Integrating machine learning for the optimization of polyacrylamide/alginate hydrogel.

ABSTRACT: Hydrogels are highly promising due to their soft texture and excellent biocompatibility. However, the designation and optimization of hydrogels involve numerous experimental parameters, posing challenges in achieving rapid optimization through conventional experimental methods. In this study, we leverage machine learning algorithms to optimize a dual-network hydrogel based on a blend of acrylamide (AM) and alginate, targeting applications in flexible electronics. By treating the concentrations of components as experimental parameters and utilizing five material properties as evaluation criteria, we conduct a comprehensive property assessment of the material using a linear weighting method. Subsequently, we design a series of experimental plans using the Bayesian optimization algorithm and

SUBMITTER: Xu S 

PROVIDER: S-EPMC11422183 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature