Data-Driven\nFramework for the Prediction of PEGDA\nHydrogel Mechanics

Poly(ethylene glycol) diacrylate (PEGDA) hydrogels are\nbiocompatible\nand photo-cross-linkable, with accessible values of elastic modulus\nranging from kPa to MPa, leading to their wide use in biomedical and\nsoft material applications. However, PEGDA gels possess complex microstructures,\nlimiting the use of standard polymer theories to describe them. As\na result, we lack a foundational understanding of how to relate their\ncomposition, processing, and mechanical properties. To address this\nneed, we use a data-driven approach to develop an empirical predictive\nframework based on high-quality data obtained from uniaxial compression\ntests and validated using prior data found in the literature. The\ndeveloped framework accurately predicts the hydrogel shear modulus\nand the strain-stiffening coefficient using only synthesis parameters,\nsuch as the molecular weight and initial concentration of PEGDA, as\ninputs. These results provide simple and reliable experimental guidelines\nfor precisely controlling both the low-strain and high-strain mechanical\nresponses of PEGDA hydrogels, thereby facilitating their design for\nvarious applications.