The role of solute conformation, solvent–solute and solute–solute interactions in crystal nucleation

Abstract

The induction time for nucleation can differ based on the solutions used to conduct a crystallization, which can in turn impact the efficiency and economics of a crystallization process, the crystal size distribution, the morphology and ultimately functionality of the final product. Establishing a link between the nucleation pathway/solution structure and nucleation induction time is essential to achieve improved comprehension of the process of crystal nucleation from solution. In this study, the role of solute conformation, solvent-solute interaction, and solute-solute interaction in nucleation was examined using tolbutamide as a model compound in toluene and toluenealcohol solutions. Through a combination of induction time experiments, attenuated total reflection Fourier transformed infrared spectroscopy, nuclear magnetic resonance spectroscopy, molecular dynamics simulations, and quantum chemical calculations, it was found that not only solvent-solute interactions but also solute-solute interactions and structural similarities between molecular self-assemblies in the solution and synthons in the crystal structure, can significantly influence the nucleation induction time.

Mechanistic Study on the Effect of Solvents on Polymorphic Transformation of Aripiprazole by Solvent Property Parameters and Molecular Dynamics Simulation

“…Since solute–solvent interactions can strongly influence the conformations of the solute in solution, and the conformations of the solute in solution are one of the most important factors affecting the polymorphism of the product, the conformations of the APZ molecules in 15 different solvents were investigated. − First, a search for conformations of the APZ molecules was carried out using Molclus with an energy threshold of 7 kcal/mol . Fifty-one different conformations were obtained after several rounds of optimization and de-emphasis and ranked in the descending order of stability, and the results are shown in Figure S3.…”

Machine Learning Methods to Improve Crystallization through the Prediction of Solute–Solvent Interactions

“…If the interactions between the solute and solvent are strong, the solvent may be more likely to become incorporated into the crystal structure, leading to impure crystals. On the other hand, if the interactions are weak, the solvent may be less likely to become incorporated into the crystal structure, leading to purer crystals [49].…”

“…Understanding these interactions can help scientists optimize the crystallization process, leading to the formation of high-quality crystals. This can be important in many fields [49].…”

“…By optimizing the solute-solvent interactions, manufacturers can control the rate of crystallization, which can influence the size and purity of the crystals. This can lead to a more efficient manufacturing process, reducing waste and lowering production costs [49].…”