A Large-Scale and Low-Cost Thermoacoustic Loudspeaker Based on Three-Dimensional Graphene Foam.

Graphene is a promising material for thermoacoustic sources due to its extremely low heat capacity per unit area and high thermal conductivity. However, current graphene thermoacoustic devices have limited device area and relatively high cost, which limit their applications of daily use. Here, we adopt a dip-coating method to fabricate a large-scale and cost-effective graphene sound source. This sound source has the three-dimensional (3D) porous structure that can increase the contact area between graphene and air, thus assisting heat to release into the air. In this method, polyurethane (PU) is used as a support, and graphene nanoplates are attached onto the PU skeleton so that a highly flexible graphene foam (GrF) device is obtained. At a measuring distance of 1 mm, it can emit sound at up to 70 dB under the normalized input power of 1 W. Considering its unique porous structure, we establish a thermoacoustic analysis model to simulate the acoustic performance of GrF. Furthermore, the obtained GrF can be made up to 44 in. (100 cm × 50 cm) in size, and it has good flexibility and processability, which broadens the application fields of GrF loudspeakers. It can be attached to the surfaces of objects with different shapes, making it suitable to be used as a large-area speaker in automobiles, houses, and other application scenarios, such as neck mounted speaker. In addition, it can also be widely used as a fully flexible in-ear earphone.

The Roadmap of 2D Materials and Devices Toward Chips.

Due to the constraints imposed by physical effects and performance degradation, silicon-based chip technology is facing certain limitations in sustaining the advancement of Moore's law. Two-dimensional (2D) materials have emerged as highly promising candidates for the post-Moore era, offering significant potential in domains such as integrated circuits and next-generation computing. Here, in this review, the progress of 2D semiconductors in process engineering and various electronic applications are summarized. A careful introduction of material synthesis, transistor engineering focused on device configuration, dielectric engineering, contact engineering, and material integration are given first. Then 2D transistors for certain electronic applications including digital and analog circuits, heterogeneous integration chips, and sensing circuits are discussed. Moreover, several promising applications (artificial intelligence chips and quantum chips) based on specific mechanism devices are introduced. Finally, the challenges for 2D materials encountered in achieving circuit-level or system-level applications are analyzed, and potential development pathways or roadmaps are further speculated and outlooked.

Laser-Induced Skin-like Flexible Pressure Sensor for Artificial Intelligence Speech Recognition.

Skin-like flexible pressure sensors with good sensing performance have great application potential, but their development is limited owing to the need for multistep, high-cost, and low-efficiency preparation processes. Herein, a simple, low-cost, and efficient laser-induced forming process is proposed for the first time to prepare a skin-like flexible piezoresistive sensor. In the laser-induced forming process, based on the photothermal effect of graphene and the foaming effect of glucose, a skin-like polydimethylsiloxanes (PDMS) film with porous structures and surface protrusions is obtained by using infrared laser irradiation of the glucose/graphene/PDMS prepolymer film. Further, based on the skin-like PDMS film with a graphene conductive layer, a new skin-like flexible piezoresistive sensor is obtained. Due to the stress concentration caused by the surface protrusions and the low stiffness caused by the porous structures, the flexible piezoresistive sensor realizes an ultrahigh sensitivity of 1348 kPa-1 at 0-2 kPa, a wide range of 200 kPa, a fast response/recovery time of 52 ms/35 ms, and good stability over 5000 cycles. The application of the sensor to the detection of human pulses and robot clamping force indicates its potential for health monitoring and soft robots. Furthermore, in combination with the neural network (CNN) algorithm in artificial intelligence technology, the sensor achieves 95% accuracy in speech recognition, which demonstrates its great potential for intelligent wearable electronics. Especially, the laser-induced forming process is expected to facilitate the efficient, large-scale preparation of flexible devices with multilevel structures.

Programmable Ferroelectricity in Hf0.5Zr0.5O2 Enabled by Oxygen Defect Engineering.

Ferroelectricity, especially the Si-compatible type recently observed in hafnia-based materials, is technologically useful for modern memory and logic applications, but it is challenging to differentiate intrinsic ferroelectric polarization from the polar phase and oxygen vacancy. Here, we report electrically controllable ferroelectricity in a Hf0.5Zr0.5O2-based heterostructure with Sr-doped LaMnO3, a mixed ionic-electronic conductor, as an electrode. Electrically reversible extraction and insertion of an oxygen vacancy into Hf0.5Zr0.5O2 are macroscopically characterized and atomically imaged in situ. Utilizing this reversible process, we achieved multilevel polarization states modulated by the electric field. Our study demonstrates the usefulness of the mixed conductor to repair, create, manipulate, and utilize advanced ferroelectric functionality. Furthermore, the programmed ferroelectric heterostructures with Si-compatible doped hafnia are desirable for the development of future ferroelectric electronics.

Graphene-based Two-Stage Enhancement Pressure Sensor for Subtle Mechanical Force Monitoring.

The development of pressure sensors with high sensitivity and a low detection limit for subtle mechanical force monitoring and the understanding of the sensing mechanism behind subtle mechanical force monitoring are of great significance for intelligent technology. Here, we proposed a graphene-based two-stage enhancement pressure sensor (GTEPS), and we analyzed the difference between subtle mechanical force monitoring and conventional mechanical force monitoring. The GTEPS exhibited a high sensitivity of 62.2 kPa-1 and a low detection limit of 0.1 Pa. Leveraging its excellent performance, the GTEPS was successfully applied in various subtle mechanical force monitoring applications, including acoustic wave detection, voice-print recognition, and pulse wave monitoring. In acoustic wave detection, the GTEPS achieved a 100% recognition accuracy for six words. In voiceprint recognition, the sensor exhibited accurate identification of distinct voiceprints among individuals. Furthermore, in pulse wave monitoring, GTEPS demonstrated effective detection of pulse waves. By combination of the pulse wave signals with electrocardiogram (ECG) signals, it enabled the assessment of blood pressure. These results demonstrate the excellent performance of GTEPS and highlight its great potential for subtle mechanical force monitoring and its various applications. The current results indicate that GTEPS shows great potential for applications in subtle mechanical force monitoring.

Polarized Tunneling Transistor for Ultralow-Energy-Consumption Artificial Synapse toward Neuromorphic Computing.

Neural networks based on low-power artificial synapses can significantly reduce energy consumption, which is of great importance in today's era of artificial intelligence. Two-dimensional (2D) material-based floating-gate transistors (FGTs) have emerged as compelling candidates for simulating artificial synapses owing to their multilevel and nonvolatile data storage capabilities. However, the low erasing/programming speed of FGTs renders them unsuitable for low-energy-consumption artificial synapses, thereby limiting their potential in high-energy-efficient neuromorphic computing. Here, we introduce a FGT-inspired MoS2/Trap/PZT heterostructure-based polarized tunneling transistor (PTT) with a simple fabrication process and significantly enhanced erasing/programming speed. Distinct from the FGT, the PTT lacks a tunnel layer, leading to a marked improvement in its erasing/programming speed. The PTT's highest erasing/programming (operation) speed can reach ∼20 ns, which outperforms the performance of most FGTs based on 2D heterostructures. Furthermore, the PTT has been utilized as an artificial synapse, and its weight-update energy consumption can be as low as 0.0002 femtojoule (fJ), which benefits from the PTT's ultrahigh operation speed. Additionally, PTT-based artificial synapses have been employed in constructing artificial neural network simulations, achieving facial-recognition accuracy (95%). This groundbreaking work makes it possible for fabricating future high-energy-efficient neuromorphic transistors utilizing 2D materials.

PZT-Enabled MoS2 Floating Gate Transistors: Overcoming Boltzmann Tyranny and Achieving Ultralow Energy Consumption for High-Accuracy Neuromorphic Computing.

Low-power electronic devices play a pivotal role in the burgeoning artificial intelligence era. The study of such devices encompasses low-subthreshold swing (SS) transistors and neuromorphic devices. However, conventional field-effect transistors (FETs) face the inherent limitation of the "Boltzmann tyranny", which restricts SS to 60 mV decade-1 at room temperature. Additionally, FET-based neuromorphic devices lack sufficient conductance states for highly accurate neuromorphic computing due to a narrow memory window. In this study, we propose a pioneering PZT-enabled MoS2 floating gate transistor (PFGT) configuration, demonstrating a low SS of 46 mV decade-1 and a wide memory window of 7.2 V in the dual-sweeping gate voltage range from -7 to 7 V. The wide memory window provides 112 distinct conductance states for PFGT. Moreover, the PFGT-based artificial neural network achieves an outstanding facial-recognition accuracy of 97.3%. This study lays the groundwork for the development of low-SS transistors and highly energy efficient artificial synapses utilizing two-dimensional materials.

Landauer-QFLPS Model for Mixed Schottky-Ohmic Contact Two-Dimensional Transistors.

Two-dimensional material-based field-effect transistors (2DM-FETs) are playing a revolutionary role in electronic devices. However, before electronic design automation (EDA) for 2DM-FETs can be achieved, it remains necessary to determine how to incorporate contact transports into model. Reported methods compromise between physical intelligibility and model compactness due to the heterojunction nature. To address this, quasi-Fermi-level phase space theory (QFLPS) is generalized to incorporate contact transports using the Landauer formula. It turns out that the Landauer-QFLPS model effectively overcomes the issue of concern. The proposed new formula can describe 2DM-FETs with Schottky or Ohmic contacts with superior accuracy and efficiency over previous methods, especially when describing non-monotonic drain conductance characteristics. A three-bit threshold inverter quantizer (TIQ) circuit is fabricated using ambipolar black phosphorus and it is demonstrated that the model accurately predicts circuit performance. The model could be very effective and valuable in the development of 2DM-FET-based integrated circuits.

Macroscopic negative differential thermal resistance in the overlapping graphene homojunction structure.

As one of the most potential ways to manipulate heat, thermal functional devices have achieved several breakthroughs in recent years, but are still limited to theoretical simulations. One of its theoretical bases is the existence of the negative differential thermal resistance (NDTR). However, most of the existing systems where the phenomenon of NDTR is found are atomic-level systems. In order to realize the macroscopic NDTR and provide effective theoretical guidance and support for the practical realization of thermal functional devices, we construct the overlapping graphene homojunction model, using the negative thermal expansion property of graphene to modify the overlapping area, and thus regulating the heat flow. The COMSOL-MATLAB co-simulation is used to perform calculations through negative feedback loops. It is found that the NDTR phenomenon exists under certain parameter conditions, which can provide new ideas and bring more opportunities for the experimental realization of nonlinear thermal functional devices.

Straight Manipulation Annealing in a Solvent Atmosphere for Quality-Improved Cs2AgBiBr6 Perovskites.

As a new-generation photoelectric material, perovskites have attracted researchers' attention due to their excellent optoelectronic properties. However, the existence of defects inevitably causes structural degradation and restricts their performance, which need to be further improved by post-treatment. At present, post-treatments mostly focus on non-contact treatments, which may constrain the effect since the influence on the perovskites caused by the direct contact is much more straightly. Therefore, we proposed an annealing strategy of straight manipulation in a solvent atmosphere with the assistance of polyimide (PI) tape for the perovskite post-treatment, due to the high heat resistance and less glue residual of this tape. It casts an influence on the perovskite directly, proving the possibility of the straight manipulation by operators, promoting the recrystallization of the perovskite grains and removing the impurity substance. The optimized Pb-free perovskite film exhibits a better X-ray sensitivity of 7.5 × 104 µC Gyair-1 cm-2 and a great detection limit of 47 nGyair s-1, which is comparable to advanced Pb-based perovskite X-ray detectors and all commercial ones. The new annealing strategy provides a facile, effective, and simple method to improve the perovskite quality, exhibiting the potential and harmlessness of the direct contact post-treatment, which paves the way for a broader application of perovskites.

Polarized Tunneling Transistor for Ultrafast Memory.

In today's information age, high performance nonvolatile memory devices have become extremely important. Despite their potential, existing devices suffer from limitations, such as low operation speed, low memory capacity, short retention time, and a complex preparation process. To overcome these limitations, advanced memory designs are required to improve speed, memory capacity, and retention time and reduce the number of preparation steps. Here, we present a nonvolatile floating-gate-like memory device based on a transistor that uses the polarization effect of ferroelectric material PZT (Pb[Zr0.2Ti0.8]O3) for regulating tunneling electrons for charging and discharging the MoS2 channel layer. The transistor is defined as a polarized tunneling transistor (PTT) and does not require a tunnel layer or a floating-gate layer. The PTT demonstrates an ultrafast programming/erasing speed of 25/20 ns and a response time of 120/105 ns, which is comparable to the ultrafast flash memories based on van der Waals heterostructures. Additionally, the PTT has a high extinction ratio of 104, a long retention time of 10 years, and a simple fabrication process. Our research provides future guidelines for the development of the next generation of ultrafast nonvolatile memory devices.

Recent progress in flexible micro-pressure sensors for wearable health monitoring.

In recent years, flexible micro-pressure sensors have been used widely in wearable health monitoring applications due to their excellent flexibility, stretchability, non-invasiveness, comfort wearing and real-time detection. According to the working mechanism of the flexible micro-pressure sensor, it can be classified as piezoresistive, piezoelectric, capacitive and triboelectric types. Herein, an overview of flexible micro-pressure sensors for wearable health monitoring is presented. The physiological signaling and body motions contain a lot of health status information. Thus, this review focuses on the applications of flexible micro-pressure sensors in these fields. Additionally, the contents of sensing mechanism, sensing materials and performance of flexible micro-pressure sensors are introduced in detail. Finally, we predict the future research directions of the flexible micro-pressure sensors, and discuss the challenges in practical applications.

Cellular automata imbedded memristor-based recirculated logic in-memory computing.

Memristor-based circuits offer low hardware costs and in-memory computing, but full-memristive circuit integration for different algorithm remains limited. Cellular automata (CA) has been noticed for its well-known parallel, bio-inspired, computational characteristics. Running CA on conventional chips suffers from low parallelism and high hardware costs. Establishing dedicated hardware for CA remains elusive. We propose a recirculated logic operation scheme (RLOS) using memristive hardware and 2D transistors for CA evolution, significantly reducing hardware complexity. RLOS's versatility supports multiple CA algorithms on a single circuit, including elementary CA rules and more complex majority classification and edge detection algorithms. Results demonstrate up to a 79-fold reduction in hardware costs compared to FPGA-based approaches. RLOS-based reservoir computing is proposed for edge computing development, boasting the lowest hardware cost (6 components/per cell) among existing implementations. This work advances efficient, low-cost CA hardware and encourages edge computing hardware exploration.

Lead-Free Halide Perovskites for Direct X-Ray Detectors.

Lead halide perovskites have made remarkable progress in the field of radiation detection owing to the excellent and unique optoelectronic properties. However, the instability and the toxicity of lead-based perovskites have greatly hindered its practical applications. Alternatively, lead-free perovskites with high stability and environmental friendliness thus have fascinated significant research attention for direct X-ray detection. In this review, the current research progress of X-ray detectors based on lead-free halide perovskites is focused. First, the synthesis methods of lead-free perovskites including single crystals and films are discussed. In addition, the properties of these materials and the detectors, which can provide a better understanding and designing satisfactory devices are also presented. Finally, the challenge and outlook for developing high-performance lead-free perovskite X-ray detectors are also provided.

Stretchable Ink Printed Graphene Device with Weft-Knitted Fabric Substrate Based on Thermal-Acoustic Effect.

Thermal-acoustic devices have great potential as flexible ultrathin sound sources. However, stretchable sound sources based on a thermal-acoustic mechanism remain elusive, as realizing stable resistance in a reasonable range is challenging. In this study, a stretchable thermal-acoustic device based on graphene ink is fabricated on a weft-knitted fabric. After optimization of the graphene ink concentration, the device resistance changes by 8.94% during 4000 cycles of operation in the unstretchable state. After multiple cycles of bending, folding, prodding, and washing, the sound pressure level (SPL) change of the device is within 10%. Moreover, the SPL has an increase with the strain in a specific range, showing a phenomenon similar to the negative differential resistance (NDR) effect. This study sheds light on the use of stretchable thermal-acoustic devices for e-skin and wearable electronics.

Biocompatible and Long-Term Monitoring Strategies of Wearable, Ingestible and Implantable Biosensors: Reform the Next Generation Healthcare.

Sensors enable the detection of physiological indicators and pathological markers to assist in the diagnosis, treatment, and long-term monitoring of diseases, in addition to playing an essential role in the observation and evaluation of physiological activities. The development of modern medical activities cannot be separated from the precise detection, reliable acquisition, and intelligent analysis of human body information. Therefore, sensors have become the core of new-generation health technologies along with the Internet of Things (IoTs) and artificial intelligence (AI). Previous research on the sensing of human information has conferred many superior properties on sensors, of which biocompatibility is one of the most important. Recently, biocompatible biosensors have developed rapidly to provide the possibility for the long-term and in-situ monitoring of physiological information. In this review, we summarize the ideal features and engineering realization strategies of three different types of biocompatible biosensors, including wearable, ingestible, and implantable sensors from the level of sensor designing and application. Additionally, the detection targets of the biosensors are further divided into vital life parameters (e.g., body temperature, heart rate, blood pressure, and respiratory rate), biochemical indicators, as well as physical and physiological parameters based on the clinical needs. In this review, starting from the emerging concept of next-generation diagnostics and healthcare technologies, we discuss how biocompatible sensors revolutionize the state-of-art healthcare system unprecedentedly, as well as the challenges and opportunities faced in the future development of biocompatible health sensors.

Wearable Continuous Blood Pressure Monitoring Devices Based on Pulse Wave Transit Time and Pulse Arrival Time: A Review.

Continuous blood pressure (BP) monitoring is of great significance for the real-time monitoring and early prevention of cardiovascular diseases. Recently, wearable BP monitoring devices have made great progress in the development of daily BP monitoring because they adapt to long-term and high-comfort wear requirements. However, the research and development of wearable continuous BP monitoring devices still face great challenges such as obvious motion noise and slow dynamic response speeds. The pulse wave transit time method which is combined with photoplethysmography (PPG) waves and electrocardiogram (ECG) waves for continuous BP monitoring has received wide attention due to its advantages in terms of excellent dynamic response characteristics and high accuracy. Here, we review the recent state-of-art wearable continuous BP monitoring devices and related technology based on the pulse wave transit time; their measuring principles, design methods, preparation processes, and properties are analyzed in detail. In addition, the potential development directions and challenges of wearable continuous BP monitoring devices based on the pulse wave transit time method are discussed.

Recent Progress in Long-Term Sleep Monitoring Technology.

Sleep is an essential physiological activity, accounting for about one-third of our lives, which significantly impacts our memory, mood, health, and children's growth. Especially after the COVID-19 epidemic, sleep health issues have attracted more attention. In recent years, with the development of wearable electronic devices, there have been more and more studies, products, or solutions related to sleep monitoring. Many mature technologies, such as polysomnography, have been applied to clinical practice. However, it is urgent to develop wearable or non-contacting electronic devices suitable for household continuous sleep monitoring. This paper first introduces the basic knowledge of sleep and the significance of sleep monitoring. Then, according to the types of physiological signals monitored, this paper describes the research progress of bioelectrical signals, biomechanical signals, and biochemical signals used for sleep monitoring. However, it is not ideal to monitor the sleep quality for the whole night based on only one signal. Therefore, this paper reviews the research on multi-signal monitoring and introduces systematic sleep monitoring schemes. Finally, a conclusion and discussion of sleep monitoring are presented to propose potential future directions and prospects for sleep monitoring.

Soft Electronics for Health Monitoring Assisted by Machine Learning.

Due to the development of the novel materials, the past two decades have witnessed the rapid advances of soft electronics. The soft electronics have huge potential in the physical sign monitoring and health care. One of the important advantages of soft electronics is forming good interface with skin, which can increase the user scale and improve the signal quality. Therefore, it is easy to build the specific dataset, which is important to improve the performance of machine learning algorithm. At the same time, with the assistance of machine learning algorithm, the soft electronics have become more and more intelligent to realize real-time analysis and diagnosis. The soft electronics and machining learning algorithms complement each other very well. It is indubitable that the soft electronics will bring us to a healthier and more intelligent world in the near future. Therefore, in this review, we will give a careful introduction about the new soft material, physiological signal detected by soft devices, and the soft devices assisted by machine learning algorithm. Some soft materials will be discussed such as two-dimensional material, carbon nanotube, nanowire, nanomesh, and hydrogel. Then, soft sensors will be discussed according to the physiological signal types (pulse, respiration, human motion, intraocular pressure, phonation, etc.). After that, the soft electronics assisted by various algorithms will be reviewed, including some classical algorithms and powerful neural network algorithms. Especially, the soft device assisted by neural network will be introduced carefully. Finally, the outlook, challenge, and conclusion of soft system powered by machine learning algorithm will be discussed.