RESUMEN
The solubility of eplerenone (EP) in 13 pure solvents (acetonitrile, N,N-dimethylformamide (DMF), acetone, 2-butanone, 4-methyl-2-pentanone, ethyl formate, methyl acetate, ethyl acetate, propyl acetate, butyl acetate, methyl propionate, ethyl propionate, ethanol, and 1-propanol) was determined by the gravimetric method at atmospheric pressure and various temperatures (from 283.15 to 323.15 K). The results showed that the solubility of EP in the selected solvents was positively correlated with the thermodynamic temperature, and the order of solubility of EP at 298.15 K was acetonitrile > DMF > 2-butanone > methyl acetate > 4-methyl-2-pentanone > methyl propionate > ethyl acetate > propyl acetate > ethyl formate > acetone > butyl acetate > ethanol >1-propanol. The modified Apelblat model, van't Hoff model, λh model, and polynomial empirical model were used for fitting the solubility data, and then the λh model was found to have the highest fitting accuracy with a minimum ARD of 7.0 × 10-3 and a minimum RMSD of 6.1 × 10-6. The solvent effect between the solute and the solvent was analyzed using linear solvation energy relationship (LSER), and the enthalpy of solvation (ΔsolH°), entropy of solvation (ΔsolS°), and Gibbs free energy of solvation (ΔsolG°) of the dissolution process of EP were calculated by the van't Hoff model, which indicated that the dissolution process of EP in the selected solvents was endothermic, nonspontaneous, and entropy-increasing. In this work, the solubility, dissolution characteristics, and thermodynamic parameters of EP were studied, which will provide data support for the production, crystallization, and purification of EP and will provide important guidance for the crystallization optimization of EP in industry.
RESUMEN
Anti-wear performance is a crucial quality of lubricants, and it is important to conduct research into the structure-activity relationship of anti-wear additives in bio-based lubricants. These lubricants are eco-friendly and energy-efficient. A literature review resulted in the construction of a dataset comprising 779 anti-wear properties of 79 anti-wear additives in rapeseed oil, at various loadings and additive levels. The anti-wear additives were classified into six groups, including phosphoric acid, formate esters, borate esters, thiazoles, triazine derivatives, and thiophene. Logistic regression analysis revealed that the quantity and kind of anti-wear agents had significant effects on the anti-wear properties of rapeseed oil, with phosphoric acid being the most effective and thiophene being the least effective. To identify the specific structural data that affect the anti-wear capabilities of additives in bio-based lubricants of rapeseed oil, a random forest classification model was developed. The results showed a 0.964 accuracy (ACC) and a 0.931 Matthews Correlation Coefficient (MCC) on the test set. The ranking of importance and characterization of MACCS descriptors in the model confirms that anti-wear additives with chemical structures containing P, O, N, S and heterocyclic groups, along with more than two methyl groups, improve the anti-wear performance of rapeseed oil. The application of data analysis and machine learning to investigate the classifications and structural characteristics of anti-wear additives in rapeseed oil provides data references and guiding principles for designing anti-wear additives in bio-based lubricants.