High conductivity, large mechanical strength, and elongation are important parameters for soft electronic applications. However, it is difficult to find a material with balanced electronic and mechanical performance. Here, a simple method is developed to introduce ion-rich pores into strong hydrogel matrix and fabricate a novel ionic conductive hydrogel with a high level of electronic and mechanical properties. The proposed ionic conductive hydrogel is achieved by physically cross-linking the tough biocompatible polyvinyl alcohol (PVA) gel as the matrix and embedding hydroxypropyl cellulose (HPC) biopolymer fibers inside matrix followed by salt solution soaking. The wrinkle and dense structure induced by salting in PVA matrix provides large stress (1.3 MPa) and strain (975%). The well-distributed porous structure as well as ion migration-facilitated ion-rich environment generated by embedded HPC fibers dramatically enhances ionic conductivity (up to 3.4 S m −1 , at f = 1 MHz). The conductive hybrid hydrogel can work as an artificial nerve in a 3D printed robotic hand, allowing passing of stable and tunable electrical signals and full recovery under robotic hand finger movements. This natural rubber-like ionic conductive hydrogel has a promising application in artificial flexible electronics.

Sensory neurons within skin form an interface between the external physical reality and the inner tactile perception. This interface enables sensory information to be organized identified, and interpreted through perceptual learning-the process whereby the sensing abilities improve through experience. Here, an artificial sensory neuron that can integrate and differentiate the spatiotemporal features of touched patterns for recognition is shown. The system comprises sensing, transmitting, and processing components that are parallel to those found in a sensory neuron. A resistive pressure sensor converts pressure stimuli into electric signals, which are transmitted to a synaptic transistor through interfacial ionic/electronic coupling via a soft ionic conductor. Furthermore, the recognition error rate can be dramatically decreased from 44% to 0.4% by integrating with the machine learning method. This work represents a step toward the design and use of neuromorphic electronic skin with artificial intelligence for robotics and prosthetics.

X. (2020). Gesture recognition using a bioinspired learning architecture that integrates visual data with somatosensory data from stretchable sensors.

Soft and stretchable electronic devices are important in wearable and implantable applications because of the high skin conformability. Due to the natural biocompatibility and biodegradability, silk protein is one of the ideal platforms for wearable electronic devices. However, the realization of skin-conformable electronic devices based on silk has been limited by the mechanical mismatch with skin, and the difficulty in integrating stretchable electronics. Here, silk protein is used as the substrate for soft and stretchable on-skin electronics. The original high Young's modulus (5-12 GPa) and low stretchability (<20%) are tuned into 0.1-2 MPa and > 400%, respectively. This plasticization is realized by the addition of CaCl and ambient hydration, whose mechanism is further investigated by molecular dynamics simulations. Moreover, highly stretchable (>100%) electrodes are obtained by the thin-film metallization and the formation of wrinkled structures after ambient hydration. Finally, the plasticized silk electrodes, with the high electrical performance and skin conformability, achieve on-skin electrophysiological recording comparable to that by commercial gel electrodes. The proposed skin-conformable electronics based on biomaterials will pave the way for the harmonized integration of electronics into human.

Compared with traditional stimuli-responsive devices with simple planar or tubular geometries, 3D printed stimuli-responsive devices not only intimately meet the requirement of complicated shapes at macrolevel but also satisfy various conformation changes triggered by external stimuli at the microscopic scale. However, their development is limited by the lack of 3D printing functional materials. This paper demonstrates the 3D printing of photoresponsive shape memory devices through combining fused deposition modeling printing technology and photoresponsive shape memory composites based on shape memory polymers and carbon black with high photothermal conversion efficiency. External illumination triggers the shape recovery of 3D printed devices from the temporary shape to the original shape. The effect of materials thickness and light density on the shape memory behavior of 3D printed devices is quantified and calculated. Remarkably, sunlight also triggers the shape memory behavior of these 3D printed devices. This facile printing strategy would provide tremendous opportunities for the design and fabrication of biomimetic smart devices and soft robotics.