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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.

Skin-like sensors that transduce tactile pressures and vibrations with minimal environment variation on performance are crucial in robotic sensing and prosthetic skins. However, sensor performance ...

Poly(dimethylsiloxane) (PDMS) as one of the electron-drawing materials has been widely used in triboelectric nanogenerators (TENG), which is expected to generate electron through friction and required to endure dynamic loads. However, the nature of the siloxane bond and the low interchain interaction between the methyl side groups result in low fracture energy in PDMS elastomers. Here, a strategy that combined the advantages of the dynamic of hierarchical hydrogen bonding and phase-separation-like structure was adopted to improve the toughness of PDMS elastomers. By varying both stronger and weaker hydrogen bonding within the PDMS network, a series of super tough (up to 24,000 J/m2), notch-insensitive, transparent, and autonomous self-healable elastomers were achieved. In addition, a hydrophilic polymeric material (PDMAS-U10) was synthesized as the conductive layer. A transparent TENG was fabricated by sandwiching the PDMAS-U10 between two pieces of the PDMS elastomer. Despite its hydrophilic nature, PDMAS-U10 exhibit strong adhesion interaction with hydrophobic PDMS elastomers. As such, a tough (16,500 J/m2), self-healable (efficiency ∼97%), and transparent triboelectric nanogenerator was constructed. A self-powered system employing the TENG is also demonstrated in this work.

Despite the ubiquity of poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) as a transparent conducting electrode in flexible organic electronic devices, its potential as a stretchable conductor has not been fully explored. This paper describes the electronic and morphological characteristics of PEDOT:PSS on stretchable poly(dimethylsiloxane) (PDMS) substrates. The evolution of resistance with strain depends dramatically on the methods used to coat the hydrophobic surface of PDMS with PEDOT:PSS, which is cast from an aqueous suspension. Treatment of the PDMS with an oxygen plasma produces a brittle skin that causes the PEDOT:PSS film to fracture and an increase in resistivity by four orders of magnitude at only 10% strain. In contrast, a mild treatment of the PDMS surface with ultraviolet/ozone (UV/O3) and the addition of 1% Zonyl fluorosurfactant to the PEDOT:PSS solution produces a mechanically resilient film whose resistance increases by a factor of only two at 50% strain and retains si...

Skin is the body's largest organ and is responsible for the transduction of a vast amount of information. This conformable material simultaneously collects signals from external stimuli that translate into information such as pressure, pain, and temperature. The development of an electronic material, inspired by the complexity of this organ is a tremendous, unrealized engineering challenge. However, the advent of carbon-based electronics may offer a potential solution to this long-standing problem. In this Account, we describe the use of an organic field-effect transistor (OFET) architecture to transduce mechanical and chemical stimuli into electrical signals. In developing this mimic of human skin, we thought of the sensory elements of the OFET as analogous to the various layers and constituents of skin. In this fashion, each layer of the OFET can be optimized to carry out a specific recognition function. The separation of multimodal sensing among the components of the OFET may be considered a "divide and conquer" approach, where the electronic skin (e-skin) can take advantage of the optimized chemistry and materials properties of each layer. This design of a novel microstructured gate dielectric has led to unprecedented sensitivity for tactile pressure events. Typically, pressure-sensitive components within electronic configurations have suffered from a lack of sensitivity or long mechanical relaxation times often associated with elastomeric materials. Within our method, these components are directly compatible with OFETs and have achieved the highest reported sensitivity to date. Moreover, the tactile sensors operate on a time scale comparable with human skin, making them ideal candidates for integration as synthetic skin devices. The methodology is compatible with large-scale fabrication and employs simple, commercially available elastomers. The design of materials within the semiconductor layer has led to the incorporation of selectivity and sensitivity within gas-sensing devices and has enabled stable sensor operation within aqueous media. Furthermore, careful tuning of the chemical composition of the dielectric layer has provided a means to operate the sensor in real time within an aqueous environment and without the need for encapsulation layers. The integration of such devices as electronic mimics of skin will require the incorporation of biocompatible or biodegradable components. Toward this goal, OFETs may be fabricated with99% biodegradable components by weight, and the devices are robust and stable, even in aqueous environments. Collectively, progress to date suggests that OFETs may be integrated within a single substrate to function as an electronic mimic of human skin, which could enable a large range of sensing-related applications from novel prosthetics to robotic surgery.