Moldovan Alexandru A, Maloney Andrew G P
The Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, U.K.
Cryst Growth Des. 2024 May 1;24(10):4160-4169. doi: 10.1021/acs.cgd.4c00259. eCollection 2024 May 15.
Understanding the surface properties of particles is crucial for optimizing the performance of formulated products in various industries. However, acquiring this understanding often requires expensive trial-and-error studies. Here, we present advanced surface analysis tools that enable the visualization and quantification of chemical and topological information derived from crystallographic data. By employing functional group analysis, roughness calculations, and statistical interaction data, we facilitate direct comparisons of surfaces. We further demonstrate the practicality of our approach by correlating the sticking propensity of distinct ibuprofen morphologies with surface and particle descriptors calculated from a single crystal structure. Our findings support and expand upon previous work, demonstrating that the presence of a carboxylic acid group on the {011} facet leads to significant differences in particle properties and explains the higher electrostatic potential observed in the block-like morphology. While our surface analysis tools are not intended to replace the importance of chemical intuition and expertise, they provide valuable insights for formulators and particle engineers, facilitating informed, data-driven decisions to mitigate formulation risks. This research represents a significant step toward a comprehensive understanding of particle surfaces and their impact on products.
了解颗粒的表面性质对于优化各行业配方产品的性能至关重要。然而,要获得这种了解通常需要进行昂贵的反复试验研究。在此,我们展示了先进的表面分析工具,这些工具能够对源自晶体学数据的化学和拓扑信息进行可视化和量化。通过采用官能团分析、粗糙度计算和统计相互作用数据,我们便于对表面进行直接比较。我们还通过将不同布洛芬形态的粘附倾向与从单晶结构计算出的表面和颗粒描述符相关联,进一步证明了我们方法的实用性。我们的研究结果支持并扩展了先前的工作,表明{011}晶面上羧酸基团的存在导致颗粒性质存在显著差异,并解释了在块状形态中观察到的较高静电势。虽然我们的表面分析工具并非旨在取代化学直觉和专业知识的重要性,但它们为配方设计师和颗粒工程师提供了有价值的数据,有助于做出明智的、基于数据的决策以降低配方风险。这项研究朝着全面理解颗粒表面及其对产品的影响迈出了重要一步。