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 ). 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