Application of the Consonance Solvent Concept for Accurate Prediction of Buckminster Solubility in 180 Net Solvents using COSMO-RS Approach

Intermolecular Interactions as a Measure of Dapsone Solubility in Neat Solvents and Binary Solvent Mixtures

Dapsone is an effective antibacterial drug used to treat a variety of conditions. However, the aqueous solubility of this drug is limited, as is its permeability. This study expands the available solubility data pool for dapsone by measuring its solubility in several pure organic solvents: N-methyl-2-pyrrolidone (CAS: 872-50-4), dimethyl sulfoxide (CAS: 67-68-5), 4-formylmorpholine (CAS: 4394-85-8), tetraethylene pentamine (CAS: 112-57-2), and diethylene glycol bis(3-aminopropyl) ether (CAS: 4246-51-9). Furthermore, the study proposes the use of intermolecular interactions as molecular descriptors to predict the solubility of dapsone in neat solvents and binary mixtures using machine learning models. An ensemble of regressors was used, including support vector machines, random forests, gradient boosting, and neural networks. Affinities of dapsone to solvent molecules were calculated using COSMO-RS and used as input for model training. Due to the polymorphic nature of dapsone, fusion data are not available, which prohibits the direct use of COSMO-RS for solubility calculations. Therefore, a consonance solvent approach was tested, which allows an indirect estimation of the fusion properties. Unfortunately, the resulting accuracy is unsatisfactory. In contrast, the developed regressors showed high predictive potential. This work documents that intermolecular interactions characterized by solute-solvent contacts can be considered valuable molecular descriptors for solubility modeling and that the wealth of encoded information is sufficient for solubility predictions for new systems, including those for which experimental measurements of thermodynamic properties are unavailable.

Carbon nanomaterials against pathogens; the antimicrobial activity of carbon nanotubes, graphene/graphene oxide, fullerenes, and their nanocomposites

Recently, antibiotic resistance of pathogens has grown given the excessive and inappropriate usage of common antimicrobial agents. Hence, producing novel antimicrobial compounds is a necessity. Carbon nanomaterials (CNMs) such as carbon nanotubes, graphene/graphene oxide, and fullerenes, as an emerging class of novel materials, can exhibit a considerable antimicrobial activity, especially in the nanocomposite forms suitable for different fields including biomedical and food applications. These nanomaterials have attracted a great deal of interest due to their broad efficiency and novel features. The most important factor affecting the antimicrobial activity of CNMs is their size. Smaller particles with a higher surface to volume ratio can easily attach onto the microbial cells and affect their cell membrane integrity, metabolic procedures, and structural components. As these unique characteristics are found in CNMs, a wide range of possibilities have raised in terms of antimicrobial applications. This study aims to cover the antimicrobial activities of CNMs (both as individual forms and in nanocomposites) and comprehensively explain their mechanisms of action. The results of this review will present a broad perspective, summarizes the most remarkable findings, and provides an outlook regarding the antimicrobial properties of CNMs and their potential applications.