Data-driven multi-scale stability analysis of multi-stimuli-responsive hydrogels

Hydrogels are promising candidates for innovative biomedical applications such as in drug delivery or wound dressing. Next to being biocompatible, their most striking feature is that they show very large deformation of up to a few hundreds of percent. Usually, these deformations are driven by swelling during which solvent molecules diffuse through the hydrogel’s polymer network. Their applicability is however limited in situations where instantaneous and switchable deformations are required. One way to make hydrogels accessible for associated scenarios is to provide them with multi-stimuli-responsive properties such that they react to, e.g., electrical or magnetical fields. The present project aims at designing such multi-functional hydrogels that respond to coupled loading with controllable, large deformations via buckling-type structural instabilities. In a multi-stimuli-responsive setting, instabilities can be triggered in a collaborative way by the joint action of multi-physical loading. The goal of the present project is to design polymeric multi-stimuli-responsive hydrogels that show instability-induced large deformations under coupled loading conditions. Focus will be on the detection, prediction and data-driven exploitation of collaborative instabilities triggered by multi-physical means and combinations thereof. The analysis will be based on multi-field and multi-scale incremental variational principles that are to be combined with computational methods of homogenization and stability analysis. Ultimate goal is to develop a data-integrated machine-learning framework for predicting collaborative-instability-induced large deformations, giving direct access to materials design. Selected goals and challenges are as follows.