Project description
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.
Project information
Project title | Data-driven multi-scale stability analysis of multi-stimuli-responsive hydrogels |
Project leaders | Marc-André Keip (Tim Ricken, Felix Fritzen) |
Project staff | Elten Polukhov, postdoctoral researcher |
Project duration | July 2023 - December 2025 |
Project number | PN 3-5 (II) |
- Preceding project 3-5
Data-driven surrogate modeling of structural instabilities in electroactive polymers
Publications PN 3-5 and PN 3-5 (II)
2024
- A. Krischok, B. Yaraguntappa, and M.-A. Keip, “Fast implicit update schemes for Cahn–Hilliard-type gradient flow in the context of Fourier-spectral methods,” Computer Methods in Applied Mechanics and Engineering, vol. 431, p. 117220, 2024, doi: https://doi.org/10.1016/j.cma.2024.117220.
- S. Sriram, E. Polukhov, and M.-A. Keip, “Data-driven analysis of structural instabilities in electroactive polymer bilayers based on a variational saddle-point principle,” International Journal of Solids and Structures, vol. 291, p. 112663, Apr. 2024, doi: 10.1016/j.ijsolstr.2024.112663.
- L. Werneck, M. Han, E. Yildiz, M.-A. Keip, M. Sitti, and M. Ortiz, “A simple quantitative model of neuromodulation, Part I : Ion flow neural ion channels,” Journal of the mechanics and physics of solids, vol. 182, no. January, Art. no. January, 2024, doi: 10.1016/j.jmps.2023.105457.
2023
- E. Polukhov, L. Pytel, and M.-A. Keip, “Swelling-induced pattern transformations of periodic hydrogels : from the wrinkling of internal surfaces to the buckling of thin films,” Journal of the mechanics and physics of solids, vol. 175, no. June, Art. no. June, 2023, doi: 10.1016/j.jmps.2023.105250.
- A. Müller, M. Bischoff, and M.-A. Keip, “Thin cylindrical magnetic nanodots revisited : Variational formulation, accurate solution and phase diagram,” Journal of magnetism and magnetic materials, vol. 586, p. 171095, 2023, doi: 10.1016/j.jmmm.2023.171095.
2022
- A. Kanan, E. Polukhov, M.-A. Keip, L. Dorfmann, and M. Kaliske, “Computational material stability analysis in finite thermo-electro-mechanics,” Mechanics research communications, vol. 121, no. April, Art. no. April, 2022, doi: 10.1016/j.mechrescom.2022.103867.
2021
- S. Nirupama Sriram, E. Polukhov, and M.-A. Keip, “Transient stability analysis of composite hydrogel structures based on a minimization-type variational formulation,” International Journal of Solids and Structures, vol. 230–231, p. 111080, 2021, doi: https://doi.org/10.1016/j.ijsolstr.2021.111080.
- E. Polukhov and M.-A. Keip, “Multiscale stability analysis of periodic magnetorheological elastomers,” Mechanics of Materials, vol. 159, p. 103699, 2021, doi: https://doi.org/10.1016/j.mechmat.2020.103699.
2020
- L. T. K. Nguyen, M. Rambausek, and M.-A. Keip, “Variational framework for distance-minimizing method in data-driven computational mechanics,” Computer Methods in Applied Mechanics and Engineering, vol. 365, p. 112898, 2020, doi: https://doi.org/10.1016/j.cma.2020.112898.
- E. Polukhov and M.-A. Keip, “Computational homogenization of transient chemo-mechanical processes based on a variational minimization principle,” Advanced Modeling and Simulation in Engineering Sciences, vol. 7, no. 1, Art. no. 1, Jul. 2020, doi: 10.1186/s40323-020-00161-6.
2019
- F. S. Göküzüm, L. T. K. Nguyen, and M.-A. Keip, “An Artificial Neural Network Based Solution Scheme for Periodic Computational Homogenization of Electrostatic Problems,” Mathematical and Computational Applications, vol. 24, no. 2, Art. no. 2, Apr. 2019, doi: 10.3390/mca24020040.
Data and software publications PN 3-5 and PN 3-5 (II)
- L. Werneck, E. Yildiz, M. Han, M.-A. Keip, M. Sitti, and M. Ortiz, “Ion Flow Through Neural Ion Membrane: scripts and data,” 2023. doi: 10.18419/darus-3575.