Journal of Colloid and Interface Science, Vol.582, 859-873, 2021
Nucleation and growth of cholesteric collagen tactoids: A time-series statistical analysis based on integration of direct numerical simulation (DNS) and long short-term memory recurrent neural network (LSTM-RNN)
Hypothesis: Liquid-crystalline phase separation by nucleation and growth (NG) is a crucial step in the formation of collagen-based biomaterials. However, the fundamental mechanisms are not completely understood for chiral lyotropic colloidal mesogens such as collagen. Methodology: To capture the dynamics of NG under a quenching process into the biphasic equilibrium zone, we use direct numerical simulation based on the time-dependent Ginzburg-Landau model allowing minimization of the total free energy comprised of five key contributions: phase separation (Flory-Huggins), ordering (Landau-de Gennes), chiral orientational elasticity (Frank-Oseen-Mermin), interfacial and coupling effects. LSTM-RNN is applied as a surrogate model to greatly enrich the results. Significant correlations are established using Symbolic Regression. Findings: We quantify the NG boundaries existing in the collagen phase diagram that has recently been developed and validated by our thermodynamic model (Khadem and Rey, 2019 [1]). We characterize the three NG stages (induction, nucleation, and coarsening) in terms of tactoids' shape, morphology, growth laws, and population across the NG zone. Wide-range generic correlations are developed, revealing the quench depth dependence of NG characteristics and connecting the sequential NG stages. We confirm experimental observations on time-dependent growth law exponent changes from an initial n approximate to 0.5 for the mass transfer limited regime to n approximate to 1 for the volume-driven phase ordering regime upon increasing quench depth during the nucleation period and having exclusively a value of n approximate to 0.5 for the coarsening period regardless of quench depth. We lastly uncover the underlying physics behind the NG phenomena. (C) 2020 Elsevier Inc. All rights reserved.
Keywords:Biological chiral lyotropic liquid crystals;Biomimetic collagen-based biomaterials;Liquid-crystalline self-assembly;Chiral nematic tactoids;Cholesteric nucleation;Growth and coarsening;Universal growth laws;Uphill diffusion;Time-dependent Ginzburg-Landau model;Long Short-Term Memory Recurrent Neural Network;Symbolic Regression