Abstract Silica monoliths are hierarchically structured, versatile materials that are widely used in analytical separation science, e.g., liquid chromatography. Their functionality strongly depends on the 3D morphology of their macropore and mesopore spaces. In the present paper, we consider three differently manufactured silica monolith samples, where the process conditions of their hydrothermal treatment (affecting, e.g., mesopore size) have been varied, and present a parametric stochastic 3D microstructure model that is able to generate digital twins of the resulting mesopore spaces. The model, which is based on random point processes and relative neighborhood graphs, theoretically guarantees the complete connectivity of both, the silica phase and the mesopores. The parametric model is fitted to electron tomographic image data. For this purpose, we optimize a cost function that is based on empirically derived relationships between model parameters and volume fraction, mean geodesic tortuosity and constrictivity. Validation is performed regarding further microstructure characteristics, which are not used for model fitting, and regarding effective diffusivity, which is numerically simulated by a particle-tracking algorithm based on random walks.

中文翻译：

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通过基于图形的随机微观结构建模生成介孔二氧化硅的数字孪生

摘要 二氧化硅整料是分层结构的多功能材料，广泛用于分析分离科学，例如液相色谱。它们的功能在很大程度上取决于其大孔和中孔空间的 3D 形态。在本文中，我们考虑了三种不同制造的二氧化硅整体样品，其中它们的水热处理工艺条件（影响，例如中孔尺寸）已经改变，并提出了一个参数随机 3D 微观结构模型，该模型能够生成由此产生的中孔空间。该模型基于随机点过程和相对邻域图，理论上保证了二氧化硅相和中孔的完全连通性。参数模型适合电子断层摄影图像数据。以此目的，我们优化了一个成本函数，该函数基于模型参数与体积分数、平均测地线曲率和收缩率之间的经验关系。对不用于模型拟合的进一步微观结构特征和有效扩散率进行验证，其通过基于随机游走的粒子跟踪算法进行数值模拟。