Resumen:
Reconstructing the morphology of aerogels presents significant challenges if 3D visualizations of their mesoporous nanostructure are desired. Available microscopic and tomographic tools find it difficult to probe into all types of aerogels for the purposes of reconstructing their 3D nanoporous morphology. This is where computational approaches have shown promising efforts to explain experimental observed phenomena.
First, the subject of scaling laws in aerogels will be addressed [1]. The role of random spatial arrangement of particles within the aerogel network will be elucidated. Different approaches for modelling aerogels will be discussed [2]. Particularly, particle-aggregated aerogels such as those from silica as well as fibrillar ones such as those from biopolymers will be the subject of presentation. The application of data-driven methods, namely machine learning, for predictive modelling as well as for reverse engineering the design
of aerogels will be demonstrated.
References
- S. Aney, P. Pandit, L. Ratke, B. Milow, and A. Rege, J. Sol-Gel Sci. Technol. (2023)
- A. Rege, Adv. Eng. Mater. 25, 2201097 (2023
