Diosmetin, a natural flavonoid compound, has been demonstrated to exhibit significant neuroprotective effects by inhibiting neuroinflammation and oxidative stress, thereby ameliorating the pathological progression of Alzheimer's disease. However, its current industrial production primarily relies on chemical synthesis, where solvent selection critically impacts reaction efficiency and product purity, consequently limiting its large-scale pharmaceutical applications. This study systematically investigated the solubility behavior of diosmetin in ten pure solvents and three binary solvent systems within a temperature range of 278.15 to 323.15 K. The solubility data were accurately determined using the static equilibrium-high performance liquid chromatography method, while the solvent effects were mechanistically elucidated through an innovative combination of Hansen solubility parameters and the KAT-LSER model. By establishing a multidimensional thermodynamic correlation framework including the modified Apelblat model, λh model, CNIBS/R-K model, Jouyban-Acree model, and SUN model, the study revealed that solubility exhibited a positive correlation with temperature, with N,N-dimethylformamide demonstrating the optimal dissolution performance. Thermodynamic analysis revealed the dissolution process to be endothermic, entropy-driven and non-spontaneous, with hydrogen-bond basicity/polarity enhancing solubility while cohesion energy inhibited it. The modified Apelblat and CNIBS/R-K models exhibited optimal predictive accuracy for pure and binary systems respectively. These findings provide a crucial theoretical foundation for solvent screening and process optimization in the industrial production of diosmetin.
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