Odegova, Valeria

Odegova V., Lavrinenko A.K., Rakhmanov T., Sysuev G., Dmitrenko A., Vinogradov V.V.

The use of organic solvents in various industries poses significant environmental risks. Deep eutectic solvents (DESs) have emerged as a promising alternative due to their environmentally friendly properties. However, finding...

Zenkevich I.G., Byvsheva S.V., Gerasimov A.I., Gladnev S.V., Grigoriev M.V., Gubina N.V., Didenko E.A., Kazantsev A.S., Kalutskaia T.D., Katernuk E.V., Koblova A.A., Krutin D.V., Malkova K.P., Metliaeva S.A., Odegova V.S., et. al.

Uncertainties of the results of quantitative determinations in gas chromatography using the methods based on the absolute peak areas (including the external standard method) are rather “sensitive” to the reproducibility of injections. The effective way to compensate for such errors is to introduce the additional standards into the samples, followed by replacing the absolute peak areas by their ratios to peak areas of the standards. It is important to underline that any constituents of the samples can be used as additional standards, including the solvents. Solvents can be used for these purposes even if the heights of their peaks are restric­ted when the analytical signals exceed the amplifier limits. Using the relative peak areas does not require any extra sample processing besides the registration of peak areas for solvents. Comparing the commonly known and modified methods of external stan­dard demonstrates that using the relative peak areas instead of the absolute ones does not influence the overall precision of determinations (according to the criterion “intro­duced-determined”) but improve the reproducibility by 2-3 times. The problem of increasing the reliability of such statistical evaluations of results is discussed and to solve it, it is proposed to change the “design” of the experiments. Instead of series of successive analyses of similar origin samples, the use of parallel determinations is preferable. This can be realized, for example, during the fulfillment of student’s practical works.

Podurets A., Odegova V., Cherkashina K., Bulatov A., Bobrysheva N., Osmolowsky M., Voznesenskiy M., Osmolovskaya O.

A challenging problem to create an efficient photocatalyst suitable for industrial water remediation, aiming to remove cyclic organic compounds attracts increasing attention. The current study aimed to clarify a few "dark spots" in the field, namely to find out if it is possible to make an efficient photocatalyst activated with visible light by using a simple and cheap strategy and what are the key factor impacting its efficiency. In this work, a new procedure to obtain spherical nanoparticles with the same average size but different amounts of oxygen vacancies and defects and dopant concentrations was developed. The approach based on hydrothermal treatment was suggested to obtain rod-shaped nanoparticles. The systematic study of photocatalytic behavior on the example of oxytetracycline and methylene blue degradation under visible light of widely available LED lamp was performed. Based on chemical and computational experiments the main factor affecting the process efficiency was determined.

Meshina K., Barabanov N., Tkachenko D., Vorontsov-Velyaminov P., Bobrysheva N., Voznesenskiy M., Osmolowsky M., Osmolovskaya O.

Jin D., He H., Sun L., Zeng Z., Liu Z.

Nguyen C.T., Nguyen K.T., Nguyen B.Q., Pham C.N., Van Ngo Q., Dao H.N., Luu H.T., Nguyen M.V., Dao N.N.

Kosyrev D., Podurets A., Skripkin E., Bobrysheva N., Osmolowsky M., Osmolovskaya O., Voznesenskiy M.

Mahto B., Ali H., Barhoi A., Hussain S.

Jyolsna P., Gowthami V., Hajeera Aseen A.

The objective of the present study is to optimise the removal of metals such as aluminium, zinc, and copper from industrial wastewater using green-synthesised nanoadsorbents. To achieve this, the Box–Behnken experimental design and response surface methodology will be employed. We used inductively coupled plasma mass spectrometry to analyse the metals present in the wastewater. A three-factor, three-stage Box–Behnken design was used to maximise the removal of these metals from aqueous solution. This involved response surface modelling and quadratic programming based on 17 different experimental data from a batch study. The study focused on three independent variables: pH, contact time, and adsorbent amount. The nanoadsorbents were prepared using a combination of Citrus X sinensis peel and Musa Cavendish peel extract, which served as the reducing agents, to produce a combined peel extract-silver nanoparticle product. Field emission scanning electron microscopy imaging and UV–visible spectroscopic analysis unequivocally demonstrated the presence of nanoparticles, with a surface plasmon resonance at 438 nm. The optimal values of the selected variables were determined by solving the quadratic regression model and analysing the contour plots of the reaction surface. At the experimental conditions of pH = 5, contact time = 92.5 min, and adsorbent dosage = 0.1 g/L, the recovery efficiency of Al, Cu, and Zn was significantly reduced. The optimised parameters were successfully applied to wastewater collected, and the degradation of detected metal ions was tested. The experiment demonstrated an effective reduction in these metals.

Halder A.K., Haghbakhsh R., Ferreira E.S., Duarte A.R., Cordeiro M.N.

Lv T., Liu F., Xiao M., Liu Y., Wang L., Gao G.

Makarov D.M., Kolker A.M.

Viscosity, the measure of a fluid's resistance to deformation, is a critical parameter in many industries. Being able to accurately predict viscosity is essential for the successful design and optimization of technological processes. In this research, regression models were created to predict the viscosity of deep eutectic solvents (DESs). Machine learning models were trained using a data set of 3440 data points for two component DESs. Different algorithms, such as Multiple Linear Regression, Random Forest, CatBoost, and Transformer CNF, were employed alongside a variety of structural representations like fingerprints, σ-profiles, and molecular descriptors. The effectiveness of the models was assessed for interpolation tasks within the training data and extrapolation outside of it. The results indicate that a rigorous splitting of the dataset into subsets is necessary to accurately evaluate the performance of the models. Two new choline chloride-based DESs were prepared and their viscosities were measured to evaluate the predictive capabilities of the models. The CatBoost algorithm with CDK molecular descriptors was chosen as the recommended model. The average absolute relative deviations (AARD) of this model exhibited fluctuations during 5-fold cross-validation, ranging from 10.8% when interpolating within the dataset to 88% when extrapolating to new mixture components. The open access model was presented in this study (http://chem-predictor.isc-ras.ru/ionic/des/).

López-Flores F.J., Ramírez-Márquez C., González-Campos J.B., Ponce-Ortega J.M.

Christodoulou S., Cousseau C., Limanton E., Toucouere L., Gauffre F., Legouin B., Maron L., Paquin L., Poteau R.

Voskanyan L., Syukkalova E., Glavinskaya V., Osmolowsky M., Voznesenskiy M., Bobrysheva N., Osmolovskaya O.

Soni S., Kumari A., Kumari S., Sharma A., Sheel V., Bhatia S.K., Sharma A.K.

P J., V G., A H.A.

There is a growing demand for cost-effective and sustainable technologies for treating wastewater as water consumption increases and conventional technologies become more expensive. Nanoparticles have a great deal of potential for use in the treatment of waste water. Their unique surface area allows them to effectively remove toxic metal ions, pathogenic microorganisms, organic and inorganic solutes from water. This study investigated the potential of orange and banana peels as renewable nano adsorbents for removing dyes and dissolved organic compounds from textile wastewater. Orange and banana peels are an optimal selection due to their favourable chemical characteristics, namely the presence of cellulose, pectic, hemicellulose, and lignin. Their capacity to adsorb diverse anionic and cationic compounds on their surface-active sites is attributed to their unique functional group compositions. Silver nanoparticles are able to adsorb heavy metals due to their exceptionally low electrical and thermal resistance and surface plasmon resonance. The samples were thoroughly characterised using field emission scanning electron microscopy (FESEM), UV-Visible spectrometry, Fourier transform infrared spectroscopy (FTIR) and XRD. The nanoparticles were prepared (10 gm,50 gm,100 gm) and subsequently introduced to the wastewater sample. The optical density values were recorded at various time points. The optical density values demonstrate a decline over the course of the experiment, with a notable decrease observed over time. The results of this study provide valuable insights into the efficacy of these natural adsorbents and their potential for sustainable water purification technologies. For the purpose of this research, high performance instrumentation methods were performed as follows:•Field emission scanning electron microscopy for surface morphology studies.•Gas chromatography-mass spectrometry (GC-MS) for analytical technique that combines gas chromatography (GC) and mass spectrometry (MS) to identify unknown substances or contaminants.•Optical density values were measured for different timings of degradation.

Kamo A., Ates Sonmezoglu O., Sonmezoglu S.

Ravichandran A., Honrao S., Xie S., Fonseca E., Lawson J.W.

Lavrinenko A.K., Chernyshov I.Y., Pidko E.A.

Boudreault J., Campagna C., Chebana F.

Extreme heat events pose a significant threat to population health that is amplified by climate change. Traditionally, statistical models have been used to model heat-health relationships, but they do not consider potential interactions between temperature-related and air pollution predictors. Artificial intelligence (AI) methods, which have gained popularity for health applications in recent years, can account for these complex and non-linear interactions, but have been underutilized in modelling heat-related health impacts. In this paper, six machine and deep learning models were considered to model the heat-mortality relationship in Montreal (Canada) and compared to three statistical models commonly used in the field. Decision Tree (DT), Random Forest (RF), Gradient Boosting Machine (GBM), Single- and Multi-Layer Perceptrons (SLP and MLP), Long Short-Term Memory (LSTM), Generalized Linear and Additive Models (GLM and GAM), and Distributed Lag Non-Linear Model (DLNM) were employed. Heat exposure was characterized by air temperature, relative humidity and wind speed, while air pollution was also included in the models using five pollutants. The results confirmed that air temperature at lags of up to 3 days was the most important variable for the heat-mortality relationship in all models. NO2 concentration and relative humidity (at lags 1 to 3 days) were also particularly important. Ensemble tree-based methods (GBM and RF) outperformed other approaches to model daily mortality during summer months based on three performance criteria. However, a partial validation during two recent major heatwaves highlighted that non-linear statistical models (GAM and DLNM) and simpler decision tree may more closely reproduce the spike of mortality observed during such events. Hence, both machine learning and statistical models are relevant for modelling heat-health relationships depending on the end user goal. Such extensive comparative analysis should be extended to other health outcomes and regions.

Wang R., Chen J., Song Z., Qi Z.

Shi D., Zhou F., Mu W., Ling C., Mu T., Yu G., Li R.

Deep eutectic solvents (DESs) are emerging as novel green solvents for the processes of mass transport and heat transfer, in which the viscosity of DESs is important for their industrial applications. However, for DESs, the measurement of viscosity is time-consuming, and there are many factors influencing the viscosity, which impedes their wider application. This study aims to develop a data-driven model which could accurately and rapidly predict the viscosity of diverse DESs at different temperatures, and furthermore boost the design and screening of novel DESs. In this work, we collected 107 DESs with 994 experimental values of viscosity from published works. Given the significant effect of water on viscosity, the water content of each collected DES was labeled. The Morgan fingerprint was first employed as a feature to describe the chemical environment of DESs. And four machine learning algorithms were used to train models: support vector regression (SVR), random forest (RF), neural network (NN), and extreme gradient boosting (XGBoost), and XGBoost showed the best predictive performance. In combination with the powerful interpretation method SHapley Additive exPlanation (SHAP), we further revealed the positive or negative effect of features on viscosity. Overall, this work provides a machine learning model which could predict viscosity precisely and facilitate the design and application of DESs.

Khokhar V., Dhingra D., Pandey S.

• Lanthanide metal-based type IV deep eutectic solvents (DESs) are prepared. • Density of type IV metal DESs are higher compared to the other urea-based DESs. • Dynamic viscosities of these DESs are lower compared to other urea-based DESs. • Viscosity of the DESs exhibits Vogel − Fulcher − Tammann (VFT) temperature dependence. • Density and viscosity both decrease with increase in mole ratio of urea. Deep eutectic solvents (DESs) are rapidly becoming solvent media of prominence due not only to their unique properties but also to their promising environmentally-benign nature. For effective utilization of these neoteric media, assessment of their physical properties is essential. Density and dynamic viscosity of a class of DESs constituted of a hydrated lanthanide salt and urea at varying composition in the temperature range 293.15 to 363.15 K are presented. Specifically, different possible eutectic mixtures of lanthanum nitrate hexahydrate: urea (named La : Urea), cerium nitrate hexahydrate: urea (named Ce : Urea) and gadolinium nitrate hexahydrate: urea (named Gd : Urea) are investigated. DES (Gd : Urea) at 1: 2 mole ratio exhibits the highest density (1.98783 g.cm −3 at 293.15 K) of all the DESs investigated; density is found to decrease with increasing urea. Density of these DESs decreases linearly with increasing temperature. Dynamic viscosity of these DESs also decrease with increasing urea content. The temperature dependence of the dynamic viscosity follows Vogel − Fulcher − Tammann (VFT) expression. While the activation energy of viscous flow ( E a,η ) for these (lanthanide salt : Urea) DESs is closer to those of (choline chloride + H-bond donor) DESs, their viscosity-temperature dependence is similar to that of common imidazolium ionic liquids.

Omar K.A., Sadeghi R.

• DESs and their physicochemical properties. • Variation of DESs physicochemical properties with molar ratio, anion size, and alkyl chain length. • Role of water on the formation and physicochemical properties of DESs mixture. Deep eutectic solvents are a unique type of solvent with interesting physical properties, which made them comply with the green solvents criteria such as safety, non-toxicity in case of natural DESs, non-flammability, non-volatility, thermal stability, sustainability, biodegradability, and low cost. In this review, the physicochemical characteristics of deep eutectic solvents under the effect of molar ratio of HBAs/HBDs, size of anion, alkyl chain length, and molar mass on the melting point, density, viscosity, conductivity, surface tension, and refractive index have been revealed. Moreover, the polarity, pH, toxicity, biodegradability, and effect of water on the formation of DESs have been illustrated.

Perumal V., Inmozhi C., Uthrakumar R., Robert R., Chandrasekar M., Mohamed S.B., Honey S., Raja A., Al-Mekhlafi F.A., Kaviyarasu K.

Surfactant -treated tin oxide (SnO2) hierarchical nanorods were successfully synthesized through hydrothermal technique. The X-ray diffraction analysis showed the prepared SnO2 possesses tetragonal rutile structure having appreciable crystallinity with crystallite sizes in the range of 110 nm-120 nm. UV-visible diffuse reflectance absorption spectra confirm that the better visible light absorption band of SnO2 hierarchical nanorods have red shift compared to the pure SnO2. Fourier transform infrared spectroscopy (FTIR) study evident that the as-prepared SnO2 nanorods encompass the characteristic bands of SnO2 nanostructures. The morphological analyses of prepared materials were performed by FESEM, which shows that hierarchal nanorods and complex nanostructures. EDX analyses disclose all the samples are composed of Sn and O elements. The photocatalytic performance of the prepared surfactant treated SnO2 hierarchical nanorods was evaluated using methylene blue (MB) dye removal under direct natural sunlight. Recycling experiment results of CTAB - SnO2 nanorods and photocatalytic reaction mechanism also discussed in detail.

Yu L., Hou X., Ren G., Wu K., He C.

Deep eutectic solvents (DESs), a novel category of sustainable solvents, are expected to achieve the design of the chemical processes without utilizing or generating harmful chemicals. In this work, based on the mathematical model inspired by the transition state theory, the group contribution method is used to accurately predict the viscosity of DESs. The model is constrained by Eyring rate theory and hard sphere free volume theory. A dataset of 2229 experimental viscosity data points of 183 DESs from literature is used to determine the model parameters and subsequently verify the model. The rules introduced by this model are simple and easy to follow. The results show that the proposed model is capable to predict the viscosity of DESs with very high accuracy, using only temperature and composition as inputs. The average absolute relative deviations (AARDs) of the model are 8.12% and 8.64% over the training and test sets, respectively, and the maximum ARD is 34.63%. Therefore, the as-proposed model can be considered a highly reliable tool for predicting the viscosity of DESs when experimental data are absent. It will provide useful guidance for the synthesis of DESs with specific viscosity to meet different application requirements and promote their industrial-scale implementation.

Abdollahzadeh M., Khosravi M., Hajipour Khire Masjidi B., Samimi Behbahan A., Bagherzadeh A., Shahkar A., Tat Shahdost F.

Deep eutectic solvents (DES) are recently synthesized to cover limitations of conventional solvents. These green solvents have wide ranges of potential usages in real-life applications. Precise measuring or accurate estimating thermophysical properties of DESs is a prerequisite for their successful applications. Density is likely the most crucial affecting characteristic on the solvation ability of DESs. This study utilizes seven machine learning techniques to estimate the density of 149 deep eutectic solvents. The density is anticipated as a function of temperature, critical pressure and temperature, and acentric factor. The LSSVR (least-squares support vector regression) presents the highest accuracy among 1530 constructed intelligent estimators. The LSSVR predicts 1239 densities with the mean absolute percentage error (MAPE) of 0.26% and R2 = 0.99798. Comparing the LSSVR and four empirical correlations revealed that the earlier possesses the highest accuracy level. The prediction accuracy of the LSSVR (i.e., MAPE = 0. 26%) is 74.5% better than the best-obtained results by the empirical correlations (i.e., MAPE = 1.02%).

Vakh C., Malkova K., Syukkalova E., Bobrysheva N., Voznesenskiy M., Bulatov A., Osmolovskaya O.

A challenging task in analytical chemistry is an application of renewable and natural materials for isolation of hazardous substances such as antimicrobial drugs from environmental samples. The energy-efficient scalable hydrothermal procedure to fabricate the eco-friendly “switchable” sorbent based on hydroxyapatite nanoparticles with in situ modified surface using a small amount of capping agents was developed. Sorbents characterization including the surface composition investigation via quantum-chemical calculation based on the original approach was provided. The sorbents demonstrated well expressed controllable surface switching and high values of the sorption and elution efficiency for tetracycline, oxytetracycline, and chlortetracycline achieved by simple change of the medium pH. These processes were thoroughly discussed based on the results of chemical and computational experiments. A simple and universal strategy for choosing a suitable sorbent for solid phase extraction of target analytes was proposed for the first time. It was shown that the developed eco-friendly sample preparation procedure with use of biocompatible sorbents could be applied both for removal of target analytes from sample matrix (water samples) as well as for the quantitative analytes determination after elution step. It is believed that the presented research is significant for the determination of different amphoteric analytes in wide variety of samples. • In situ modified HAp nanoparticles were prepared by scalable efficient hydrothermal synthesis. • D- μ -SPE approach based on switchable behavior of HAp nanoparticles surface has been developed. • Simple computational strategy was suggested to predict the analytical performance. • Antibiotics in real samples were determined using HAp nanoparticles and HPLC-UV. • Removal of analytes as well as quantitative analytes determination are possible.

Halder A.K., Haghbakhsh R., Voroshylova I.V., Duarte A.R., Cordeiro M.N.

Deep eutectic solvents (DES) are often regarded as greener sustainable alternative solvents and are currently employed in many industrial applications on a large scale. Bearing in mind the industrial importance of DES—and because the vast majority of DES has yet to be synthesized—the development of cheminformatic models and tools efficiently profiling their density becomes essential. In this work, after rigorous validation, quantitative structure-property relationship (QSPR) models were proposed for use in estimating the density of a wide variety of DES. These models were based on a modelling dataset previously employed for constructing thermodynamic models for the same endpoint. The best QSPR models were robust and sound, performing well on an external validation set (set up with recently reported experimental density data of DES). Furthermore, the results revealed structural features that could play crucial roles in ruling DES density. Then, intelligent consensus prediction was employed to develop a consensus model with improved predictive accuracy. All models were derived using publicly available tools to facilitate easy reproducibility of the proposed methodology. Future work may involve setting up reliable, interpretable cheminformatic models for other thermodynamic properties of DES and guiding the design of these solvents for applications.

Hou X., Yu L., He C., Wu K.

Deep eutectic solvents (DESs) are mixtures of two or more components that have lower melting temperatures compared to their constituting components. DESs possess many advantages, for example, low volatility, low flammability, and low toxicity, which make them promising alternatives to traditional organic solvents. The melting temperature, one of the important physical properties, is of essential importance for industrial applications. In this work, a group and group-interaction contribution method was proposed to estimate the melting temperatures of DESs using an extensive database (1528 DESs, 1541 data points). The average absolute relative deviation (%AARD) between the estimated and experimental values of the melting temperature was 5.67% for binary DESs. Subsequently, this method was also extended to estimate the melting temperature of ternary DESs, with the AARD of 6.13%. The results indicate the high accuracy and broad applicability of the method and pave the way for the rational design of task-specific DESs.

Chandran K., Kait C.F., Wilfred C.D., Zaid H.F.

• The application of eutectic ionic liquids in different fields are reviewed. • The physiochemical properties of different types of eutectic ionic liquids are compared. • The effect the physiochemical properties on sulfur dioxide absorption capacity are reviewed. • Performance evaluation for real environmental matrices was compared. Solvent plays an important role as a green chemistry. The solvents must meet certain criteria such as biodegradable, recyclability, low cost, availability and non-toxic to be qualified as a green medium. To date, solvents which are labelled as green medium are very limited. The new family of ionic fluids, called deep eutectic solvents (DESs) are now rapidly developing in terms of green research. A DES is a fluid that typically consists of two or three components that are secure and cost-friendly, able to associate frequently by itself through interactions of the hydrogen bond, creating a eutectic mixture with a melting point lower than that of each component. The DESs tend to be liquid at temperatures below 373.15 K. Most of the DESs exhibits similar physico-chemical properties to the traditionally used ionic liquids, adding in the advantage that it is much cheaper and environmentally friendlier. Thus, DESs are increasingly involved in many research areas, this is because of the advantage that it carries compared to ionic liquids itself especially in sulfur dioxide gas separation process. The main aim of this review is to determine and summarize the recent research and the major contributions of DESs especially in sulfur dioxide extraction and absorption.

Hou X., Yu L., Wang Y., Wu K., He C.