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

Supercritical Antisolvent Fractionation of Antioxidant Compounds from Salvia officinalis

The increasing interest towards greener antioxidants obtained via natural sources and more sustainable processes encourages the development of new theoretical and experimental methods in the field of those compounds. Two advanced separation methods using supercritical CO2 are applied to obtain valuable antioxidants from Salvia officinalis, and a first approximation to a QSAR model relating molecular structure with antioxidant activity is explored in order to be used, in the future, as a guide for the preselection of compounds of interest in these processes. Separation experiments through antisolvent fractionation with supercritical CO2 were designed using a Response Surface Methodology to study the effect of pressure and CO2 flow rate on both mass yields and capability to obtain fractions enriched in three antioxidant compounds: chlorogenic acid, caffeic acid and rosmarinic acid which were tracked using HPLC PDA. Rosmarinic acid was completely retained in the precipitation vessel while chlorogenic and caffeic acids, though distributed between the two separated fractions, had a major presence in the precipitation vessel too. The conditions predicted for an optimal overall yield and enrichment were 148 bar and 10 g/min. Although a training dataset including much more compounds than those now considered can be recommended, descriptors calculated from the σ-profiles provided by COSMO-RS model seem to be adequate for estimating the antioxidant activity of pure compounds through QSAR.

Molecular dynamic study of alcohol-based deep eutectic solvents

The applicability of deep eutectic solvents is determined by their physicochemical properties. In turn, the properties of eutectic mixtures are the result of the components' molar ratio and chemical composition. Owing to the relatively low viscosities displayed by alcohol-based deep eutectic solvents (DESs), their application in industry is more appealing. Modeling the composition-property relationships established in polyalcohol-based mixtures is crucial for both understanding and predicting their behavior. In this work, a physicochemical property-structure comparison study is made between four choline chloride polyalcohol-based DESs, namely, ethaline, propeline, propaneline, and glyceline. Physicochemical properties obtained from molecular dynamic simulations are compared to experimental data, whenever possible. The simulations cover the temperature range from 298.15 to 348.15 K. The simulated and literature experimental data are generally in good agreement for all the studied DESs. Structural properties, such as radial and spatial distribution functions, coordination numbers, hydrogen bond donor (HBD)-HBD aggregate formation, and hydrogen bonding are analyzed in detail. The higher prevalence of HBD:HBD and HBD:anion hydrogen bonds is likely to be the major reason for the relatively high density and viscosity of glyceline as well as for lower DES self-diffusions.

Deep Eutectic Solvents and Multicomponent Reactions: Two Convergent Items to Green Chemistry Strategies

One of the highlights of green chemistry is the development of techniques and procedures with low environmental impact. In the last years, deep eutectic solvents (DES) have become an important alternative to conventional organic solvents. For a period ionic liquids have provoked remarkable interest, but they have been displaced by DES because they show easier preparation methods, lower prices, many of them are biodegradable and compatible with biological systems. In addition, they show adjustable physicochemical properties, high thermal stability, low volatility and are compatible with water. In this paper is reviewed the state of the art of the use of DES paying special attention to the role of reaction media in organic synthesis.

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.

Industrial Requirements for Thermodynamic and Transport Properties: 2020

This paper reports the results of an investigation of industrial requirements for thermodynamic and transport properties carried out during the years 2019-2020. It is a follow-up of a similar investigation performed and published 10 years ago by the Working Party (WP) of Thermodynamics and Transport Properties of European Federation of Chemical Engineering (EFCE).1 The main goal was to investigate the advances in this area over the past 10 years, to identify the limitations that still exist, and to propose future R&D directions that will address the industrial needs. An updated questionnaire, with two new categories, namely, digitalization and comparison to previous survey/changes over the past 10 years, was sent to a broad number of experts in companies with a diverse activity spectrum, in oil and gas, chemicals, pharmaceuticals/biotechnology, food, chemical/mechanical engineering, consultancy, and power generation, among others, and in software suppliers and contract research laboratories. Very comprehensive answers were received by 37 companies, mostly from Europe (operating globally), but answers were also provided by companies in the USA and Japan. The response rate was about 60%, compared to 47% in the year 2010. The paper is written in such a way that both the majority and minority points of view are presented, and although the discussion is focused on needs and challenges, the benefits of thermodynamics and success stories are also reported. The results of the survey are thematically structured and cover changes, challenges, and further needs for a number of areas of interest such as data, models, systems, properties, and computational aspects (molecular simulation, algorithms and standards, and digitalization). Education and collaboration are discussed and recommendations on the future research activities are also outlined. In addition, a few initiatives, books, and reviews published in the past decade are briefly discussed. It is a long paper and, to provide the reader with a more complete understanding of the survey, many (anonymous) quotations (indicated with "..." and italics) from the industrial colleagues who have participated in the survey are provided. To help disseminate the specific information of interest only to particular industrial sectors, the paper has been written in such a way that the individual sections can also be read independently of each other.

Synthesis, molecular dynamics simulation and adsorption study of different pollutants on functionalized mesosilica

Experimental and computational works were carried out on a new type of mesoporous silica. In the experimental section, functionalized hollow mesosilica spheres were prepared via a facile technique and then evaluated using some analytical techniques (FESEM, TEM, L-XRD, FTIR, BET-BJH, and TGA). The obtained results revealed that the synthesized material had hollow structure with a diamino-grafted porous shell. The molecular separation of crystal Violet (CV) and neutral Red (NR) dyes from water were investigated by adsorption process using the synthesized powder. Influence of adsorbent loading was evaluated as adsorption ability and dyes removal efficiency. Also, the obtained modeling results revealed appropriate fitting of data with non-linear Langmuir model. The theoretical studies were employed to study the adsorption and removal mechanism of cationic (CV and NR) and anionic (orange II (OII)) dyes using molecular dynamics calculations. Moreover, the simulation outcomes provided valuable information about quantum chemical properties including the HOMO-LUMO maps, chemical reactivity, global softness (σ) and hardness (η) for silica-linker-water-dyes components.

In silico drug discovery of IKK-β inhibitors from 2-amino-3-cyano-4-alkyl-6-(2-hydroxyphenyl) pyridine derivatives based on QSAR, docking, molecular dynamics and drug-likeness evaluation studies

The Inhibitor of IKK-β (nuclear factor kappa B kinase subunit beta), a specific modulator of NF-κB (nuclear factor-κB), is considered a valid target to discover new active compounds for various cancers and rheumatoid arthritis treatment. In this study a series of thirty 2-amino-3-cyano-4-alkyl-6-(2-hydroxyphenyl) pyridine derivatives was involved for a quantitative structure activity relationship model (QSAR) elaboration which allows the prediction of the pIC50 values of new designed compounds. The model can be used to predict the activity of new compounds within its applicability domain. Then a molecular docking study was carried out to identify the interactions between the compounds and the amino acids of the active site. After that, golden triangle, Veber's rule, and Lipinski's rule properties were calculated to identify the drug-likeness properties of the investigated compounds. Finally, in-silico-toxicity studies were performed to predict the toxicity of the new designed compounds. The analysis of the results of QSAR model and molecular docking succeeded to screen 21 interesting compounds with better inhibitory concentration having a good affinity to IKK-β. All compounds were within the range set by Veber's rule and Lipinski's rule. the analysis of golden triangle showed that the thirty 2-amino-3-cyano-4-alkyl-6-(2-hydroxyphenyl) pyridine derivatives would not have clearance and cell membrane permeability problems except comp6 comp12,comp20, comp21, and comp26.As for the new designed compounds, their properties may have these problems, except two compounds which are: A8m, A8p. The A1m, A1p, A3p and A11m compounds were predicted to be nontoxic. These findings indicate that the novel potent candidate drugs have promising potential to IKK-β enzyme inhibition and should motivate future experimental investigations.Communicated by Ramaswamy H. Sarma.