Cellular Automata Modeling of Silica Aerogel Condensation Kinetics

Borzęcka, Nina H.; Nowak, Bartosz; Pakuła, Rafał; Przewodzki, Robert; Gac, Jakub M.

doi:10.3390/gels7020050

Cited by 10 publications

(2 citation statements)

References 26 publications

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“…Other authors have also shown the applicability of DLCA toward the description of silica aerogels. For example, very recently, Borzecka et al, [ 48 ] developed 2D DLCA and RLCA models to study the gelation kinetics in aerogels. They obtained the condensation kinetics curves and qualitatively compared the results to those obtained experimentally.…”

Section: Particle‐aggregated Aerogelsmentioning

confidence: 99%

A Perspective on Methods to Computationally Design the Morphology of Aerogels

Rege

2022

Adv Eng Mater

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Reconstructing aerogel morphology presents significant challenges, in particular, if 3D visualizations of their mesoporous network 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. Herein, diverse models that can be applied to describing different aerogels are explored. To begin with, cluster–cluster aggregation models are examined for simulating the sol–gel process and the resulting morphologies in fractal aerogels, e.g., silica‐based. Gaussian random field models and polymerization‐induced phase separation models are explored for modeling organic non‐fractal aerogels, e.g., resorcinol‐formaldehyde (RF) ones. This is followed by Langevin‐dynamics‐based discrete element models that are explored for simulating gelation in fibrillar aerogels, e.g., those from biopolymer sources. Lastly, modified Voronoi approaches are investigated for describing the 3D fibrillar morphology, also of fibrillar aerogels, like those from biopolymers. A perspective is presented highlighting the strengths as well as shortcomings in each of the model approaches. Possibilities to either extend available approaches or explore new ones are briefly discussed at every interval.

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Section: Particle‐aggregated Aerogelsmentioning

confidence: 99%

A Perspective on Methods to Computationally Design the Morphology of Aerogels

Rege

2022

Adv Eng Mater

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“…Figure 1 b visualises its corresponding computational parallel, generated through stacking of 2D slices of DLCA structures. Moreover, it has also been shown to qualitatively describe the gelation kinetics post the nucleation of the secondary particles during the sol–gel process 36 , 37 . Thus, the DLCA models, if optimised, present the opportunity to be extrapolated for the controlled synthesis of silica aerogel in an automated deep-learning laboratory, while expediting the process and facilitating the discovery of more efficient and sustainable aerogel synthesis pathways.…”

Section: Introductionmentioning

confidence: 99%

Deep reinforcement learning for microstructural optimisation of silica aerogels

Pandit,

Abdusalamov,

Itskov

et al. 2024

Sci Rep

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Silica aerogels are being extensively studied for aerospace and transportation applications due to their diverse multifunctional properties. While their microstructural features dictate their thermal, mechanical, and acoustic properties, their accurate characterisation remains challenging due to their nanoporous morphology and the stochastic nature of gelation. In this work, a deep reinforcement learning (DRL) framework is presented to optimise silica aerogel microstructures modelled with the diffusion-limited cluster–cluster aggregation (DLCA) algorithm. For faster computations, two environments consisting of DLCA surrogate models are tested with the DRL framework for inverse microstructure design. The DRL framework is shown to effectively optimise the microstructure morphology, wherein the error of the material properties achieved is dependent upon the complexity of the environment. However, in all cases, with adequate training of the DRL agent, material microstructures with desired properties can be achieved by the framework. Thus, the methodology provides a resource-efficient means to design aerogels, offering computational advantages over experimental iterations or direct numerical solutions.

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Cellular Automata Coupled with Two‐Phase Lattice Boltzmann Model for Modeling of Kinetics of Formation and Structure of Silica‐Based Sol–Gel Materials

Gac,

Nowak,

Borzęcka

2024

Adv Eng Mater

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A numerical model describing the sol–gel process on a mesoscopic scale is presented. The model is implemented as a cellular automaton‐based system, specifically reaction‐limited aggregation merged with two‐phase lattice Boltzmann method, which allows to describe the sol–gel process together with microscopic phase separation occurring during this process. The influence of model parameters on the structural properties of the resulting gel porosity, mesoporosity, and specific surface area, as well as on the kinetics of the gelation process: the shape of the kinetics curve and the structure formation time, is examined. It is proposed to combine the model parameters with the composition of the reaction mixture (the content of individual reagents and catalysts) and the process conditions.

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Cellular Automata Modeling of Silica Aerogel Condensation Kinetics

Cited by 10 publications

References 26 publications

A Perspective on Methods to Computationally Design the Morphology of Aerogels

A Perspective on Methods to Computationally Design the Morphology of Aerogels

Deep reinforcement learning for microstructural optimisation of silica aerogels

Cellular Automata Coupled with Two‐Phase Lattice Boltzmann Model for Modeling of Kinetics of Formation and Structure of Silica‐Based Sol–Gel Materials

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