Dynamic Modeling of Intrinsic Self-Healing Polymers Using Deep Learning.

The properties of self-healing polymers are traditionally identified through destructive testing. This means that the mechanics are explored in hindsight with either theoretical derivations and/or simulations. Here, a self-healing property evolution using energy functional dynamical (SPEED) model is proposed to predict and understand the mechanics of self-healing of polymers using images of cuts dynamically healing over time. Using machine learning, an energy functional minimization (EFM) model extracted an effective underlying dynamical system from a time series of two-dimensional cut images on a self-healing polymer of constant thickness. This model can be used to capture the physics behind the self-healing dynamics in terms of potential and interface energies. When combined with a static property prediction model, the SPEED model can predict the macroscopic evolution of material properties after training only on a small set of experimental measurements. Such temporal evolutions are usually inaccessible from pure experiments or computational modeling due to the need for destructive testing. As an example, we validate this approach on toughness measurements of an intrinsic self-healing conductive polymer by capturing over 100 000 image frames of cuts to build the machine learning (ML) model. The results show that the SPEED model can be applied to predict the temporal evolution of macroscopic properties using few measurements as training data.

Progress and Roadmap for Intelligent Self-Healing Materials in Autonomous Robotics.

Robots are increasingly assisting humans in performing various tasks. Like special agents with elite skills, they can venture to distant locations and adverse environments, such as the deep sea and outer space. Micro/nanobots can also act as intrabody agents for healthcare applications. Self-healing materials that can autonomously perform repair functions are useful to address the unpredictability of the environment and the increasing drive toward the autonomous operation. Having self-healable robotic materials can potentially reduce costs, electronic wastes, and improve a robot endowed with such materials longevity. This review aims to serve as a roadmap driven by past advances and inspire future cross-disciplinary research in robotic materials and electronics. By first charting the history of self-healing materials, new avenues are provided to classify the various self-healing materials proposed over several decades. The materials and strategies for self-healing in robotics and stretchable electronics are also reviewed and discussed. It is believed that this article encourages further innovation in this exciting and emerging branch in robotics interfacing with material science and electronics.