided the most accurate description of the biosorption kinetics. The thermodynamics process shows the spontaneous and endothermic character of Zn²⁺ sorption on Inula Viscosa leaves, which also entails the participation of physical interactions. In addition, the atom-in-molecule analysis, density functional theory, and the conductor like screening model for real solvents, were used to investigate the relationship that exists between quantum calculations and experimental outcomes.

Effective removal of cationic dye on activated carbon made from cactus fruit peels: a combined experimental and theoretical study

This article investigates experimentally and theoretically the adsorption of Basic Red 46 cationic dye (BR46) using activated carbon generated from cactus fruit peels (ACCFP). The prepared adsorbent was characterized by different analytical tools showing a good surface for the uptake of pollutants. A maximum batch adsorption capacity of 806.38 mg g^-1 was achieved at optimal conditions. The Freundlich model best represented the equilibrium data, although the pseudo-second-order kinetic model best described the adsorption kinetics. The thermodynamic studies demonstrated that the adsorption process was spontaneous (ΔG° < 0) and endothermic (ΔH° = 32.512 kJ mol^-1). DFT descriptors were combined with COSMO-RS and AIM theory to provide a complete picture of the adsorbate/adsorbent system and its molecular interactions. Last, the ACCFP was regenerable up to four times, emphasizing the idea of using it as an adsorbent to treat textile wastewaters.

Extraction of Phenolic Compound from Model Pyrolysis Oil Using Deep Eutectic Solvents: Computational Screening and Experimental Validation

Green Deep Eutectic Solvents (DESs) are considered here as an alternative to conventional organic solvents and ionic liquids (IL) for the extraction of phenolic compounds from pyrolysis oil. Although ionic liquids have shown a promising future in extraction processes, DESs possess not only most of their remarkable physico-chemical properties, but are also cheaper, easier to prepare and non-toxic, increasing the infatuation with these new moieties to the detriment of ionic liquids. In this work, phenol was selected as a representative of phenolic compounds, and toluene and heptane were used to model the pyrolysis oil. COSMO-RS was used to investigate the interaction between the considered Dess, phenol, n-heptane, and toluene. Two DESs (one ammonium and one phosphonium based) were subsequently used for experimental liquid–liquid extraction. A ternary liquid–liquid equilibrium (LLE) experiment was conducted with different feed concentrations of phenol ranging from 5 to 25 wt% in model oil at 25 °C and at atmospheric pressure. Although both DESs were able to extract phenol from model pyrolysis oil with high distribution ratios, the results showed that ammonium-based DES was more efficient than the phosphonium-based one. The composition of phenol in the raffinate and extract phases was determined using gas chromatography. A similar trend was observed by the COSMO-RS screening for the two DESs.

Searching for Sustainable Refrigerants by Bridging Molecular Modeling with Machine Learning

We present here a novel integrated approach employing machine learning algorithms for predicting thermophysical properties of fluids. The approach allows obtaining molecular parameters to be used in the polar soft-statistical associating fluid theory (SAFT) equation of state using molecular descriptors obtained from the conductor-like screening model for real solvents (COSMO-RS). The procedure is used for modeling 18 refrigerants including hydrofluorocarbons, hydrofluoroolefins, and hydrochlorofluoroolefins. The training dataset included six inputs obtained from COSMO-RS and five outputs from polar soft-SAFT parameters, with the accurate algorithm training ensured by its high statistical accuracy. The predicted molecular parameters were used in polar soft-SAFT for evaluating the thermophysical properties of the refrigerants such as density, vapor pressure, heat capacity, enthalpy of vaporization, and speed of sound. Predictions provided a good level of accuracy (AADs = 1.3–10.5%) compared to experimental data, and within a similar level of accuracy using parameters obtained from standard fitting procedures. Moreover, the predicted parameters provided a comparable level of predictive accuracy to parameters obtained from standard procedure when extended to modeling selected binary mixtures. The proposed approach enables bridging the gap in the data of thermodynamic properties of low global warming potential refrigerants, which hinders their technical evaluation and hence their final application.

Strength Enhancement of Regenerated Cellulose Fibers by Adjustment of Hydrogen Bond Distribution in Ionic Liquid

To improve the physical strength of regenerated cellulose fibers, cellulose dissolution was analyzed with a conductor-like screening model for real solvents in which 1-allyl-3-methylimidazolium chloride (AMIMCl) worked only as a hydrogen bond acceptor while dissolving the cellulose. This process could be promoted by the addition of urea, glycerol, and choline chloride. The dissolution and regeneration of cellulose was achieved through dry-jet and wet-spinning. The results demonstrated that the addition of hydrogen bond donors and acceptors either on their own or in combination can enhance the tensile strength, but their effects on the crystallinity of the regenerated fibers were quite limited. Compared with the regenerated fibers without any additives, the tensile strength was improved from 54.43 MPa to 139.62 MPa after introducing the choline chloride and glycerol, while related the crystallinity was only changed from 60.06% to 62.97%. By contrast, a more compact structure and fewer pores on the fiber surface were identified in samples with additives along with well-preserved cellulose frameworks. Besides, it should be noted that an optimization in the overall thermal stability was obtained in samples with additives. The significant effect of regenerated cellulose with the addition of glycerol was attributed to the reduction of cellulose damage by slowing down the dissolution and cross-linking in the cellulose viscose. The enhancement of the physical strength of regenerated cellulose fiber can be realized by the appropriate adjustment of the hydrogen bond distribution in the ionic liquid system with additives.

Molecular dynamics simulations, docking and MMGBSA studies of newly designed peptide-conjugated glucosyloxy stilbene derivatives with tumor cell receptors

In this work, for the first time, we designed derivatives of beta-D-glucosyloxy-3-hydroxy-trans-stiblene-2-carboxylic acid (GHS), by conjugating GHS with tumor targeting peptides RPARPAR and GGKRPAR to target over-expressed receptors in tumor cells. The sequences RPARPAR and GGKRPAR are known to target the neuropilin1 (NRP1) receptor due to the C-terminal Arg domain; however, their effectiveness has never been examined with other commonly over-expressed receptors in tumor cells, particularly of chronic lymphocytic leukemia that include integrin α1β1 and CD22. By conjugating these peptides with GHS, which is known for its inherent anti-cancer properties, the goal is to further enhance tumor cell targeting by developing compounds that can target multiple receptors. The physicochemical properties of the conjugates and individual peptides were analyzed using Turbomole and COSMOthermX20 in order to determine their hydrogen bond accepting and donating capabilities. The web server POCASA was used in order to determine the surface cavities and binding pockets of the three receptors. To explore the binding affinities, we conducted molecular docking studies with the peptides and the conjugates with each of the receptors. After molecular docking, the complexes were analyzed using Protein-Ligand Interaction Profiler to determine the types of interactions involved. Molecular dynamics simulation studies were conducted to explore the stability of the receptor-ligand complexes. Our results indicated that in most cases the conjugates showed higher binding and stability with the receptors. Additionally, highly stable complexes of conjugates were obtained with CD22, NRP1 and in most cases with the integrin α1β1 receptor as well. The binding energies were calculated for each of the receptor ligand complexes through trajectory analysis using MMGBSA studies. SwissADME studies revealed that the compounds showed low GI absorption and were not found to be CYP inhibitors and had bioavailability score that would allow them to be considered as potential drug candidates. Overall, our results for the first time show that the designed conjugates can target multiple over-expressed receptors in tumor cells and may be potentially developed as future therapeutics for targeting tumor cells.

Green Extraction of Volatile Fatty Acids from Fermented Wastewater Using Hydrophobic Deep Eutectic Solvents

Volatile fatty acids (VFAs) are carboxylic acids mainly produced via the fermentation of organic waste streams. Being industrial platform chemicals, sustainable, green and economical recovery of VFAs is necessary. Herein, hydrophobic deep eutectic solvents (HDES), “a new generation of water-immiscible designer solvents”, were assessed for the recovery of VFAs via liquid–liquid extraction. The eutectic mixture of menthol-lauric acid exhibited the highest stability and hydrophobicity. The binary solubility of the pairs {VFA in water} and {VFA in HDES}—and the saturation of the HDES with water were measured. Furthermore, the influences of key parameters on the extraction efficiency were investigated. On multi-stage extraction, an efficiency of 88% was achieved in 4 stages, and the HDES was successfully regenerated using vacuum evaporation. The HDES performance was also compared to other reported relevant solvents. It was concluded that menthol-lauric acid HDES is a promising candidate for the green extraction of VFAs from fermented wastewater.