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)

Khadem, Sayyed Ahmad; Rey, Alejandro D.

doi:10.1016/j.jcis.2020.08.052

Cited by 24 publications

(21 citation statements)

References 94 publications

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“…For the four (L) lumens, N = 4 and the plywood has a -1 saddle defect (*). Republished with permission of Royal Society of Chemistry, from (Rey, 2010); permission conveyed through Copyright Clearance Center, Inc. 2019b; Khadem and Rey, 2021). The predictions are in excellent agreement with experimental data (Gobeaux et al, 2007).…”

Section: Paranematic Phase Ordering Under Shear and Extensional Kinem...supporting

confidence: 61%

Structure and Pattern Formation in Biological Liquid Crystals: Insights From Theory and Simulation of Self-Assembly and Self-Organization

Wang

Servio

Rey

2022

Front. Soft. Matter

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This review presents theory and simulation of liquid crystal phase ordering in biological fibrous materials, solutions, and composites in the presence of elastic fields, second phase inclusions, and transport phenomena, including complex shear-extensional flow and mass transfer. Liquid crystal self-assembly through phase ordering on elastic deformable membranes is first applied to characterize the mechanisms that control the structures in plant cell walls, highlighting how curvophobic and curvophilic effects introduce new structuring fields beyond hard-core repulsion. Then chiral nematic self-assembly is simulated in a mesophase containing fibrillar colloidal inclusions (liquid crystal-fibre composites) to demonstrate how the inclusion positional order generates defects and disclinations as shown in the plant cell wall. Coupling phase ordering to tuned transport phenomena is shown how and why it leads to self-organization such as paranematic states of dilute acidic aqueous collagen solutions. Further directed dehydration of well-organized paranematic collagen leads to defect free cholesteric films only when directed dehydration is synchronized with chirality formation. In addition, the ubiquitous surface nanowrinkling of cholesterics is captured with surface anchoring. In these four representative systems, the new mechanisms that enhance the well-known exclude volume interactions are identified quantified and validated with experimental data. Future directions to create new advanced multifunctional materials based on principles of self-assembly and self-organization are identified by leveraging the new couplings between material structure, geometry, and transport phenomena.

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Section: Paranematic Phase Ordering Under Shear and Extensional Kinem...supporting

confidence: 61%

Structure and Pattern Formation in Biological Liquid Crystals: Insights From Theory and Simulation of Self-Assembly and Self-Organization

Wang

Servio

Rey

2022

Front. Soft. Matter

Self Cite

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“…In order to show the RNN modelling performance in the presence of a data shortage problem, we assume that the data samples corresponding to three days for zones a and b are available. Two scenarios are considered for each zone: (1) the first scenario considers using the data of three days in January (26-28 January), and (2) the second scenario uses the data of three days in February (24)(25)(26). These cases are listed in Table 1.…”

Section: Rnn Model Identificationmentioning

confidence: 99%

“…Among several machine learning techniques, recurrent neural networks (RNN) have widely been used for dynamic modelling due to their capabilities to capture nonlinearities, [21][22][23][24][25][26] in general. RNN models have also been used for dynamic modelling of HVAC systems, in particular.…”

Section: Introductionmentioning

confidence: 99%

A hybrid machine learning approach integrating recurrent neural networks with subspace identification for modelling HVAC systems

2022

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This paper addresses the problem of system identification for heating, ventilation, and air conditioning (HVAC) systems using a relatively small amount of data for the zone under consideration, by leveraging larger datasets for similar zones. To this end, a hybrid machine learning approach is developed where a pre‐trained recurrent neural network (RNN) model, trained on a large amount of data from a representative zone, is leveraged to build models for the other zones using a smaller amount of data. This is achieved by developing a hybrid model that integrates the pre‐trained RNN model with the models built using the subspace identification (SubID) technique to predict the residuals (differences between the real outputs and the predicted outputs from the pre‐trained RNN model) in the other zones. The effectiveness of the proposed hybrid approach is shown using real data collected from a multi‐zone fitness centre. The results demonstrate the superior performance of the hybrid approach over the cases where individual RNN and SubID models are directly developed using only the data from the zones in question.

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“…For certain systems, induction time cannot even be considered due to sharp phase transition, while for some cases there is induction time governed by different material properties. For example, by evaporating a cellulose nanocrystal-based cholesteric drop, the drop edges are pinned to the substrate, which leads to nonequilibrium sliding of the individual cholesteric fragment with active ordering [22]; following the induction period of cholesteric collagen tactoids, phase separation goes through the nucleation process during which multiple chiral nuclei spontaneously emerge and grow throughout the continuous isotropic phase [23]. In the present work, a model was proposed based on CNT to relate the induction time and MSZW data with the nucleation parameters for the systems with an experimentally measurable nucleation point.…”

Section: Introductionmentioning

confidence: 99%

Comparison of the Nucleation Parameters of Aqueous l-glycine Solutions in the Presence of l-arginine from Induction Time and Metastable-Zone-Width Data

Shiau

2021

Crystals

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Induction time and metastable-zone-width (MSZW) data for aqueous L-glycine solutions in the presence of L-arginine impurity were experimentally measured using a turbidity probe in this study. The nucleation parameters, including the interfacial free energy and pre-exponential nucleation factor, obtained from induction time data, were compared with those obtained from MSZW data. The influences of lag time on the nucleation parameters were examined for the induction time data. The effects of L-arginine impurity concentration on the nucleation parameters based on both the induction time and MSZW data were investigated in detail.

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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)

Cited by 24 publications

References 94 publications

Structure and Pattern Formation in Biological Liquid Crystals: Insights From Theory and Simulation of Self-Assembly and Self-Organization

Structure and Pattern Formation in Biological Liquid Crystals: Insights From Theory and Simulation of Self-Assembly and Self-Organization

A hybrid machine learning approach integrating recurrent neural networks with subspace identification for modelling HVAC systems

Comparison of the Nucleation Parameters of Aqueous l-glycine Solutions in the Presence of l-arginine from Induction Time and Metastable-Zone-Width Data

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