Analysis and prediction of nucleation mechanisms in a bi-component powder bed with wettability differentials

Abstract

In this study, the effect of hydrophobic components was analyzed through single drop nucleation experiments on a particle bed. The particle bed's relative hydrophobicity/wettability was varied by changing the percentage composition of hydrophobic and hydrophilic glass beads. The generated nuclei were then isolated and tested using near-infrared spectroscopy and image analysis. The data collected from repeating the experiments were then processed using a random forest machine learning model to predict the probability of immersion and solid-spread nucleation. This model was used to indicate the nucleation mechanism of two formulations. The first contains a varying composition of Ibuprofen and Microcrystalline cellulose, and the second consists of varying compositions of Acetaminophen and Microcrystalline cellulose. The model was validated with experiments. At higher percentage composition of Acetaminophen, the model predicted a higher probability of immersion nucleation. At a higher concentration of Ibuprofen, the model predicted a high likelihood of solid-spread nucleation.