Flexible Piezoelectric Acoustic Sensors and Machine Learning for Speech Processing

Molecular Ferroelectrics for Highly Sensitive Detection Toward Low-Frequency Sound Recognition

Human hearing cannot sensitively detect sounds below 100 Hz, which can affect the physical well-being and lead to dizziness, headaches, and nausea. Piezoelectric acoustic sensors still lack sensitivity to low-frequency sounds owing to the low piezoelectric coefficient or high elastic modulus of materials. The low elastic modulus and substantial piezoelectric coefficient of molecular ferroelectric materials make them excellent candidates for acoustic sensors. In this study, the molecular ferroelectric, [(CH3)3NCH2Cl]CdCl3, is used as a piezoelectric active layer in the construction of a piezoelectric acoustic sensor for low-frequency sound detection. The sensor exhibits high sensitivity (47.43 mV Pa-1 cm-2) at 87 Hz, with an excellent level of frequency resolution (up to 0.1 Hz). This facilitates the accurate discrimination and detection of low-frequency sounds, which is suitable for noise detection applications. The sensor differentiates between various musical instruments and heartbeats, and recognizes audio signals. This study highlights the potential of molecular ferroelectric materials in piezoelectric acoustic device applications, including noise detection, health monitoring, and human-computer interactions.

Emerging Wearable Acoustic Sensing Technologies

Sound signals not only serve as the primary communication medium but also find application in fields such as medical diagnosis and fault detection. With public healthcare resources increasingly under pressure, and challenges faced by disabled individuals on a daily basis, solutions that facilitate low-cost private healthcare hold considerable promise. Acoustic methods have been widely studied because of their lower technical complexity compared to other medical solutions, as well as the high safety threshold of the human body to acoustic energy. Furthermore, with the recent development of artificial intelligence technology applied to speech recognition, speech recognition devices, and systems capable of assisting disabled individuals in interacting with scenes are constantly being updated. This review meticulously summarizes the sensing mechanisms, materials, structural design, and multidisciplinary applications of wearable acoustic devices applied to human health and human-computer interaction. Further, the advantages and disadvantages of the different approaches used in flexible acoustic devices in various fields are examined. Finally, the current challenges and a roadmap for future research are analyzed based on existing research progress to achieve more comprehensive and personalized healthcare.

Bio-Inspired Neuromorphic Sensory Systems from Intelligent Perception to Nervetronics

Inspired by the extensive signal processing capabilities of the human nervous system, neuromorphic artificial sensory systems have emerged as a pivotal technology in advancing brain-like computing for applications in humanoid robotics, prosthetics, and wearable technologies. These systems mimic the functionalities of the central and peripheral nervous systems through the integration of sensory synaptic devices and neural network algorithms, enabling external stimuli to be converted into actionable electrical signals. This review delves into the intricate relationship between synaptic device technologies and neural network processing algorithms, highlighting their mutual influence on artificial intelligence capabilities. This study explores the latest advancements in artificial synaptic properties triggered by various stimuli, including optical, auditory, mechanical, and chemical inputs, and their subsequent processing through artificial neural networks for applications in image recognition and multimodal pattern recognition. The discussion extends to the emulation of biological perception via artificial synapses and concludes with future perspectives and challenges in neuromorphic system development, emphasizing the need for a deeper understanding of neural network processing to innovate and refine these complex systems.

A Fully Integrated Orthodontic Aligner With Force Sensing Ability for Machine Learning-Assisted Diagnosis

Currently, the diagnosis of malocclusion is a highly demanding process involving complicated examinations of the dental occlusion, which increases the demand for innovative tools for occlusal data monitoring. Nevertheless, continuous wireless monitoring within the oral cavity is challenging due to limitations in sampling and device size. In this study, by embedding high-performance piezoelectric sensors into the occlusal surfaces using flexible printed circuits, a fully integrated, flexible, and self-contained transparent aligner is developed. This aligner exhibits excellent sensitivity for occlusal force detection, with a broad detection threshold and continuous pressure monitoring ability at eight distinct sites. Integrated with machine learning algorithm, this fully integrated aligner can also identify and track adverse oral habits that can cause/exacerbate malocclusion, such as lip biting, thumb sucking, and teeth grinding. This system achieved 95% accuracy in determining malocclusion types by analyzing occlusal data from over 1400 malocclusion models. This fully-integrated sensing system, with wireless monitoring and machine learning processing, marks a significant advancement in the development of intraoral wearable sensors. Moreover, it can also facilitate remote orthodontic monitoring and evaluation, offering a new avenue for effective orthodontic care.

Self-Poled Bismuth Ferrite Thin Film Micromachined for Piezoelectric Ultrasound Transducers

Piezoelectric micromachined ultrasound transducers (pMUTs), especially those using lead-free materials, are crucial next-generation microdevices for precise actuation and sensing, driving advancements in medical, industrial, and environmental applications. Bismuth ferrite (BiFeO3) is emerging as a promising lead-free piezoelectric material to replace Pb(Zr,Ti)O3 in pMUTs. Despite its potential, the integration of BiFeO3 thin films into pMUTs has been hindered by poling issues. Here, a BiFeO3 heterostructure compositionally downgraded with Gd doping is developed to introduce compressive strain, resulting in strong self-poling. Utilizing a large-area self-poled thin film over an entire 6-inch wafer, a pMUT with a 6 × 6 array at the device level is designed and evaluated. At a resonant frequency of 21 kHz, the dynamic vibration displacement can reach 24.0 nm. At 500 Hz, far below the resonant frequency of 21 kHz, the pMUT also displays sensitive converse piezoelectric response, even at a high temperature of 200 °C. This work represents a significant breakthrough in lead-free BiFeO3 thin film for practical sensing applications, paving the way for the transformation of macro-transducers into next-generation functional microdevices.

Highly stable and ultra-fast vibration-responsive flexible iontronic sensors for accurate acoustic signal recognition

Wearable verbal language servers function as sophisticated and effective tools for fostering intelligent interactions between humans and machines. In the realm of collecting acoustic vibration signals, flexible iontronic pressure sensors have demonstrated their efficacy by incorporating microstructures into the functional layer, resulting in heightened pressure sensitivity. However, the substantial viscosity of the integrated iontronic materials or the lack of bonding at the heterogeneous interface emerges as a significant hindrance to capacitance recovery, leading to sluggish response speeds and mechanical instability. Here, we address the issue by introducing hydrogen bonding between naturally microstructured protein micro-fibers and hydrophilic ionic hydrogel into the dielectric layer. Due to the good resilience of protein micro-fibers and the enahnced interfacial bonding, this flexible vibration sensor demonstrates outstanding performance characteristics, featuring exceptional signal stability, a high-pressure resolution of 522 pF kPa-1, an ultra-fast response time of 0.6 ms, and a relaxation time of 0.6 ms, with a limit of detection (LOD) of 0.12 Pa, making it well-suited for acoustic vibration acquisition. By using a one-dimensional convolutional neural network (1D-CNN) deep learning to process and recognize collected acoustic signals, our sensor achieved an impressive accuracy of 98.2%. These wearable vibration sensors exemplify promising versatile applications in biometric authentication, personalized services, and human-computer interaction.

Leather-Based Shoe Soles for Real-Time Gait Recognition and Automatic Remote Assistance Using Machine Learning

Real-time monitoring of gait characteristics is crucial for applications in health monitoring, patient rehabilitation feedback, and telemedicine. However, the effective and stable acquisition and automatic analysis of gait information remain significant challenges. In this study, we present a flexible sensor based on a carbon nanotube/graphene composite conductive leather (CGL), which uses collagen fiber with a three-dimensional network structure as the flexible substrate. The CGL-based sensor demonstrates a high dynamic range, with notable pressure responses ranging from 0.6 to 14.5 kPa and high sensitivity (S = 0.2465 kPa-1). We further developed a device incorporating the CGL-based sensor to collect foot characteristic signals from human motion and designed smart sports shoes to facilitate effective human-computer interaction. Machine learning was employed to collect and process gait characteristic information in various states, including standing, sitting, walking, and falling. For real-time monitoring of falls, we optimized the K-Nearest Time Series Classifier (KNTC) algorithm, achieving an accuracy of 0.99 and a prediction time of only 13 ms, which highlights the system's excellent intelligent response capabilities. The system maintained a gait recognition accuracy of 90% across diverse populations, with low false-positive (3.3%) and false-negative (3.3%) rates. This work demonstrates stable gait recognition capabilities and provides valuable methods and insights for plantar behavior monitoring and data analysis, contributing to the development of advanced real-time gait monitoring systems.

Piezoelectric MEMS microphones based on rib structures and single crystal PZT thin film

In this study, a controllable mass‒frequency tuning method is presented using the etching of rib structures on a single-crystal PZT membrane. The rib structures were optimized to reduce the membrane mass while maintaining the stiffness; therefore, the center frequency could be increased to improve the low-frequency bandwidth of microphones. Additionally, this methodology could reduce the modulus and improve the sensitivity for the same resonant frequency, which typically indicates the maximum acoustic overload point (AOP). The PZT film was chosen because of its greater density; the simulation results showed that PZT could provide a greater frequency tuning (24.9%) compared to that of the AlN film (5.8%), and its large dielectric constant enabled the optimal design to have small electrodes at the maximum stress location while mitigating the sacrificial capacitance effect on electrical gain. An analytical model of rib-structure microphones was established and greatly reduced the computing time. The experimental results of the impedance tests revealed that the center frequencies of the six microphones shifted from 74.6 kHz to 106.3 kHz with rib-structure inner radii ranging from 0 μm to 340 μm; this result was in good agreement with the those of the analytical analysis and finite element modeling. While the center frequency greatly varied, the measured sensitivities at 1 kHz only varied within a small range from 22.3 mV/Pa to 25.7 mV/Pa; thus, the membrane stiffness minimally changed. Moreover, a single-crystal PZT film with a (100) crystal orientation and 0.24-degree full width at half maximum (FWHM) was used to enable differential sensing and a low possibility of undesirable polarization. Paired with a two-stage differential charge amplifier, a differential sensing microphone was experimentally demonstrated to improve the sensitivity from 25.7 mV/Pa to 36.1 mV/Pa and reduce the noise from -68.2 dBV to -82.8 dBV.

Intelligent Song Recognition via a Hollow-Microstructure-Based, Ultrasensitive Artificial Eardrum

Artificial ears with intelligence, which can sensitively detect sound-a variant of pressure-and generate consciousness and logical decision-making abilities, hold great promise to transform life. However, despite the emerging flexible sensors for sound detection, most success is limited to very simple phonemes, such as a couple of letters or words, probably due to the lack of device sensitivity and capability. Herein, the construction of ultrasensitive artificial eardrums enabling intelligent song recognition is reported. This strategy employs novel geometric engineering of sensing units in the soft microstructure array (to significantly reduce effective modulus) along with complex song recognition exploration leveraging machine learning algorithms. Unprecedented pressure sensitivity (6.9 × 103 kPa-1) is demonstrated in a sensor with a hollow pyramid architecture with porous slants. The integrated device exhibits unparalleled (exceeding by 1-2 orders of magnitude compared with reported benchmark samples) sound detection sensitivity, and can accurately identify 100% (for training set) and 97.7% (for test set) of a database of the segments from 77 songs varying in language, style, and singer. Overall, the results highlight the outstanding performance of the hollow-microstructure-based sensor, indicating its potential applications in human-machine interaction and wearable acoustical technologies.

Gradient-Layered MXene/Hollow Lignin Nanospheres Architecture Design for Flexible and Stretchable Supercapacitors

With the rapid development of flexible wearable electronics, the demand for stretchable energy storage devices has surged. In this work, a novel gradient-layered architecture was design based on single-pore hollow lignin nanospheres (HLNPs)-intercalated two-dimensional transition metal carbide (Ti3C2Tx MXene) for fabricating highly stretchable and durable supercapacitors. By depositing and inserting HLNPs in the MXene layers with a bottom-up decreasing gradient, a multilayered porous MXene structure with smooth ion channels was constructed by reducing the overstacking of MXene lamella. Moreover, the micro-chamber architecture of thin-walled lignin nanospheres effectively extended the contact area between lignin and MXene to improve ion and electron accessibility, thus better utilizing the pseudocapacitive property of lignin. All these strategies effectively enhanced the capacitive performance of the electrodes. In addition, HLNPs, which acted as a protective phase for MXene layer, enhanced mechanical properties of the wrinkled stretchable electrodes by releasing stress through slip and deformation during the stretch-release cycling and greatly improved the structural integrity and capacitive stability of the electrodes. Flexible electrodes and symmetric flexible all-solid-state supercapacitors capable of enduring 600% uniaxial tensile strain were developed with high specific capacitances of 1273 mF cm-2 (241 F g-1) and 514 mF cm-2 (95 F g-1), respectively. Moreover, their capacitances were well preserved after 1000 times of 600% stretch-release cycling. This study showcased new possibilities of incorporating biobased lignin nanospheres in energy storage devices to fabricate stretchable devices leveraging synergies among various two-dimensional nanomaterials.

Ultra-High Sensitivity Anisotropic Piezoelectric Sensors for Structural Health Monitoring and Robotic Perception

Monitoring minuscule mechanical signals, both in magnitude and direction, is imperative in many application scenarios, e.g., structural health monitoring and robotic sensing systems. However, the piezoelectric sensor struggles to satisfy the requirements for directional recognition due to the limited piezoelectric coefficient matrix, and achieving sensitivity for detecting micrometer-scale deformations is also challenging. Herein, we develop a vector sensor composed of lead zirconate titanate-electronic grade glass fiber composite filaments with oriented arrangement, capable of detecting minute anisotropic deformations. The as-prepared vector sensor can identify the deformation directions even when subjected to an unprecedented nominal strain of 0.06%, thereby enabling its utility in accurately discerning the 5 μm-height wrinkles in thin films and in monitoring human pulse waves. The ultra-high sensitivity is attributed to the formation of porous ferroelectret and the efficient load transfer efficiency of continuous lead zirconate titanate phase. Additionally, when integrated with machine learning techniques, the sensor's capability to recognize multi-signals enables it to differentiate between 10 types of fine textures with 100% accuracy. The structural design in piezoelectric devices enables a more comprehensive perception of mechanical stimuli, offering a novel perspective for enhancing recognition accuracy.

Multifunctional Self-Powered Sensors Integrated on a Robot Hand for Detecting Temperature-Pressure Stimuli and Recognizing Objects

Tactile sensing, especially pressure and temperature recognition, is crucial for both humans and robots in identifying objects. The general solutions, which use piezoresistive, capacitive, and thermal resistance effects, are usually subject to single-mode sensing and an energy supply. Here, we propose a multimode self-powered sensor. The sensor can respond to pressure and temperature stimuli using triboelectric and thermoelectric effects. Furthermore, we developed a sensing system comprising sensors, a deep learning block, and a smart board. The deep learning model can fuse features of triboelectric and thermoelectric signals, enabling a high accuracy of 99.8% in recognizing ten objects. This method may provide the future design of self-powered sensors for object recognition in robotics.

Flexible piezoelectric materials and strain sensors for wearable electronics and artificial intelligence applications

With the rapid development of artificial intelligence, the applications of flexible piezoelectric sensors in health monitoring and human-machine interaction have attracted increasing attention. Recent advances in flexible materials and fabrication technologies have promoted practical applications of wearable devices, enabling their assembly in various forms such as ultra-thin films, electronic skins and electronic tattoos. These piezoelectric sensors meet the requirements of high integration, miniaturization and low power consumption, while simultaneously maintaining their unique sensing performance advantages. This review provides a comprehensive overview of cutting-edge research studies on enhanced wearable piezoelectric sensors. Promising piezoelectric polymer materials are highlighted, including polyvinylidene fluoride and conductive hydrogels. Material engineering strategies for improving sensitivity, cycle life, biocompatibility, and processability are summarized and discussed focusing on filler doping, fabrication techniques optimization, and microstructure engineering. Additionally, this review presents representative application cases of smart piezoelectric sensors in health monitoring and human-machine interaction. Finally, critical challenges and promising principles concerning advanced manufacture, biological safety and function integration are discussed to shed light on future directions in the field of piezoelectrics.

A Nanoparticle-Based Artificial Ear for Personalized Classification of Emotions in the Human Voice Using Deep Learning

Artificial intelligence and human-computer interaction advances demand bioinspired sensing modalities capable of comprehending human affective states and speech. However, endowing skin-like interfaces with such intricate perception abilities remains challenging. Here, we have developed a flexible piezoresistive artificial ear (AE) sensor based on gold nanoparticles, which can convert sound signals into electrical signals through changes in resistance. By testing the sensor's performance at both frequency and sound pressure level (SPL), the AE has a frequency response range of 20 Hz to 12 kHz and can sense sound signals from up to 5 m away at a frequency of 1 kHz and an SPL of 126 dB. Furthermore, through deep learning, the device achieves up to 96.9% and 95.0% accuracy in classification and recognition applications for seven emotional and eight urban environmental noises, respectively. Hence, on one hand, our device can monitor the patient's emotional state by their speech, such as sudden yelling and screaming, which can help healthcare workers understand patients' condition in time. On the other hand, the device could also be used for real-time monitoring of noise levels in aircraft, ships, factories, and other high-decibel equipment and environments.

Machine Learning Assisted Self-Powered Identity Recognition Based on Thermogalvanic Hydrogel for Intelligent Security

Identity recognition as the first barrier of intelligent security plays a vital role, which is facing new challenges that are unable to meet the need of intelligent era due to low accuracy, complex configuration and dependence on power supply. Here, a finger temperature-driven intelligent identity recognition strategy is presented based on a thermogalvanic hydrogel (TGH) by actively discerning biometric characteristics of fingers. The TGH is a dual network PVA/Agar hydrogel in an H2O/glycerol binary solvent with [Fe(CN)6]3-/4- as a redox couple. Using a concave-arranged TGH array, the characteristics of users can be distinguished adequately even under an open environment by extracting self-existent intrinsic temperature features from five typical sites of fingers. Combined with machine learning, the TGH array can recognize different users with a high average accuracy of 97.6%. This self-powered identity recognition strategy is further applied to a smart lock, attaining a more reliable security protection from biometric characteristics than bare passwords. This work provides a promising solution for achieving better identity recognition, which has great advantages in intelligent security and human-machine interaction toward future Internet of everything.

Acoustically Enhanced Triboelectric Stethoscope for Ultrasensitive Cardiac Sounds Sensing and Disease Diagnosis

Electronic stethoscope used to detect cardiac sounds that contain essential clinical information is a primary tool for diagnosis of various cardiac disorders. However, the linear electromechanical constitutive relation makes conventional piezoelectric sensors rather ineffective to detect low-intensity, low-frequency heart acoustic signal without the assistance of complex filtering and amplification circuits. Herein, it is found that triboelectric sensor features superior advantages over piezoelectric one for microquantity sensing originated from the fast saturated constitutive characteristic. As a result, the triboelectric sensor shows ultrahigh sensitivity (1215 mV Pa-1) than the piezoelectric counterpart (21 mV Pa-1) in the sound pressure range of 50-80 dB under the same testing condition. By designing a trumpet-shaped auscultatory cavity with a power function cross-section to achieve acoustic energy converging and impedance matching, triboelectric stethoscope delivers 36 dB signal-to-noise ratio for human test (2.3 times of that for piezoelectric one). Further combining with machine learning, five cardiac states can be diagnosed at 97% accuracy. In general, the triboelectric sensor is distinctly unique in basic mechanism, provides a novel design concept for sensing micromechanical quantities, and presents significant potential for application in cardiac sounds sensing and disease diagnosis.

Bionic Spider Web Flexible Strain Sensor Based on CF-L and Machine Learning

At present, the preparation of laser-induced graphene (LIG) has become an important technology in sensor manufacturing. In the conventional preparation process, the CO2 laser is widely used; however, its experimental period is long and its efficiency needs to be improved. We propose an innovative strategy to improve the experimental efficiency. We use the machine learning method to accurately predict the preparation parameters of LIG, so as to optimize the experimental process. Different structures can lead to different sensor performances. The structure constructed by the CO2 laser is rough and has a large size, which can affect the performance of the sensor. Therefore, we propose for the first time an innovative method for intramembrane structure construction that combines the advantages of the CO2 laser and fiber laser (CF-L). With this CF-L method, we have successfully prepared a biomimetic, flexible strain sensor. This sensor not only maintains a high degree of sensitivity, but also has a more refined and optimized structure. The manufacturing process of the whole sensor is simple, economical, and durable and can be prepared in large quantities and can be used to detect the extension and bending of human joints.

Robotics Perception and Control: Key Technologies and Applications

The integration of advanced sensor technologies has significantly propelled the dynamic development of robotics, thus inaugurating a new era in automation and artificial intelligence. Given the rapid advancements in robotics technology, its core area-robot control technology-has attracted increasing attention. Notably, sensors and sensor fusion technologies, which are considered essential for enhancing robot control technologies, have been widely and successfully applied in the field of robotics. Therefore, the integration of sensors and sensor fusion techniques with robot control technologies, which enables adaptation to various tasks in new situations, is emerging as a promising approach. This review seeks to delineate how sensors and sensor fusion technologies are combined with robot control technologies. It presents nine types of sensors used in robot control, discusses representative control methods, and summarizes their applications across various domains. Finally, this survey discusses existing challenges and potential future directions.

Age of Flexible Electronics: Emerging Trends in Soft Multifunctional Sensors

With the commercialization of first-generation flexible mobiles and displays in the late 2010s, humanity has stepped into the age of flexible electronics. Inevitably, soft multifunctional sensors, as essential components of next-generation flexible electronics, have attracted tremendous research interest like never before. This review is dedicated to offering an overview of the latest emerging trends in soft multifunctional sensors and their accordant future research and development (R&D) directions for the coming decade. First, key characteristics and the predominant target stimuli for soft multifunctional sensors are highlighted. Second, important selection criteria for soft multifunctional sensors are introduced. Next, emerging materials/structures and trends for soft multifunctional sensors are identified. Specifically, the future R&D directions of these sensors are envisaged based on their emerging trends, namely i) decoupling of multiple stimuli, ii) data processing, iii) skin conformability, and iv) energy sources. Finally, the challenges and potential opportunities for these sensors in future are discussed, offering new insights into prospects in the fast-emerging technology.

Design of Double Conductive Layer and Grid-Assistant Face-to-Face Structure for Wide Linear Range, High Sensitivity Flexible Pressure Sensors

Recently, flexible pressure sensors have drawn great attention because of their potential application in human-machine interfaces, healthcare monitoring, electronic skin, etc. Although many sensors with good performance have been reported, researchers mostly focused on surface morphology regulation, and the effect of the resistance characteristics on the performance of the sensor was still rarely systematically investigated. In this paper, a strategy for modulating electron transport is proposed to adjust the linear range and sensitivity of the sensor. In the modulating process, we constructed a double conductive layer (DCL) and grid-assistant face-to-face structure and obtained the sensor with a wide linear range of 0-700 kPa and a high sensitivity of 57.5 kPa-1, which is one of the best results for piezoresistive sensors. In contrast, the sensor with a single conductive layer (SCL) and simple face-to-face structure exhibited a moderate linear range (7 kPa) and sensitivity (2.8 kPa-1). Benefiting from the great performance, the modulated sensor allows for clear pulse wave detection and good recognition of gait signals, which indicates the great application potential in human daily life.

Insights into Materials, Physics, and Applications in Flexible and Wearable Acoustic Sensing Technology

Sound plays a crucial role in the perception of the world. It allows to communicate, learn, and detect potential dangers, diagnose diseases, and much more. However, traditional acoustic sensors are limited in their form factors, being rigid and cumbersome, which restricts their potential applications. Recently, acoustic sensors have made significant advancements, transitioning from rudimentary forms to wearable devices and smart everyday clothing that can conform to soft, curved, and deformable surfaces or surroundings. In this review, the latest scientific and technological breakthroughs with insightful analysis in materials, physics, design principles, fabrication strategies, functions, and applications of flexible and wearable acoustic sensing technology are comprehensively explored. The new generation of acoustic sensors that can recognize voice, interact with machines, control robots, enable marine positioning and localization, monitor structural health, diagnose human vital signs in deep tissues, and perform organ imaging is highlighted. These innovations offer unique solutions to significant challenges in fields such as healthcare, biomedicine, wearables, robotics, and metaverse. Finally, the existing challenges and future opportunities in the field are addressed, providing strategies to advance acoustic sensing technologies for intriguing real-world applications and inspire new research directions.

Machine Learning Accelerated Discovery of Functional MXenes with Giant Piezoelectric Coefficients

Efficient and rapid screening of target materials in a vast material space remains a significant challenge in the field of materials science. In this study, first-principles calculations and machine learning algorithms are performed to search for high out-of-plane piezoelectric stress coefficient materials in the MXene functional database among the 1757 groups of noncentrosymmetric MXenes with nonzero band gaps, which meet the criteria for piezoelectric properties. For the monatomic MXene testing set, the random forest regression (RFR), gradient boosting regression (GBR), support vector regression (SVR), and multilayer perceptron regression (MLPR) exhibit R2 values of 0.80, 0.80, 0.89, and 0.87, respectively. Expanding our analysis to the entire MXene data set, the best active learning cycle finds more than 140 and 22 MXenes with out-of-plane piezoelectric stress coefficients (e31) exceeding 3 × 10-10 and 5 × 10-10 C/m, respectively. Moreover, thermodynamic stabilities were confirmed in 22 MXenes with giant piezoelectric stress coefficients and 9 MXenes with both large in-plane (d11 > 15 pm/V) and out-of-plane (d31 > 2 pm/V) piezoelectric strain coefficients. These findings highlight the remarkable capabilities of machine learning and its optimization algorithms in accelerating the discovery of novel piezoelectric materials, and MXene materials emerge as highly promising candidates for piezoelectric materials.

Intelligent, Flexible Artificial Throats with Sound Emitting, Detecting, and Recognizing Abilities

In recent years, there has been a notable rise in the number of patients afflicted with laryngeal diseases, including cancer, trauma, and other ailments leading to voice loss. Currently, the market is witnessing a pressing demand for medical and healthcare products designed to assist individuals with voice defects, prompting the invention of the artificial throat (AT). This user-friendly device eliminates the need for complex procedures like phonation reconstruction surgery. Therefore, in this review, we will initially give a careful introduction to the intelligent AT, which can act not only as a sound sensor but also as a thin-film sound emitter. Then, the sensing principle to detect sound will be discussed carefully, including capacitive, piezoelectric, electromagnetic, and piezoresistive components employed in the realm of sound sensing. Following this, the development of thermoacoustic theory and different materials made of sound emitters will also be analyzed. After that, various algorithms utilized by the intelligent AT for speech pattern recognition will be reviewed, including some classical algorithms and neural network algorithms. Finally, the outlook, challenge, and conclusion of the intelligent AT will be stated. The intelligent AT presents clear advantages for patients with voice impairments, demonstrating significant social values.

Iontronic Dynamic Sensor with Broad Bandwidth and Flat Frequency Response Using Controlled Preloading Strategy

Rapid advancements in human-machine interaction and voice biometrics impose desirability on soft mechanical sensors for sensing complex dynamic signals. However, existing soft mechanical sensors mainly concern quasi-static signals such as pressure and pulsation for health monitoring, limiting their applications in emerging wearable electronics. Here, we propose a hydrogel-based soft mechanical sensor that enables recording a wide range of dynamic signals relevant to humans by combining a preloading design strategy and iontronic sensing mechanism. The proposed sensor offers a two-orders-of-magnitude larger working bandwidth (up to 1000 Hz) than most of the reported soft mechanical sensors and meanwhile provides a high sensitivity (-23 dB) that surpasses the common commercial microphone. The amplitude-frequency characteristic of the proposed sensor can be precisely tuned to meet the desired requirement by adjusting the preloads and the parameters of the microstructured hydrogel. The sensor is capable of recording instrumental sounds with high fidelity from simple pure tones to melodic songs. Demonstration of a skin-mountable sensor used for human-voice-based remote control of a toy car shows great potential for applications in the voice user interface of human-machine interactions.

Ultrasensitive Soft Sensor from Anisotropic Conductive Biphasic Liquid Metal-Polymer Gels

Subtle vibrations, such as sound and ambient noises, are common mechanical waves that can transmit energy and signals for modern technologies such as robotics and health management devices. However, soft electronics cannot accurately distinguish ultrasmall vibrations owing to their extremely small pressure, complex vibration waveforms, and high noise susceptibility. This study successfully recognizes signals from subtle vibrations using a highly flexible anisotropic conductive gel (ACG) based on biphasic liquid metals. The relationships between the anisotropic structure, subtle vibrations, and electrical performance are investigated using rheological-electrical experiments. The refined anisotropic design successfully realized low-cost flexible electronics with ultrahigh sensitivity (Gauge Factor: 12787), extremely low detection limit (strain: 0.01%), and excellent frequency recognition accuracy (>99%), significantly surpassing those of current flexible sensors. The ultrasensitive flexible electronics in this study are beneficial for diverse advanced technologies such as acoustic engineering, wearable electronics, and intelligent robotics.

Self-Reinforced Piezoelectric Response of an Electroluminescent Film for the Dual-Channel Signal Monitoring of Damaged Areas

Organic piezoelectric nanogenerators (PENGs) show promise for monitoring damage in mechanical equipment. However, weak interfacial bonding between the reinforcing phase and the fluorinated material limits the feedback signal from the damaged area. In this study, we developed a PENG film capable of real-time identification of the damage location and extent. By incorporating core-shell barium titanate (BTO@PVDF-HFP) nanoparticles, we achieved enhanced piezoelectric characteristics, flexibility, and processability. The composite film exhibited an expanded output voltage range, reaching 41.8 V with an increase in frequency, load, and damage depth. Additionally, the film demonstrated self-powered electroluminescence (EL) during the wear process, thanks to its inherent ferroelectric properties and the presence of luminescent ZnS:Cu particles. Unlike conventional PENG electroluminescent devices, the PENG film exhibited luminescence at the damage location over a wide temperature range. Our findings offer a novel approach for realizing modular and miniaturized real-time damage mapping systems in the field of safety engineering.

Artificial Neuron Devices

Efforts to design devices emulating complex cognitive abilities and response processes of biological systems have long been a coveted goal. Recent advancements in flexible electronics, mirroring human tissue's mechanical properties, hold significant promise. Artificial neuron devices, hinging on flexible artificial synapses, bioinspired sensors, and actuators, are meticulously engineered to mimic the biological systems. However, this field is in its infancy, requiring substantial groundwork to achieve autonomous systems with intelligent feedback, adaptability, and tangible problem-solving capabilities. This review provides a comprehensive overview of recent advancements in artificial neuron devices. It starts with fundamental principles of artificial synaptic devices and explores artificial sensory systems, integrating artificial synapses and bioinspired sensors to replicate all five human senses. A systematic presentation of artificial nervous systems follows, designed to emulate fundamental human nervous system functions. The review also discusses potential applications and outlines existing challenges, offering insights into future prospects. We aim for this review to illuminate the burgeoning field of artificial neuron devices, inspiring further innovation in this captivating area of research.

Artificial Intelligence Meets Flexible Sensors: Emerging Smart Flexible Sensing Systems Driven by Machine Learning and Artificial Synapses

The recent wave of the artificial intelligence (AI) revolution has aroused unprecedented interest in the intelligentialize of human society. As an essential component that bridges the physical world and digital signals, flexible sensors are evolving from a single sensing element to a smarter system, which is capable of highly efficient acquisition, analysis, and even perception of vast, multifaceted data. While challenging from a manual perspective, the development of intelligent flexible sensing has been remarkably facilitated owing to the rapid advances of brain-inspired AI innovations from both the algorithm (machine learning) and the framework (artificial synapses) level. This review presents the recent progress of the emerging AI-driven, intelligent flexible sensing systems. The basic concept of machine learning and artificial synapses are introduced. The new enabling features induced by the fusion of AI and flexible sensing are comprehensively reviewed, which significantly advances the applications such as flexible sensory systems, soft/humanoid robotics, and human activity monitoring. As two of the most profound innovations in the twenty-first century, the deep incorporation of flexible sensing and AI technology holds tremendous potential for creating a smarter world for human beings.

Ultrasensitive Acoustic Detection Using an Enlarged Fabry-Perot Cavity with a Graphene Diaphragm

For exerting high sensitivity of ultrathin graphene to detection deformation, an enlarged backing air cavity (EBC) structure is developed to further enhance the mechanical sensitivity (SM) of a graphene-based Fabry-Perot (F-P) acoustic sensor. COMSOL acoustic field simulation on the air cavity size-dependent SM confirms the optimal length and radius of the EBC of 0.2 and 1.5 mm, respectively, with the maximum simulation SM of 26.16 nm/Pa@1 kHz. Acoustic experiments further demonstrate that the frequency response of the fabricated graphene-based F-P acoustic sensor after the use of the EBC is enhanced by 5.73-79.33 times in the range of 0.5-18 kHz, compared with the conventional one without the EBC. Especially the maximum SM is up to 187.32 nm/Pa@16 kHz, which is at least 17% higher than the SM values ranging from 1.1 to 160 nm/Pa in previously reported F-P acoustic sensors using various diaphragm materials. More acoustic characteristics are examined to highlight various merits of the EBC structure, including a signal-to-noise ratio (SNR) of 60-75 dB@0.5-18 kHz, a time stability of less than ±1.3% for 90 min, a detection resolution of 0.01 Hz, and a high-fidelity speech detection with a cross-correlation coefficient of greater than 0.9, thereby revealing its high-performance weak acoustic sensing and speech recognition applications.

Recent development of piezoelectric biosensors for physiological signal detection and machine learning assisted cardiovascular disease diagnosis

As cardiovascular disease stands as a global primary cause of mortality, there has been an urgent need for continuous and real-time heart monitoring to effectively identify irregular heart rhythms and to offer timely patient alerts. However, conventional cardiac monitoring systems encounter challenges due to inflexible interfaces and discomfort during prolonged monitoring. In this review article, we address these issues by emphasizing the recent development of the flexible, wearable, and comfortable piezoelectric passive sensor assisted by machine learning technology for diagnosis. This innovative device not only harmonizes with the dynamic mechanical properties of human skin but also facilitates continuous and real-time collection of physiological signals. Addressing identified challenges and constraints, this review provides insights into recent advances in piezoelectric cardiac sensors, from devices to circuit systems. Furthermore, this review delves into the integration of machine learning technologies, showcasing their pivotal role in facilitating continuous and real-time assessment of cardiac status. The synergistic combination of flexible piezoelectric sensor design and machine learning holds substantial potential in automating the detection of cardiac irregularities with minimal human intervention. This transformative approach has the power to revolutionize patient care paradigms.

Health Monitoring via Heart, Breath, and Korotkoff Sounds by Wearable Piezoelectret Patches

Real-time monitoring of vital sounds from cardiovascular and respiratory systems via wearable devices together with modern data analysis schemes have the potential to reveal a variety of health conditions. Here, a flexible piezoelectret sensing system is developed to examine audio physiological signals in an unobtrusive manner, including heart, Korotkoff, and breath sounds. A customized electromagnetic shielding structure is designed for precision and high-fidelity measurements and several unique physiological sound patterns related to clinical applications are collected and analyzed. At the left chest location for the heart sounds, the S1 and S2 segments related to cardiac systole and diastole conditions, respectively, are successfully extracted and analyzed with good consistency from those of a commercial medical device. At the upper arm location, recorded Korotkoff sounds are used to characterize the systolic and diastolic blood pressure without a doctor or prior calibration. An Omron blood pressure monitor is used to validate these results. The breath sound detections from the lung/ trachea region are achieved a signal-to-noise ration comparable to those of a medical recorder, BIOPAC, with pattern classification capabilities for the diagnosis of viable respiratory diseases. Finally, a 6×6 sensor array is used to record heart sounds at different locations of the chest area simultaneously, including the Aortic, Pulmonic, Erb's point, Tricuspid, and Mitral regions in the form of mixed data resulting from the physiological activities of four heart valves. These signals are then separated by the independent component analysis algorithm and individual heart sound components from specific heart valves can reveal their instantaneous behaviors for the accurate diagnosis of heart diseases. The combination of these demonstrations illustrate a new class of wearable healthcare detection system for potentially advanced diagnostic schemes.

Piezotronic Antimony Sulphoiodide/Polyvinylidene Composite for Strain-Sensing and Energy-Harvesting Applications

This study investigates the piezoelectric and piezotronic properties of a novel composite material comprising polyvinylidene fluoride (PVDF) and antimony sulphoiodide (SbSI) nanowires. The material preparation method is detailed, showcasing its simplicity and reproducibility. The material's electrical resistivity, piezoelectric response, and energy-harvesting capabilities are systematically analyzed under various deflection conditions and excitation frequencies. The piezoelectric response is characterized by the generation of charge carriers in the material due to mechanical strain, resulting in voltage output. The fundamental phenomena of charge generation, along with their influence on the material's resistivity, are proposed. Dynamic strain testing reveals the composite's potential as a piezoelectric nanogenerator (PENG), converting mechanical energy into electrical energy. Comparative analyses highlight the composite's power density advantages, thereby demonstrating its potential for energy-harvesting applications. This research provides insights into the interplay between piezoelectric and piezotronic phenomena in nanocomposites and their applicability in energy-harvesting devices.

Flexible electronics for cardiovascular healthcare monitoring

Cardiovascular diseases (CVDs) are one of the most urgent threats to humans worldwide, which are responsible for almost one-third of global mortality. Over the last decade, research on flexible electronics for monitoring and treatment of CVDs has attracted tremendous attention. In contrast to conventional medical instruments in hospitals that are usually bulky, hard to move, monofunctional, and time-consuming, flexible electronics are capable of continuous, noninvasive, real-time, and portable monitoring. Notable progress has been made in this emerging field, and thus a number of significant achievements and concomitant research prospects deserve attention for practical implementation. Here, we comprehensively review the latest progress of flexible electronics for CVDs, focusing on new functions provided by flexible electronics. First, the characteristics of CVDs and flexible electronics and the foundation of their combination are briefly reviewed. Then, four representative applications of flexible electronics for CVDs are elaborated: blood pressure (BP) monitoring, electrocardiogram (ECG) monitoring, echocardiogram monitoring, and direct epicardium monitoring. Their operational principles, progress, merits and demerits, and future efforts are discussed. Finally, the remaining challenges and opportunities for flexible electronics for cardiovascular healthcare are outlined.

Sentiment analysis in multilingual context: Comparative analysis of machine learning and hybrid deep learning models

This research paper investigates the efficacy of various machine learning models, including deep learning and hybrid models, for text classification in the English and Bangla languages. The study focuses on sentiment analysis of comments from a popular Bengali e-commerce site, "DARAZ," which comprises both Bangla and translated English reviews. The primary objective of this study is to conduct a comparative analysis of various models, evaluating their efficacy in the domain of sentiment analysis. The research methodology includes implementing seven machine learning models and deep learning models, such as Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Convolutional 1D (Conv1D), and a combined Conv1D-LSTM. Preprocessing techniques are applied to a modified text set to enhance model accuracy. The major conclusion of the study is that Support Vector Machine (SVM) models exhibit superior performance compared to other models, achieving an accuracy of 82.56% for English text sentiment analysis and 86.43% for Bangla text sentiment analysis using the porter stemming algorithm. Additionally, the Bi-LSTM Based Model demonstrates the best performance among the deep learning models, achieving an accuracy of 78.10% for English text and 83.72% for Bangla text using porter stemming. This study signifies significant progress in natural language processing research, particularly for Bangla, by enhancing improved text classification models and methodologies. The results of this research make a significant contribution to the field of sentiment analysis and offer valuable insights for future research and practical applications.

Applications, functions, and accuracy of artificial intelligence in restorative dentistry: A literature review

OBJECTIVE

The applications of artificial intelligence (AI) are increasing in restorative dentistry; however, the AI performance is unclear for dental professionals. The purpose of this narrative review was to evaluate the applications, functions, and accuracy of AI in diverse aspects of restorative dentistry including caries detection, tooth preparation margin detection, tooth restoration design, metal structure casting, dental restoration/implant detection, removable partial denture design, and tooth shade determination.

OVERVIEW

An electronic search was performed on Medline/PubMed, Embase, Web of Science, Cochrane, Scopus, and Google Scholar databases. English-language articles, published from January 1, 2000, to March 1, 2022, relevant to the aforementioned aspects were selected using the key terms of artificial intelligence, machine learning, deep learning, artificial neural networks, convolutional neural networks, clustering, soft computing, automated planning, computational learning, computer vision, and automated reasoning as inclusion criteria. A manual search was also performed. Therefore, 157 articles were included, reviewed, and discussed.

CONCLUSIONS

Based on the current literature, the AI models have shown promising performance in the mentioned aspects when being compared with traditional approaches in terms of accuracy; however, as these models are still in development, more studies are required to validate their accuracy and apply them to routine clinical practice.

CLINICAL SIGNIFICANCE

AI with its specific functions has shown successful applications with acceptable accuracy in diverse aspects of restorative dentistry. The understanding of these functions may lead to novel applications with optimal accuracy for AI in restorative dentistry.

Temperature-robust optical microphone with a compact grating interferometric module

The high demand for advanced acoustic sensors has prompted optical microphones to become a current research hotspot; this is especially the case in light of the performance of existing electroacoustic microphones having reached the ceiling. In this work, a thermally stable optical microphone has been developed for sensitive detection of low-frequency acoustic signals. The microphone was prepared using a prestressed nickel diaphragm and a compact grating interferometric module. The adjacent surfaces of the diaphragm and grating form a short Fabry-Perot cavity, which makes the microphone robust to ambient temperature fluctuation due to the reduced thermal drift of its operating point relative to the quadrature point of the interferometer. The cavity length-operating wavelength relationship of the microphone operating at the quadrature point was obtained. The performance of the prepared microphone was tested using various methods. Experimental results show that the microphone enables stable operation at the quadrature point over a wide range of temperatures from 0°C to 60°C with low signal distortion and high sensitivity. The response of the prepared optical microphone to low-frequency drone noise was measured and compared with that obtained with a commercial electret condenser microphone.

Development and Evaluation of a Flexible PVDF-Based Balloon Sensor for Detecting Mechanical Forces at Key Esophageal Nodes in Esophageal Motility Disorders

Prevailing methods for esophageal motility assessments, such as perfusion manometry and probe-based function imaging, frequently overlook the intricate stress fields acting on the liquid-filled balloons at the forefront of the probing device within the esophageal lumen. To bridge this knowledge gap, we innovatively devised an infusible flexible balloon catheter, equipped with a quartet of PVDF piezoelectric sensors. This design, working in concert with a bespoke local key-node analytical algorithm and a sensor array state analysis model, seeks to shed new light on the dynamic mechanical characteristics at pivotal esophageal locales. To further this endeavor, we pioneered a singular closed balloon system and a complementary signal acquisition and processing system that employs a homogeneously distributed PVDF piezoelectric sensor array for the real-time monitoring of dynamic mechanical nuances in the esophageal segment. An advanced analytical model was established to scrutinize the coupled physical fields under varying degrees of balloon inflation, thereby facilitating a thorough dynamic stress examination of local esophageal nodes. Our rigorous execution of static, dynamic, and simulated swallowing experiments robustly substantiated the viability of our design, the logical coherence of our esophageal key-point stress analytical algorithm, and the potential clinical utility of a flexible esophageal key-node stress detection balloon probe outfitted with a PVDF array. This study offers a fresh lens through which esophageal motility testing can be viewed and improved upon.

Emerging Trends in Soft Electronics: Integrating Machine Intelligence with Soft Acoustic/Vibration Sensors

In the last decade, soft acoustic/vibration sensors have gained tremendous research interest due to their unique ability to detect broadband acoustic/vibration stimuli, potentializing futuristic applications including voice biometrics, voice-controlled human-machine-interfaces, electronic skin, and skin-mountable healthcare devices. Importantly, to benefit most from these sensors, it is inevitable to use machine learning (ML) to process their output signals; with ML, a more accurate and efficient interpretation of original data is possible. This paper is dedicated to offering an overview of recent advances empowering the development of soft acoustic/vibration sensors and their signal processing using ML. First, the key performance parameters of the sensors are discussed. Second, popular transduction mechanisms for the sensors are addressed, followed by an in-depth overview of each type, covering materials used, structural designs, and sensing performances. Third, potential applications of the sensors are elaborated and fourth, a thorough discussion on ML is conducted, exploring different types of ML, specific ML algorithms suitable for processing acoustic/vibration signals, and current trends in ML-assisted applications. Finally, the challenges and potential opportunities in soft acoustic/vibration sensor and ML research are revealed to offer new insights into future prospects in these fields.

Excellent-Moisture-Resistance Fluorinated Polyimide Composite Film and Self-Powered Acoustic Sensing

As a clean, sustainable energy source, sound can carry a wealth of information and play a huge role in the Internet of Things era. In recent years, triboelectric acoustic sensors have received increasing attention due to the advantages of self-power supply and high sensitivity. However, the triboelectric charge is susceptible to ambient humidity, which reduces the reliability of the sensor and limits the application scenarios significantly. In this paper, a highly moisture-resistant fluorinated polyimide composited with an amorphous fluoropolymer film was prepared. The charge injection performance, triboelectric performance, and moisture resistance of the composite film were investigated. In addition, we developed a self-powered, highly sensitive, and moisture-resistant porous-structure acoustic sensor based on contact electrification. The detection characteristics of the acoustic sensor are also obtained.

Flexible BaTiO3 Thin Film-Based Coupled Nanogenerator for Simultaneously Scavenging Light and Vibration Energies

Ferroelectric materials have a variety of properties, such as piezoelectricity, pyroelectricity, and the ferroelectric photovoltaic effect, which enable them to obtain electrical energy from various external stimuli. Here, we report a coupled nanogenerator based on flexible BTO ferroelectric films with a cantilevered beam structure. It combines the photovoltaic and flexoelectric effects in a ferroelectric materials-based coupled nanogenerator for simultaneously scavenging vibration energy and light energy, thus improving energy scavenging performance. As compared with the photovoltaic effect individually, simultaneous vibration and light illumination under a light intensity of 57 mW/cm² at 405 nm can produce a photo-flexoelectric coupling current of 85 nA, where the current peak has been enhanced by 121%. Due to the photo-flexoelectric coupling effect, the device has outstanding charging performance, where a 4.7 μF capacitor can be charged to 60 mV in 150 s. These devices have potential applications in multi-energy scavenging and self-powered sensors.

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.

Hydrogel-Based Flexible Electronics

Flexible electronics is an emerging field of research involving multiple disciplines, which include but not limited to physics, chemistry, materials science, electronic engineering, and biology. However, the broad applications of flexible electronics are still restricted due to several limitations, including high Young's modulus, poor biocompatibility, and poor responsiveness. Innovative materials aiming for overcoming these drawbacks and boost its practical application is highly desirable. Hydrogel is a class of 3D crosslinked hydrated polymer networks, and its exceptional material properties render it as a promising candidate for the next generation of flexible electronics. Here, the latest methods of synthesizing advanced functional hydrogels and the state-of-art applications of hydrogel-based flexible electronics in various fields are reviewed. More importantly, the correlation between properties of the hydrogel and device performance is discussed here, to have better understanding of the development of flexible electronics by using environmentally responsive hydrogels. Last, perspectives on the current challenges and future directions in the development of hydrogel-based multifunctional flexible electronics are provided.

Multichannel Gradient Piezoelectric Transducer Assisted with Deep Learning for Broadband Acoustic Sensing

As an important part of human-machine interfaces, piezoelectric voice recognition has received extensive attention due to its unique self-powered nature. However, conventional voice recognition devices exhibit a limited response frequency band due to the intrinsic hardness and brittleness of piezoelectric ceramics or the flexibility of piezoelectric fibers. Here, we propose a cochlear-inspired multichannel piezoelectric acoustic sensor (MAS) based on gradient PVDF piezoelectric nanofibers for broadband voice recognition by a programmable electrospinning technique. Compared with the common electrospun PVDF membrane-based acoustic sensor, the developed MAS demonstrates the greatly 300%-broadened frequency band and the substantially 334.6%-enhanced piezoelectric output. More importantly, this MAS can serve as a high-fidelity auditory platform for music recording and human voice recognition, in which the classification accuracy rate can reach up to 100% in coordination with deep learning. The programmable bionic gradient piezoelectric nanofiber may provide a universal strategy for the development of intelligent bioelectronics.

In Situ Quantification of Strain-Induced Piezoelectric Potential of Dynamically Bending ZnO Microwires

The piezoelectric properties of semiconductor micro/nanowires (M/NWs) are crucial for optimizing semiconductors' electronic structure and carrier dynamics. However, the dynamic characterization of the piezoelectric properties of M/NWs remains challenging. Here, a Kelvin probe force microscopy technique based on a dual-probe atomic force microscope is developed to achieve in situ piezoelectric potential measurements of dynamic bending MWs. This technique can not only characterize the surface potential on different crystal faces of ZnO MWs in a natural state through controllable axial rotation, but also investigate the piezoelectric potential of the dynamically bending flake-like ZnO MW at different points and under different strain loads. The results show that the surface potentials of different faces/positions of the ZnO MWs are varied significantly, and determine that the quasi-static conditions piezo-strain factor of the flake-like ZnO MW is 0.28 V/%, while the factor was 0.14 V/% under low-frequency (⩽5 Hz) sinusoidal strain loading. This work provides a significant methodology to further study piezoelectric materials, and it aims to facilitate their applications in piezoelectric devices and systems.

Machine Learning-Enhanced Flexible Mechanical Sensing

To realize a hyperconnected smart society with high productivity, advances in flexible sensing technology are highly needed. Nowadays, flexible sensing technology has witnessed improvements in both the hardware performances of sensor devices and the data processing capabilities of the device's software. Significant research efforts have been devoted to improving materials, sensing mechanism, and configurations of flexible sensing systems in a quest to fulfill the requirements of future technology. Meanwhile, advanced data analysis methods are being developed to extract useful information from increasingly complicated data collected by a single sensor or network of sensors. Machine learning (ML) as an important branch of artificial intelligence can efficiently handle such complex data, which can be multi-dimensional and multi-faceted, thus providing a powerful tool for easy interpretation of sensing data. In this review, the fundamental working mechanisms and common types of flexible mechanical sensors are firstly presented. Then how ML-assisted data interpretation improves the applications of flexible mechanical sensors and other closely-related sensors in various areas is elaborated, which includes health monitoring, human-machine interfaces, object/surface recognition, pressure prediction, and human posture/motion identification. Finally, the advantages, challenges, and future perspectives associated with the fusion of flexible mechanical sensing technology and ML algorithms are discussed. These will give significant insights to enable the advancement of next-generation artificial flexible mechanical sensing.

In Situ Polymerized MXene/Polypyrrole/Hydroxyethyl Cellulose-Based Flexible Strain Sensor Enabled by Machine Learning for Handwriting Recognition

Flexible strain sensors have significant progress in the fields of human-computer interaction, medical monitoring, and handwriting recognition, but they also face many challenges such as the capture of weak signals, comprehensive acquisition of the information, and accurate recognition. Flexible strain sensors can sense externally applied deformations, accurately measure human motion and physiological signals, and record signal characteristics of handwritten text. Herein, we prepare a sandwich-structured flexible strain sensor based on an MXene/polypyrrole/hydroxyethyl cellulose (MXene/PPy/HEC) conductive material and a PDMS flexible substrate. The sensor features a wide linear strain detection range (0-94%), high sensitivity (gauge factor 357.5), reliable repeatability (>1300 cycles), ultrafast response-recovery time (300 ms), and other excellent sensing properties. The MXene/PPy/HEC sensor can detect human physiological activities, exhibiting excellent performance in measuring external strain changes and real-time motion detection. In addition, the signals of English words, Arabic numerals, and Chinese characters handwritten by volunteers measured by the MXene/PPy/HEC sensor have unique characteristics. Through machine learning technology, different handwritten characters are successfully identified, and the recognition accuracy is higher than 96%. The results show that the MXene/PPy/HEC sensor has a significant impact in the fields of human motion detection, medical and health monitoring, and handwriting recognition.

Force-induced ion generation in zwitterionic hydrogels for a sensitive silent-speech sensor

Human-sensitive mechanosensation depends on ionic currents controlled by skin mechanoreceptors. Inspired by the sensory behavior of skin, we investigate zwitterionic hydrogels that generate ions under an applied force in a mobile-ion-free system. Within this system, water dissociates as the distance between zwitterions reduces under an applied pressure. Meanwhile, zwitterionic segments can provide migration channels for the generated ions, significantly facilitating ion transport. These combined effects endow a mobile-ion-free zwitterionic skin sensor with sensitive transduction of pressure into ionic currents, achieving a sensitivity up to five times that of nonionic hydrogels. The signal response time, which relies on the crosslinking degree of the zwitterionic hydrogel, was ~38 ms, comparable to that of natural skin. The skin sensor was incorporated into a universal throat-worn silent-speech recognition system that transforms the tiny signals of laryngeal mechanical vibrations into silent speech.

Electro-mechanochemical approach towards the chloro sulfoximidations of allenes under solvent-free conditions in a ball mill

An electro-mechanochemical protocol for the synthesis of vinylic sulfoximines has been developed. Utilising mechanochemically strained BaTiO₃ nanoparticles, the catalytic active system is generated in situ by the reduction of copper(II) chloride. Various combinations of electron-donating and -withdrawing groups are tolerated, and the approach leads to products with difunctionalised double bonds in good to excellent yields. Attempts to add a sulfoximidoyl chloride to an alkyne proved difficult. Additions of a sulfonyl iodide to allenes and alkynes proceeded smoothly in the presence of silica gel without the need for activation by a piezoelectric material.

Machine-Learning Assisted Handwriting Recognition Using Graphene Oxide-Based Hydrogel

Machine-learning assisted handwriting recognition is crucial for development of next-generation biometric technologies. However, most of the currently reported handwriting recognition systems are lacking in flexible sensing and machine learning capabilities, both of which are essential for implementation of intelligent systems. Herein, assisted by machine learning, we develop a new handwriting recognition system, which can be applied as both a recognizer for written texts and an encryptor for confidential information. This flexible and intelligent handwriting recognition system combines a printed circuit board with graphene oxide-based hydrogel sensors. It offers fast response and good sensitivity and allows high-precision recognition of handwritten content from a single letter to words and signatures. By analyzing 690 acquired handwritten signatures obtained from seven participants, we successfully demonstrate a fast recognition time (less than 1 s) and a high recognition rate (∼91.30%). Our developed handwriting recognition system has great potential in advanced human-machine interactions, wearable communication devices, soft robotics manipulators, and augmented virtual reality.