Despite 55 years of efforts into short gate length transistors following the Moore's law, the gate length below 1 nm has not been realized. Here, we demonstrated a side-wall monolayer MoS 2 transistors with ultimate 0.34 nm gate length using the edge of graphene as gate electrode. Moreover, large area of chemical vapor deposition graphene and MoS 2 are used for 2-inch wafer production. These ultrashort devices show excellent ON/OFF current ratio of 2 × 10 5 . Simulation results indicate that the MoS 2 sidewall effective channel length approaches 0.34 nm in the ON state. This graphene edge gate combined with MoS 2 vertical channel structure provides an e cient gate control ability and enables the physical gate length scaling down to atomic level, which shows great potential to build next generation electronics.

Realization of biological synapses using electronic devices is regarded as the basic building blocks for neuromorphic engineering and artificial neural network. With the advantages of biocompatibility, low cost, flexibility, and compatible with printing and roll-to-roll processes, the artificial synapse based on organic transistor is of great interest. In this paper, the artificial synapse simulation by ion-gel gated organic field-effect transistors (FETs) with poly(3-hexylthiophene) (P3HT) active channel is demonstrated. Key features of the synaptic behaviors, such as paired-pulse facilitation (PPF), short-term plasticity (STP), self-tuning, the spike logic operation, spatiotemporal dentritic integration, and modulation are successfully mimicked. Furthermore, the interface doping processes of electrolyte ions between the active P3HT layer and ion gels is comprehensively studied for confirming the operating processes underlying the conductivity and excitatory postsynaptic current (EPSC) variations in the organic synaptic devices. This study represents an important step toward building future artificial neuromorphic systems with newly emerged ion gel gated organic synaptic devices.

Flexible wearable sweat sensors allow continuous, real-time, noninvasive detection of sweat analytes, provide insight into human physiology at the molecular level, and have received significant attention for their promising applications in personalized health monitoring. Electrochemical sensors are the best choice for wearable sweat sensors due to their high performance, low cost, miniaturization, and wide applicability. Recent developments in soft microfluidics, multiplexed biosensing, energy harvesting devices, and materials have advanced the compatibility of wearable electrochemical sweat-sensing platforms. In this review, we summarize the potential of sweat for medical detection and methods for sweat stimulation and collection. This paper provides an overview of the components of wearable sweat sensors and recent developments in materials and power supply technologies and highlights some typical sensing platforms for different types of analytes. Finally, the paper ends with a discussion of the challenges and a view of the prospective development of this exciting field.

We report an artificial eardrum using an acoustic sensor based on two-dimensional MXene (Ti 3 C 2 T x ), which mimics the function of a human eardrum for realizing voice detection and recognition. Using MXene with a large interlayer distance and micropyramid polydimethylsiloxane arrays can enable a two-stage amplification of pressure and acoustic sensing. The MXene artificial eardrum shows an extremely high sensitivity of 62 kPa −1 and a very low detection limit of 0.1 Pa. Notably, benefiting from the ultrasensitive MXene eardrum, the machine-learning algorithm for real-time voice classification can be realized with high accuracy. The 280 voice signals are successfully classified for seven categories, and a high accuracy of 96.4 and 95% can be achieved by the training dataset and the test dataset, respectively. The current results indicate that the MXene artificial intelligent eardrum shows great potential for applications in wearable acoustical health care devices.

High-performance electromagnetic interference (EMI) shielding materials with ultralow density, excellent flexibility, and good mechanical properties are highly desirable for aerospace and wearable electronics. Herein, honeycomb porous graphene (HPG) fabricated by laser scribing technology is reported for EMI shielding and wearable applications. Due to the honeycomb structure, the HPG exhibits an EMI shielding effectiveness (SE) up to 45 dB at a thickness of 48.3 μm. The single-piece HPG exhibits an ultrahigh absolute shielding effectiveness (SSE/t) of 240 123 dB cm 2 /g with an ultralow density of 0.0388 g/cm 3 , which is significantly superior to the reported materials such as carbon-based, MXene, and metal materials. Furthermore, MXene and AgNWs are employed to cover the honeycomb holes of the HPG to enhance surface reflection; thus, the SSE/t of the HPG/AgNWs composite membrane can reach up to 292 754 dB cm 2 /g. More importantly, the HPG exhibits excellent mechanical stability and durability in cyclic stretching and bending, which can be used to monitor weak physiological signals such as pulse, respiration, and laryngeal movement of humans. Therefore, the lightweight and flexible HPG exhibits excellent EMI shielding performance and mechanical properties, along with its low cost and ease of mass production, which is promising for practical applications in EMI shielding and wearable electronics.

Single-crystal (SC) perovskite is currently a promising material due to its high quantum efficiency and long diffusion length. However, the reported perovskite photodetection range (<800 nm) and response time (>10 μs) are still limited. Here, to promote the development of perovskite-integrated optoelectronic devices, this work demonstrates wider photodetection range and shorter response time perovskite photodetector by integrating the SC CH3NH3PbBr3 (MAPbBr3) perovskite on silicon (Si). The Si/MAPbBr3 heterojunction photodetector with an improved interface exhibits high-speed, broad-spectrum, and long-term stability performances. To the best of our knowledge, the measured detectable spectrum (405–1064 nm) largely expands the widest response range reported in previous perovskite-based photodetectors. In addition, the rise time is as fast as 520 ns, which is comparable to that of commercial germanium photodetectors. Moreover, the Si/MAPbBr3 device can maintain excellent photocurrent performance for up to 3 months. Furthermore, typical gray scale face imaging is realized by scanning the Si/MAPbBr3 single-pixel photodetector. This work using an ultrafast photodetector by directly integrating perovskite on Si can promote advances in next-generation integrated optoelectronic technology.

Most mute people cannot speak due to their vocal cord lesion. Herein, to assist mute people to “speak”, we proposed a wearable skinlike ultrasensitive artificial graphene throat (WAGT) that integrated both sound/motion detection and sound emission in single device. In this work, the growth and patterning of graphene can be realized at the same time, and a thin poly­(vinyl alcohol) film with laser-scribed graphene was obtained by a water-assisted transferring process. In virtue of the skinlike and low-resistant substrate, the WAGT has a high detection sensitivity (relative resistance changes up to 150% at 133 Ω) and an excellent sound-emitting ability (up to 75 dB at 0.38 W power and 2 mm distance). On the basis of the excellent mechanical-electrical performance of graphene structure, the sound detecting and emitting mechanisms of WAGT are realized and discussed. For sound detection, both the motion of larynx and vibration of vocal cord contribute to throat movements. For sound emission, a thermal acoustic model for WAGT was established to reveal the principle of sound emitting. More importantly, a homemade circuit board was fabricated to build a dual-mode system, combining the detection and emitting systems. Meanwhile, different human motions, such as strong and small throat movements, were also detected and transformed into different sounds like “OK” and “NO”. Therefore, the implementation of these sound/motion detection acoustic systems enable graphene to achieve device-level applications to system-level applications, and those graphene acoustic systems are wearable for its miniaturization and light weight.

There is a growing need for developing machine learning applications. However, implementation of the machine learning algorithm consumes a huge number of transistors or memory devices on-chip. Developing a machine learning capability in a single device has so far remained elusive. Here, we build a Markov chain algorithm in a single device based on the native oxide of two dimensional multilayer tin selenide. After probing the electrical transport in vertical tin oxide/tin selenide/tin oxide heterostructures, two sudden current jumps are observed during the set and reset processes. Furthermore, five filament states are observed. After classifying five filament states into three states of the Markov chain, the probabilities between each states show convergence values after multiple testing cycles. Based on this device, we demo a fixed-probability random number generator within 5% error rate. This work sheds light on a single device as one hardware core with Markov chain algorithm.