electrodes, whose capacitance changes under pressure due to the deformation of the dielectric layer. Although capacitive pressure sensors have some advantages including simple device structure and easy fabrication, they typically exhibit low sensitivity and also require more sophisticated readout circuits that can measure very small capacitance change (typically in the range of hundreds of femtofarad). Moreover, parasitic capacitance and crosstalk between the pixels could also lead to reduced sensitivity and spatial resolution. Piezoelectric materials such as polyvinylidene difluoride that can generate electrical charges from mechanical impact can also be used for pressure sensing. [19] However, such piezoelectric sensors are not suitable for measuring static pressure as they only respond to dynamic changes in pressure. Considering the above, resistive pressure sensors are more promising as they typically offer great sensitivity and only require very basic readout circuit that can measure resistance change. The resistive pressure sensors are typically made using thin films of conductors, such as nanocomposites [15] or nanowires, [16] whose electrical resistance changes under mechanical strain due to microscopic change in morphology or increase in distance between the conductive fillers. [21][22][23] For sensor fabrication, inkjet printing [24][25][26][27][28][29][30][31][32][33] has been widely used and the advantages are multifold. First, the printing process greatly simplifies the fabrication by completely eliminating the need for masks (used in photolithography), as well as high temperature or high vacuum processes commonly used in semiconductor microfabrication. Moreover, it is an additive and highly scalable process that can greatly reduce materials waste. For these reasons, the inkjet printing process could allow the sensors to be low-cost and potentially disposable. Many types of printed sensors including strain sensor, [29][30][31] temperature sensor, [24,32] and humidity sensor [27,28] have already been demonstrated.We have recently demonstrated the use of inkjet-printed silver nanoparticle (AgNP) pattern on an elastomer substrate as an ultrasensitive strain sensor. [31] Inspired by the capability of using printed AgNP thin film for strain sensing and its very high sensitivity, in this work, we demonstrate a printed resistive pressure sensor whose sensing mechanism is based on pressure-induced strain. The sensor consists of a conductive AgNP layer that is directly printed onto a polydimethylsiloxane (PDMS) substrate and subsequently encapsulated by Soft pressure sensors may find a wide range of applications in soft robotics, biomedical devices, and smart wearables. Here, an inkjet-printed resistive pressure sensor that offers high sensitivity and can be fabricated using a very simple process is reported. The device is composed of a conductive silver nanoparticle (AgNP) layer directly printed onto a polydimethylsiloxane substrate and encapsulated by a VHB tape. The pressure is measured through change in e...

There is an increasing interest in the development of memristive or artificial synaptic devices that emulate the neuronal activities for neuromorphic computing applications. While there have already been many reports on artificial synaptic transistors implemented on rigid substrates, the use of flexible devices could potentially enable an even broader range of applications. In this paper, we report artificial synaptic thinfilm transistors built on an ultrathin flexible substrate using high carrier mobility semiconducting single-wall carbon nanotubes. The synaptic characteristics of the flexible synaptic transistor including long-term/short-term plasticity, spikeamplitude-dependent plasticity, spike-width-dependent plasticity, paired-pulse facilitation, and spike-time-dependent plasticity have all been systematically characterized. Furthermore, we have demonstrated a flexible neurological electronic skin and its peripheral nerve with a flexible ferroelectret nanogenerator (FENG) serving as the sensory mechanoreceptor that generates action potentials to be processed and transmitted by the artificial synapse. In such neurological electronic skin, the flexible FENG sensor converts the tactile input (magnitude and frequency of force) into presynaptic action potential pulses, which are then passed to the gate of the synaptic transistor to induce change in its postsynaptic current, mimicking the modulation of synaptic weight in a biological synapse. Our neurological electronic skin closely imitates the behavior of actual human skin, and it allows for instantaneous detection of force stimuli and offers biological synapse-like behavior to relay the stimulus signals to the next stage. The flexible sensory skin could potentially be used to interface with skeletal muscle fibers for applications in neuroprosthetic devices.

As the initial stage in the formation of human intelligence, the sensory–memory system plays a critical role for human being to perceive, interact, and evolve with the environment. Electronic implementation of such biological sensory–memory system empowers the development of environment-interactive artificial intelligence (AI) that can learn and evolve with diversified external information, which could potentially broaden the application of the AI technology in the field of human–computer interaction. Here, we report a multimodal artificial sensory–memory system consisting of sensors for generating biomimetic visual, auditory, tactile inputs, and flexible carbon nanotube synaptic transistor that possesses synapse-like signal processing and memorizing behaviors. The transduction of physical signals into information-containing, presynaptic action potentials and the synaptic plasticity of the transistor in response to single and long-term action potential excitations have been systematically characterized. The bioreceptor-like sensing and synapse-like memorizing behaviors have also been demonstrated. On the basis of the memory and learning characteristics of the sensory–memory system, the well-known psychological model describing human memory, the “multistore memory” model, and the classical conditioning experiment that demonstrates the associative learning of brain, “Pavlov’s dog’s experiment”, have both been implemented electronically using actual physical input signals as the sources of the stimuli. The biomimetic intelligence demonstrated in this neurological sensory–memory system shows its potential in promoting the advancement in multimodal, user-environment interactive AI.

Soft wearable sensors are essential components for applications such as motion tracking, humanmachine interface, and soft robots. However, most of the reported sensors are either specifically designed to target an individual stimulus or capable of responding to multiple stimuli (e.g., pressure and strain) but without the necessary selectivity to distinguish those stimuli. Here we report an elastomeric sponge-based sensor that can respond to and distinguish three different kinds of stimuli: pressure, strain, and temperature. The sensor utilizes a porous polydimethylsiloxane (PDMS) sponge fabricated from a sugar cube sacrificial template, which was subsequently coated with a poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) conductive polymer through a low-cost dip-coating process. Responses to different types of stimuli can be

Halide perovskites have great potential for use in high‐performance light‐emitting diodes (LEDs) and displays. Here, a perovskite LEDs (PeLEDs) fabricated directly on an elastomer substrate, in which every single layer in the device from bottom anode to top cathode is patterned solely using a highly scalable inkjet printing process, is reported. Compared to PeLEDs made using conventional microfabrication processes, the printing process significantly shortens the fabrication time by at least tenfold (from over 5 h to less than 25 min). The all‐printed PeLEDs have a novel 4‐layer structure (bottom electrode, perovskite emissive layer, buffer layer, top electrode) without separate electron or hole transporting layers. For flexible PeLEDs printed directly in ambient conditions, a turn‐on voltage, maximum luminance intensity, and maximum current efficiency of 3.46 V, 10227 cd m−2, and 2.01 cd A−1, respectively, is achieved. The devices also exhibit excellent robustness and stability even when bent to a curvature radius of 2.5 mm. The reported device structure and fabrication processes can enable high‐performance flexible PeLEDs to be manufactured over a larger area at extremely low cost and fast speed, which can facilitate the adoption of the promising PeLED technology in the emerging foldable displays, smart wearables, and many other applications.