Predictive design of stretchable electrodes with strain-insensitive performance via robotics- and machine learning-integrated workflow.

ABSTRACT: Innovations in wearable electronics and soft robotics hinge significantly on the development of stretchable electrodes. However, a persistent challenge lies in balancing high stretchability, functional performance, and strain insensitivity. Conventional approaches, such as design of experiments and trial-and-error methods, often rely on time-consuming and labor-intensive experiments to navigate a vast and complex parameter space. To overcome this, we establish an integrated workflow merging robot-automated experimentation, machine learning predictions, and finite element simulations to enable the predictive design of stretchable electrodes with strain-insensitive performance. Initially, we construct an ensemble of artificial neural networks through a two-stage workflow, including feasible

SUBMITTER: Yang H 

PROVIDER: S-EPMC12916966 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature