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.

Isothermal titration calorimetry: practical approaches and current applications in soft matter

Isothermal Titration Calorimetry (ITC) elucidates the thermodynamic profile (ΔH, ΔS, ΔG, Ka, and stoichiometry) of binding and dissociation reactions in solution. While ITC has primarily been used to investigate the thermodynamics of interactions between biological macromolecules and small molecules, it has become increasingly common for measuring binding interactions between synthetic polymers and small molecules, ions, or nanoparticles. This tutorial review describes applications of ITC in studying synthetic macromolecules and provides experimental guidelines for performing ITC experiments. We also highlight specific examples of using ITC to study soft matter, then discuss the limitations and the future of ITC in this field.