Prediction of the binary surface tension of mixtures containing ionic liquids using Support Vector Machine algorithms

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.

Machine learning (ML)-assisted surface tension and oscillation-induced elastic modulus studies of oxide-coated liquid metal (LM) alloys

Pendant drops of oxide-coated high-surface tension fluids frequently produce perturbed shapes that impede interfacial studies. Eutectic gallium indium or Galinstan are high-surface tension fluids coated with a ∼5 nm gallium oxide (Ga2O3) film and falls under this fluid classification, also known as liquid metals (LMs). The recent emergence of LM-based applications often cannot proceed without analyzing interfacial energetics in different environments. While numerous techniques are available in the literature for interfacial studies- pendant droplet-based analyses are the simplest. However, the perturbed shape of the pendant drops due to the presence of surface oxide has been ignored frequently as a source of error. Also, exploratory investigations of surface oxide leveraging oscillatory pendant droplets have remained untapped. We address both challenges and present two contributing novelties- (a) by utilizing the machine learning (ML) technique, we predict the approximate surface tension value of perturbed pendant droplets, (ii) by leveraging the oscillation-induced bubble tensiometry method, we study the dynamic elastic modulus of the oxide-coated LM droplets. We have created our dataset from LM's pendant drop shape parameters and trained different models for comparison. We have achieved >99% accuracy with all models and added versatility to work with other fluids. The best-performing model was leveraged further to predict the approximate values of the nonaxisymmetric LM droplets. Then, we analyzed LM's elastic and viscous moduli in air, harnessing oscillation-induced pendant droplets, which provides complementary opportunities for interfacial studies alternative to expensive rheometers. We believe it will enable more fundamental studies of the oxide layer on LM, leveraging both symmetric and perturbed droplets. Our study broadens the materials science horizon, where researchers from ML and artificial intelligence domains can work synergistically to solve more complex problems related to surface science, interfacial studies, and other studies relevant to LM-based systems.

Insights into the estimation of surface tensions of mixtures based on designable green materials using an ensemble learning scheme

Precise estimation of the physical properties of both ionic liquids (ILs) and their mixtures is crucial for engineers to successfully design new industrial processes. Among these properties, surface tension is especially important. It's not only necessary to have knowledge of the properties of pure ILs, but also of their mixtures to ensure optimal utilization in a variety of applications. In this regard, this study aimed to evaluate the effectiveness of Stochastic Gradient Boosting (SGB) tree in modeling surface tensions of binary mixtures of various ionic liquids (ILs) using a comprehensive dataset. The dataset comprised 4010 experimental data points from 48 different ILs and 20 non-IL components, covering a surface tension range of 0.0157-0.0727 N m-1 across a temperature range of 278.15-348.15 K. The study found that the estimated values were in good agreement with the reported experimental data, as evidenced by a high correlation coefficient (R) and a low Mean Relative Absolute Error of greater than 0.999 and less than 0.004, respectively. In addition, the results of the used SGB model were compared to the results of SVM, GA-SVM, GA-LSSVM, CSA-LSSVM, GMDH-PNN, three based ANNs, PSO-ANN, GA-ANN, ICA-ANN, TLBO-ANN, ANFIS, ANFIS-ACO, ANFIS-DE, ANFIS-GA, ANFIS-PSO, and MGGP models. In terms of the accuracy, the SGB model is better and provides significantly lower deviations compared to the other techniques. Also, an evaluation was conducted to determine the importance of each variable in predicting surface tension, which revealed that the most influential factor was the mole fraction of IL. In the end, William's plot was utilized to investigate the model's applicability range. As the majority of data points, i.e. 98.5% of the whole dataset, were well within the safety margin, it was concluded that the proposed model had a high applicability domain and its predictions were valid and reliable.

Structure-based modeling of critical micelle concentration (CMC) of anionic surfactants in brine using intelligent methods

Critical micelle concentration (CMC) is one of the main physico-chemical properties of surface-active agents, also known as surfactants, with diverse theoretical and industrial applications. It is influenced by basic parameters such as temperature, pH, salinity, and the chemical structure of surfactants. Most studies have only estimated CMC at fixed conditions based on the surfactant's chemical parameters. In the present study, we aimed to develop a set of novel and applicable models for estimating CMC of well-known anionic surfactants by considering both the molecular properties of surfactants and basic affecting factors such as salinity, pH, and temperature as modeling parameters. We employed the quantitative-structural property relationship technique to employ the molecular parameters of surfactant ions. We collected 488 CMC values from literature for 111 sodium-based anionic surfactants, including sulfate types, sulfonate, benzene sulfonate, sulfosuccinate, and polyoxyethylene sulfate. We computed 1410 optimized molecular descriptors for each surfactant using Dragon software to be utilized in the modelling processes. The enhanced replacement method was used for selecting the most effective descriptors for the CMC. A multivariate linear model and two non-linear models are the outputs of the present study. The non-linear models were produced using two robust machine learning approaches, stochastic gradient boosting (SGB) trees and genetic programming (GP). Statistical assessment showed highly applicable and acceptable accuracy of the newly developed models (RSGB2 = 0.999395 and RGP2 = 0.954946). The ultimate results showed the superiority and greater ability of the SGB method for making confident predictions.

Intelligent modeling of dye removal by aluminized activated carbon

Methylene blue (MB) is an important compound in textile and wood processing industries as well as in medical research for combating malaria parasites. Despite these versatilities, direct contact with human beings results in adverse health challenges, and contamination of water bodies affects aquatic biotas. Hence, it is important to treat MB-contaminated wastewaters before disposal into water bodies. Adsorption, which depends on some parameters, proves to be an easy, cheap, and efficient technique to remove pollutants in wastewater. However, investigating these parameters experimentally is a laborious, expensive, and time-consuming process whose efficiency is limited by the conditions imposed on the experiments. Herein, we developed polynomial multiple linear regression (MLR) and the three other machine learning models to study the interplay of five adsorption parameters (descriptors) and their effects on the removal of methylene blue from water using aluminized activated carbon (Al-AC). The optimized machine learning models, that is random forest (R = 0.9905), support vector regression (R = 0.9946), and multilayer perceptron (R = 0.9993), outperformed the best MLR model (R = 0.9845) by small margins. High statistical R and low error values are not enough to satisfactorily classify a model. Hence, the generalizability of the models was further determined under different experimental conditions, and the order of predictive accuracy of the models was established as ANN > SVR > RF > 2-degree MLR. Aluminum loading, adsorbent dosage, and initial adsorbate concentration are the most important factors affecting MB removal. The removal efficiency, which could reach 99.9% at optimum conditions, does not depend on the temperature thus eliminating the need to install temperature control apparatus for practical setup.

Machine Learning Prediction of Critical Temperature of Organic Refrigerants by Molecular Topology

In this work, molecular structures, combined with machine learning algorithms, were applied to predict the critical temperatures (Tc) of a group of organic refrigerants. Aiming at solving the problem that previous models cannot distinguish isomers, a topological index was introduced. The results indicate that the novel molecular descriptor ‘molecular fingerprint + topological index’ can effectively differentiate isomers. The average absolute average deviation between the predicted and experimental values is 3.99%, which proves a reasonable prediction ability of the present method. In addition, the performance of the proposed model was compared with that of other previously reported methods. The results show that the present model is superior to other approaches with respect to accuracy.

A review on machine learning algorithms for the ionic liquid chemical space

There are thousands of papers published every year investigating the properties and possible applications of ionic liquids. Industrial use of these exceptional fluids requires adequate understanding of their physical properties, in order to create the ionic liquid that will optimally suit the application. Computational property prediction arose from the urgent need to minimise the time and cost that would be required to experimentally test different combinations of ions. This review discusses the use of machine learning algorithms as property prediction tools for ionic liquids (either as standalone methods or in conjunction with molecular dynamics simulations), presents common problems of training datasets and proposes ways that could lead to more accurate and efficient models.

An Overview on the Toxicological Properties of Ionic Liquids toward Microorganisms

Ionic liquids (ILs), a class of materials with unique physicochemical properties, have been used extensively in the fields of chemical engineering, biotechnology, material sciences, pharmaceutics, and many others. Because ILs are very polar by nature, they can migrate into the environment with the possibility of inclusion in the food chain and bioaccumulation in living organisms. However, the chemical natures of ILs are not quintessentially biocompatible. Therefore, the practical uses of ILs must be preceded by suitable toxicological assessments. Among different methods, the use of microorganisms to evaluate IL toxicity provides many advantages including short generation time, rapid growth, and environmental and industrial relevance. This article reviews the recent research progress on the toxicological properties of ILs toward microorganisms and highlights the computational prediction of various toxicity models.

High throughput approach to investigating ternary solvents of aqueous non-stoichiometric protic ionic liquids

The use of ionic liquids (ILs) is limited for many applications due to their cost and/or viscosity. An efficient solution is to make mixtures of ILs with molecular solvents. However, it is well known that there are a large number of possible cation and anion combinations resulting in ILs, and this becomes a vast number when these are then combined with a molecular solvent. Therefore, we need structure-property relationships to design new IL-molecular solvent systems. In this work we have applied high throughput methods to investigate IL containing solutions to provide systematic data of a broad compositional space. We have principally focused on the surface tension, apparent pH and liquid nanostructure to identify potential self-assembly and protein stabilizing ability of solvent systems. Non-stoichiometric and aqueous IL-solvents were prepared in a high-throughput manner based on a deliberate experimental design approach such that 26 samples were prepared for each cation-anion-water combination. A selection of 8 protic ionic liquids (PILs) were used as starting materials, comprising ethanol-, ethyl-, butyl-, and pentylammonium cations combined with formate, acetate and nitrate anions. This resulted in a total of 208 different solvent systems. The measured solvent properties showed different trends in base-rich and acid-rich solvent combinations. Surface tensions of base-rich samples exhibited a relatively linear relationship with increasing excess amine, while acid-rich samples were more dominantly affected by the change in water content. Liquid nanostructure of acid-rich samples was retained upon water dilution, whereas a significant SAXS peak shift towards lower scattering angles was observed in the presence of excess amines, indicating larger nanosized aggregates were forming. The design of experiment approach used here is considered to be applicable to any multi-component solvent compositional space due to its suitability in using small data sets to cover large compositional spaces, and hence can be employed to decrease the time and sample quantities required.