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Deep neural networks for large deformation of photo-thermo-pH responsive cationic gels

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Abstract In this work, a model is developed to analyze homogeneous and inhomogeneous large deformation of photo-thermo-pH responsive cationic gels. Constitutive equations are achieved by considering the equilibrium thermodynamics of… Click to show full abstract

Abstract In this work, a model is developed to analyze homogeneous and inhomogeneous large deformation of photo-thermo-pH responsive cationic gels. Constitutive equations are achieved by considering the equilibrium thermodynamics of swelling gels through variational method. Employing this model, coupling effects of light intensity, temperature and pH variations on large deformation of gels are analyzed. The simulation results are compared with available experimental data. Then deep neural networks are developed to approximate solutions to equilibrium equations of inhomogeneous swelling of spherical shell structure gels. The volume phase transition temperature of the gels and their dependence on light intensity are also demonstrated.

Keywords: photo thermo; large deformation; deformation; responsive cationic; thermo responsive; deformation photo

Journal Title: Applied Mathematical Modelling
Year Published: 2021

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