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

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.

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.

Comprehensive Investigation of the Adsorption, Corrosion Inhibitory Properties, and Quantum Calculations for 2-(2,4,5-Trimethoxybenzylidene) Hydrazine Carbothioamide in Mitigating Corrosion of XC38 Carbon Steel under HCl Environment

This study investigates the inhibitory effects of 2-(2,4,5-trimethoxy benzylidene) hydrazine carbothioamide (TMBHCA) on the corrosion of carbon steel in a 1 M HCl solution across various concentrations. The assessment employs a comprehensive approach, combining gravimetric analysis, potentiodynamic polarization tests, and electrochemical impedance spectroscopy (EIS). Additionally, scanning electron microscopy (SEM) and quantum chemical calculations are employed to provide a thorough understanding of the corrosion inhibition mechanism. The influence of exposure time on mild steel corrosion is systematically examined. Results reveal a remarkable reduction in the corrosion rate of steel, with TMBHCA demonstrating its highest inhibition efficiency of 97.8% at 200 ppm. Potentiodynamic polarization studies characterize TMBHCA as a mixed-type inhibitor, while Nyquist plots illustrate increased charge transfer resistance and decreased double-layer capacitance with escalating TMBHCA concentrations. Consistency between weight loss measurements and electrochemical findings further validates the efficacy of TMBHCA as a corrosion inhibitor. SEM images substantiate and visually support the obtained results. An immersion test conducted at 25 °C over 28 days showcases a notable enhancement in TMBHCA efficiency (IE%) from 45.16% to 92.43% at 200 ppm as the immersion period progresses from 1 day to 28 days. This improvement is attributed to the augmented adsorption of inhibitor molecules on the steel surface over time. These comprehensive findings significantly contribute to our understanding of TMBHCA's corrosion inhibition behavior, emphasizing its potential as a highly efficient corrosion inhibitor for diverse industrial applications.

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.

Development and evaluation of a pH-responsive Mimosa pudica seed mucilage/β- 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), β-cyclodextrin (β-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.

Importance of the Electrostatic Correlations in Surface Tension of Hydrated Reline Deep Eutectic Solvent from Combined Experiments and Molecular Dynamics Simulations

In this study, the surface tension and the structure of hydrated reline are investigated by using diverse methods. Initially, the surface tension displays a nonlinear pattern as water content increases, decreasing until reaching 45 wt %, then gradually matching that of pure water. This fluctuation is associated with strong electrostatic correlations present in pure reline, which decrease as more water is added. Changes in surface tension reflect a shift from charge layering in pure reline to an increased interfacial hydrogen bonding as the water content rises. This shift causes the segregation of urea molecules into the bulk phase and a gradual anchoring of water molecules to the air-reline interface. An interesting observation is the antisurfactant effect, where heightened interfacial anchoring results in an unexpected increase in real contribution of surface tension. This, along with weakened electrostatic correlations beyond 45 wt % due to reinforced interfacial hydrogen bonding, contributes to the complex behavior of surface tension observed in this study.

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.

Unveiling CO2 capture in tailorable green neoteric solvents: An ensemble learning approach informed by quantum chemistry

In the relentless battle against the impending climate crisis, deep eutectic solvents (DESs) have emerged as beacons of hope in the realm of green chemistry, igniting a resurgence of scientific exploration. These versatile compounds hold the promise of revolutionizing carbon capture, effectively countering the rising tide of carbon dioxide (CO2) emissions responsible for global warming and climate instability. Their adaptability offers a tantalizing prospect, as they can be finely tailored for a multitude of applications, thereby encompassing the uncharted territory of potential DESs. Navigating this unexplored terrain underscores the vital need for predictive computational methods, which serve as our guiding compass in the expansive landscape of DESs. Thermodynamic modeling and solubility prognostications stand as our unwavering navigational aides on this treacherous odyssey. In this direction, the COSMO-RS model intertwined with the captivating Stochastic Gradient Boosting (SGB) algorithm. Together, they unveil the elusive truths pertaining to CO2 solubility in DESs, forging a path toward a sustainable future. Our quest is substantiated by two exhaustive datasets, a repository of knowledge encompassing 1973 and 2327 CO2 solubility data points spanning 132 and 150 distinct DESs respectively, encapsulating a spectrum of conditions. The SGB models, incorporating features derived from COSMO-RS, as well as accounting for pressure and temperature variables, furnishes predictions that harmonize seamlessly with experimental CO2 solubility values, boasting an impressive Average Absolute Relative Deviation (AARD) of a mere 0.85% and 2.30% respectively. When juxtaposed with literature-reported methodologies like different EoS, as well as Computational Solvation, and machine learning (ML) models, our SGB model emerges as the epitome of reliability, offering robust forecasts of CO2 solubility in DESs. It emerges as a potent tool for the design and selection of DESs for CO2 capture and utilization, heralding a sustainable and environmentally conscientious future in the battle against climate change.

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

In current work, quince seed mucilage and β-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.

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.

Multivariable System Prediction Based on TCN-LSTM Networks with Self-Attention Mechanism and LASSO Variable Selection

Intelligent prediction of key output variables that are difficult to measure online in complex systems has important research significance. In this paper, by using the least absolute shrinkage and selection operator (LASSO) algorithm to analyze the principal elements of input variables, a temporal convolutional network fused with long short-term memory (TCN-LSTM) network and self-attention mechanism (SAM) is designed to realize dynamic modeling of multivariate feature sequences. For complex processes with multiple input variables, each variable has different effects on the output, so it is necessary to use the LASSO algorithm to perform regression analysis on the input and output data for selecting the principal component variables and reducing the redundancy and computation burden of the network. The TCN network is used to extract the features of the input variables efficiently. The long-term memory performance of time series is enhanced by applying an LSTM network. The multihead SAM is used to optimize the network, and the role of key features is enhanced by assigning weights with probability to further improve the accuracy of sequence prediction. Finally, by comparison with the existing network model, the offline data generated by the high and low converters in the synthetic ammonia industry is used to predict the CO content so as to verify the superiority and applicability of the proposed network model.

Novel hybrid QSPR-GPR approach for modeling of carbon dioxide capture using deep eutectic solvents

In recent years, deep eutectic solvents (DESs) have garnered considerable attention for their potential in carbon capture and utilization processes. Predicting the carbon dioxide (CO2) solubility in DES is crucial for optimizing these solvent systems and advancing their application in sustainable technologies. In this study, we presented an evolving hybrid Quantitative Structure-Property Relationship and Gaussian Process Regression (QSPR-GPR) model that enables accurate predictions of CO2 solubility in various DESs. The QSPR-GPR model combined the strengths of both approaches, leveraging molecular descriptors and structural features of DES components to establish a robust and adaptable predictive framework. Through a systematic evolution process, we iteratively refined the model, enhancing its performance and generalization capacity. By incorporating experimental CO2 solubility data in varied DES compositions and temperatures, we trained the model to capture the intricate solubility behaviour precisely. The analytical capability of the evolving hybrid model was validated against an extensive dataset of experimental CO2 solubility values, demonstrating its superiority over individual QSPR and GPR models. The model achieves high accuracy, capturing the complex interactions between CO2 and DES components under varying thermodynamic conditions. The versatility of the evolving hybrid model was highlighted by its ability to accommodate new experimental data and adapt to different DES compositions and temperatures. The proposed QSPR-GPR model presented a powerful tool for predicting CO2 solubility in DES, providing valuable insights for designing and optimizing solvent systems in carbon capture technologies. The model's remarkable performance enhances our understanding of CO2 solubility mechanisms and contributes to sustainable solutions for mitigating greenhouse gas emissions. As research in DESs progresses, the evolving hybrid QSPR-GPR model offers a versatile and accurate means for predicting CO2 solubility, supporting advancements in carbon capture and utilization processes towards a greener and more sustainable future.

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.

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.