Emulation of biological synapses is necessary for future brain-inspired neuromorphic computational systems that could look beyond the standard von Neuman architecture. Here, artificial synapses based on ionic-electronic hybrid oxide-based transistors on rigid and flexible substrates are demonstrated. The flexible transistors reported here depict a high field-effect mobility of ≈9 cm V s with good mechanical performance. Comprehensive learning abilities/synaptic rules like paired-pulse facilitation, excitatory and inhibitory postsynaptic currents, spike-time-dependent plasticity, consolidation, superlinear amplification, and dynamic logic are successfully established depicting concurrent processing and memory functionalities with spatiotemporal correlation. The results present a fully solution processable approach to fabricate artificial synapses for next-generation transparent neural circuits.

Highly sensitive and multimodal sensors have recently emerged for a wide range of applications, including epidermal electronics, robotics, health‐monitoring devices and human–machine interfaces. However, cross‐sensitivity prevents accurate measurements of the target input signals when a multiple of them are simultaneously present. Therefore, the selection of the multifunctional materials and the design of the sensor structures play a significant role in multimodal sensors with decoupled sensing mechanisms. Hence, this review article introduces varying methods to decouple different input signals for realizing truly multimodal sensors. Early efforts explore different outputs to distinguish the corresponding input signals applied to the sensor in sequence. Next, this study discusses the methods for the suppression of the interference, signal correction, and various decoupling strategies based on different outputs to simultaneously detect multiple inputs. The recent insights into the materials' properties, structure effects, and sensing mechanisms in recognition of different input signals are highlighted. The presence of the various decoupling methods also helps avoid the use of complicated signal processing steps and allows multimodal sensors with high accuracy for applications in bioelectronics, robotics, and human–machine interfaces. Finally, current challenges and potential opportunities are discussed in order to motivate future technological breakthroughs.

Though the widely available, low-cost, and disposable papers have been explored in flexible paper-based pressure sensors, it is still difficult for them to simultaneously achieve ultrahigh sensitivity, low limit and broad range of detection, and high-pressure resolution. Herein, we demonstrate a novel flexible paper-based pressure sensing platform that features the MXene-coated tissue paper (MTP) sandwiched between a polyimide encapsulation layer and a printing paper with interdigital electrodes. After replacing the polyimide with weighing paper in the MTP pressure sensor, the silver interdigital electrodes can be recycled through incineration. The resulting pressure sensor with polyimide or paper encapsulation exhibits a high sensitivity of 509.5 or 344.0 kPa–1, a low limit (∼1 Pa) and a broad range (100 kPa) of detection, and outstanding stability over 10 000 loading/unloading cycles. With ultrahigh sensitivity over a wide pressure range, the flexible pressure sensor can monitor various physiological signals and human movements. Configuring the pressure sensors into an array layout results in a smart artificial electronic skin to recognize the spatial pressure distribution. The flexible pressure sensor can also be integrated with signal processing and wireless communication modules on a face mask as a remote respiration monitoring system to wirelessly detect various respiration conditions and respiratory abnormalities for early self-identification of opioid overdose, pulmonary fibrosis, and other cardiopulmonary diseases.

Ultralow power dual-gated subthreshold oxide neuristors : an enabler for higher order neuronal temporal correlations

Electronic skins need to be versatile and able to detect multiple inputs beyond simple pressure and touch while having attributes of transparency and facile manufacturability. Herein, we demonstrate a versatile nanostructured transparent sensor capable of detecting wide range of pressures and proximity as well as novel nonoptical detection of printed patterns. The architecture and fabrication processes are straightforward and show robustness to repeated cycling and testing. The sensor displays good sensitivity and stability from 30 Pa to 5 kPa without the use of microstructuration and is conformal and sensitive to be utilized as a wrist-based heart-rate monitor. Highly sensitive proximity detection is shown from a distance of 9 cm. Finally, a unique nonoptical pattern recognition dependent on the difference in the dielectric constant between ink and paper is also demonstrated, indicating the multifunctionality of this simple architecture.

Sensory information processing in robot skins currently rely on a centralized approach where signal transduction (on the body) is separated from centralized computation and decisionmaking, requiring the transfer of large amounts of data from periphery to central processors, at the cost of wiring, latency, fault tolerance and robustness. We envision a decentralized approach where intelligence is embedded in the sensing nodes, using a unique neuromorphic methodology to extract relevant information in robotic skins. Here we specifically address pain perception and the association of nociception with tactile perception to trigger the escape reflex in a sensorized robotic arm. The proposed system comprises self-healable materials and memtransistors as enabling technologies for the implementation of neuromorphic nociceptors, spiking local associative learning and communication. Configuring memtransistors as gated-threshold and-memristive switches, the demonstrated system features in-memory edge computing with minimal hardware circuitry and wiring, and enhanced fault tolerance and robustness.

In the presented work, a significant increase in the tensile strength of the PVA composite material is reported. The obtained best value of 122 MPa and 14.6% swelling shows the excellent synergistic effect of both crosslinker and reinforcement material. The composite films were prepared by simple mechanical dispersion of reinforcement material, bacterial cellulose nanowhiskers (BCNW) into PVA solution followed by crosslinking with diacids, succinic acid (SuA), and adipic acid (AdA). The effect of aliphatic carbon chain length of crosslinker on thermal, mechanical, and water uptake properties is evaluated and discussed in detail. Neat PVA had the strength of 37.3 MPa. With 5% reinforcement of BCNW, that is, without crosslinking, it exhibited 97% increase, that is, 74.5 MPa. With crosslinking of 15 mmol of SuA and AdA PVA films for 2 h had excellent thermal properties with swelling percentage of 19 and 26.6% and tensile strength of 103 and 67 MPa, respectively. The best result obtained was for 5% BCNW–PVA films crosslinked with 15 mmol SuA. These results were explained on the basis of synergetic crosslinking and extended hydrogen bonding between PVA and reinforcement material. The composite films can be used as biological implants, a membrane for pervaporation and filtration system, etc. © 2016 Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem. 2016, 54, 2515–2525

Soft robotics focuses on mimicking natural systems to produce dexterous motion. Dielectric elastomer actuators (DEAs) are an attractive option due to their large strains, high efficiencies, lightweight design, and integrability, but require high electric fields. Conventional approaches to improve DEA performance by incorporating solid fillers in the polymer matrices can increase the dielectric constant but to the detriment of mechanical properties. In the present work, we draw inspiration from soft and deformable human skin, enabled by its unique structure, which consists of a fluid-filled membrane, to create self-enclosed liquid filler (SELF)–polymer composites by mixing an ionic liquid into the elastomeric matrix. Unlike hydrogels and ionogels, the SELF–polymer composites are made from immiscible liquid fillers, selected based on interfacial interaction with the elastomer matrix, and exist as dispersed globular phases. This combination of structure and filler selection unlocks synergetic improvements in electromechanical propertiesdoubling of dielectric constant, 100 times decrease in Young’s modulus, and ∼5 times increase in stretchability. These composites show superior thermal stability to volatile losses, combined with excellent transparency. These ultrasoft high-k composites enable a significant improvement in the actuation performance of DEAslongitudinal strain (5 times) and areal strain (8 times)at low applied nominal electric fields (4 V/μm). They also enable high-sensitivity capacitive pressure sensors without the need of miniaturization and microstructuring. This class of self-enclosed ionic liquid polymer composites could impact the areas of soft robotics, shape morphing, flexible electronics, and optoelectronics.