Flexible Piezoelectric Acoustic Sensors and Machine Learning for Speech Processing

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.

Silent Speech Recognition with Strain Sensors and Deep Learning Analysis of Directional Facial Muscle Movement

Silent communication based on biosignals from facial muscle requires accurate detection of its directional movement and thus optimally positioning minimum numbers of sensors for higher accuracy of speech recognition with a minimal person-to-person variation. So far, previous approaches based on electromyogram or pressure sensors are ineffective in detecting the directional movement of facial muscles. Therefore, in this study, high-performance strain sensors are used for separately detecting x- and y-axis strain. Directional strain distribution data of facial muscle is obtained by applying three-dimensional digital image correlation. Deep learning analysis is utilized for identifying optimal positions of directional strain sensors. The recognition system with four directional strain sensors conformably attached to the face shows silent vowel recognition with 85.24% accuracy and even 76.95% for completely nonobserved subjects. These results show that detection of the directional strain distribution at the optimal facial points will be the key enabling technology for highly accurate silent speech recognition.

A Flexible Piezoelectric Device for Frequency Sensing from PVDF/SWCNT Composite Fibers

Polymer piezoelectric devices have been widely studied as sensors, energy harvesters, and generators with flexible and simple processes. Flexible piezoelectric devices are sensitive to external stimuli and are attracting attention because of their potential and usefulness as acoustic sensors. In this regard, the frequency sensing of sound must be studied to use flexible piezoelectric devices as sensors. In this study, a flexible piezoelectric device composed of a polymer and an electrode was successfully fabricated. Polyvinylidene fluoride, the active layer of the piezoelectric device, was prepared by electrospinning, and electrodes were formed by dip−coating in a prepared single−walled carbon nanotube dispersion. The output voltage of the external sound was matched with the input frequency through a fast Fourier transform, and frequency matching was successfully performed, even with mechanical stimulation. In a high−frequency test, the piezoelectric effect and frequency domain peak started to decrease sharply at 300 Hz, and the limit of the piezoelectric effect and sensing was observed from 800 Hz. The results of this study suggest a method for developing flexible piezoelectric-fiber frequency sensors based on piezoelectric devices for acoustic sensor systems.

Wide-Bandwidth Nanocomposite-Sensor Integrated Smart Mask for Tracking Multiphase Respiratory Activities

Wearing masks has been a recommended protective measure due to the risks of coronavirus disease 2019 (COVID-19) even in its coming endemic phase. Therefore, deploying a "smart mask" to monitor human physiological signals is highly beneficial for personal and public health. This work presents a smart mask integrating an ultrathin nanocomposite sponge structure-based soundwave sensor (≈400 µm), which allows the high sensitivity in a wide-bandwidth dynamic pressure range, i.e., capable of detecting various respiratory sounds of breathing, speaking, and coughing. Thirty-one subjects test the smart mask in recording their respiratory activities. Machine/deep learning methods, i.e., support vector machine and convolutional neural networks, are used to recognize these activities, which show average macro-recalls of ≈95% in both individual and generalized models. With rich high-frequency (≈4000 Hz) information recorded, the two-/tri-phase coughs can be mapped while speaking words can be identified, demonstrating that the smart mask can be applicable as a daily wearable Internet of Things (IoT) device for respiratory disease identification, voice interaction tool, etc. in the future. This work bridges the technological gap between ultra-lightweight but high-frequency response sensor material fabrication, signal transduction and processing, and machining/deep learning to demonstrate a wearable device for potential applications in continual health monitoring in daily life.

An Electret-Powered Skin-Attachable Auditory Sensor that Functions in Harsh Acoustic Environments

Auditory sensors have shortcomings with respect to not only personalization with wearability and portability but also detecting a human voice clearly in a noisy environment or when a mask covers the mouth. In this work, an electret-powered and hole-patterned polymer diaphragm is exploited into a skin-attachable auditory sensor. The optimized charged electret diaphragm induces a voltage bias of >400 V against the counter electrode, which reduces the necessity of a bulky power source and enables the capacitive sensor to show high sensitivity (2.2 V Pa^-1 ) with incorporation of an elastomer nanodroplet seismic mass. The sophisticated capacitive structure with low mechanical damping enables a flat frequency response (80-3000 Hz) and good linearity (50-80 dB_SPL ). The hole-patterned electret diaphragms help the skin-attachable sensor detect only neck-skin vibration rather than dynamic air pressure, enabling a person's voice to be detected in a harsh acoustic environment. The sensor operates reliably even in the presence of surrounding noise and when the user is wearing a gas mask. Therefore, the sensor shows strong potential of a communication tool for disaster response and quarantine activities, and of diagnosis tool for vocal healthcare applications such as cough monitoring and voice dosimetry.

A wave-confining metasphere beamforming acoustic sensor for superior human-machine voice interaction

Highly sensitive, source-tracking acoustic sensing is essential for effective and natural human-machine interaction based on voice. It is a known challenge to omnidirectionally track sound sources under a hypersensitive rate with low noise interference using a compact sensor. Here, we present a unibody acoustic metamaterial spherical shell with equidistant defected piezoelectric cavities, referred to as the metasphere beamforming acoustic sensor (MBAS). It demonstrates a wave-confining capability and low self-noise, simultaneously achieving an outstanding intrinsic signal-to-noise ratio (72 dB) and an ultrahigh sensitivity (137 mV_pp/Pa or -26.3 dBV), with a range spanning the daily phonetic frequencies (0 to 1500 Hz) and omnidirectional beamforming for the perception and spatial filtering of sound sources. Moreover, the MBAS-based auditory system is shown for high-performance audio cloning, source localization, and speech recognition in a noisy environment without any signal enhancement, revealing its promising applications in various voice interaction systems.

Enhancement and Function of the Piezoelectric Effect in Polymer Nanofibers

The realization of intelligent, self-powered components and devices exploiting the piezoelectric effect at large scale might greatly contribute to improve our efficiency in using resources, albeit a profound redesign of the materials and architectures used in current electronic systems would be necessary. Piezoelectricity is a property of certain materials to generate an electrical bias in response to a mechanical deformation. This effect enables energy to be harvested from strain and vibration modes, and to sustain the power of actuators, transducers, and sensors in integrated networks, such as those necessary for the Internet of Thing. Polymers, combining structural flexibility with lightweight construction and ease of processing, have been largely used in this framework. In particular, the poly(vinylidene fluoride) [PVDF, (CH₂CF₂) _n ] and its copolymers exhibit strong piezoelectric response, are biocompatibile, can endure large strains and can be easily shaped in the form of nanomaterials. Confined geometries, improving crystal orientation and enhancing piezoelectricity enable the fabrication of piezoelectric nanogenerators, which satisfy many important technological requirements, such as conformability, cheap fabrication, self-powering, and operation with low-frequency mechanical inputs (Hz scale). This account reports on piezoelectric polymer nanofibers made by electrospinning. This technique enables the formation of high-aspect-ratio filaments, such as nanowires and nanofibers, through the application of high electric fields (i.e., on the order of hundreds of kV/m) and stretching forces to a polymeric solution. The solution might be charged with functional, organic or inorganic, fillers or dopants. The solution is then fed at a controlled flow rate through a metallic spinneret or forms a bath volume, from which nanofibers are delivered. Fibers are then collected onto metallic surfaces, and upon a change of the collecting geometry, they can form nonwovens, controlled arrays, or isolated features. Nanofibers show unique features, which include their versatility in terms of achievable chemical composition and chemico-physical properties. In addition, electrospinning can be up-scaled for industrial production. Insight into the energy generation mechanism and how the interaction among fibers can be used to enhance the piezoelectric performance are given in this paper, followed by an overview of fiber networks as the active layer in different device geometries for sensing, monitoring, and signal recognition. The use of biodegradable polymers, both natural and synthetic, as critically important building blocks of the roadmap for next-generation piezoelectric devices, is also discussed, with some representative examples. In particular, biodegradable materials have been utilized for applications related to life science, such as the realization of active scaffolds and of electronic devices to be placed in intimate contact with living tissues and organs. Overall, these materials show many relevant properties that can be of very high importance for building next-generation, sustainable energy harvesting, self-rechargeable devices and electronic components, for use in several different fields.

Ionic shape memory polymer gels as multifunctional sensors

Novel ionic shape memory polymer (SMP) gels were fabricated using SMPs and ionic liquids (ILs) of 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide (EMI-TFSI) at different weight ratios (W_IL). The shape memory effect and sensor performance of the ionic SMP gels were investigated by means of thermomechanical and mechanoelectrical analyses. It was found that the ionic SMP gel at W_IL = 25 wt% showed a shape memory effect with the shape fixing ratio (R_f) and shape recovery ratio (R_r) of 72.7% and 72.9%, respectively. Upon bending, the ionic SMP gel sensors with PEDOT:PSS electrodes generated an open circuit voltage of 3.3 mV and a charge of 1.6 nC which linearly increased with increasing bending displacement and velocity, respectively. Furthermore, the wearable shape memory multifunctional sensor array was demonstrated as a self-powered motion sensor for IoT applications.

Automatic Assessment of Aphasic Speech Sensed by Audio Sensors for Classification into Aphasia Severity Levels to Recommend Speech Therapies

Aphasia is a type of speech disorder that can cause speech defects in a person. Identifying the severity level of the aphasia patient is critical for the rehabilitation process. In this research, we identify ten aphasia severity levels motivated by specific speech therapies based on the presence or absence of identified characteristics in aphasic speech in order to give more specific treatment to the patient. In the aphasia severity level classification process, we experiment on different speech feature extraction techniques, lengths of input audio samples, and machine learning classifiers toward classification performance. Aphasic speech is required to be sensed by an audio sensor and then recorded and divided into audio frames and passed through an audio feature extractor before feeding into the machine learning classifier. According to the results, the mel frequency cepstral coefficient (MFCC) is the most suitable audio feature extraction method for the aphasic speech level classification process, as it outperformed the classification performance of all mel-spectrogram, chroma, and zero crossing rates by a large margin. Furthermore, the classification performance is higher when 20 s audio samples are used compared with 10 s chunks, even though the performance gap is narrow. Finally, the deep neural network approach resulted in the best classification performance, which was slightly better than both K-nearest neighbor (KNN) and random forest classifiers, and it was significantly better than decision tree algorithms. Therefore, the study shows that aphasia level classification can be completed with accuracy, precision, recall, and F1-score values of 0.99 using MFCC for 20 s audio samples using the deep neural network approach in order to recommend corresponding speech therapy for the identified level. A web application was developed for English-speaking aphasia patients to self-diagnose the severity level and engage in speech therapies.

Coherent Precipitates with Strong Domain Wall Pinning in Alkaline Niobate Ferroelectrics

High-power piezoelectric applications are predicted to share approximately one-third of the lead-free piezoelectric ceramic market in 2024 with alkaline niobates as the primary competitor. To suppress self-heating in high-power devices due to mechanical loss when driven by large electric fields, piezoelectric hardening to restrict domain wall motion is required. In the present work, highly effective piezoelectric hardening via coherent plate-like precipitates in a model system of the (Li,Na)NbO₃ (LNN) solid solution delivers a reduction in losses, quantified as an electromechanical quality factor, by a factor of ten. Various thermal aging schemes are demonstrated to control the average size, number density, and location of the precipitates. The established properties are correlated with a detailed determination of short- and long-range atomic structure by X-ray diffraction and pair distribution function analysis, respectively, as well as microstructure determined by transmission electron microscopy. The impact of microstructure with precipitates on both small- and large-field properties is also established. These results pave the way to implement precipitate hardening in piezoelectric materials, analogous to precipitate hardening in metals, broadening their use cases in applications.

Prospects and Challenges of AI and Neural Network Algorithms in MEMS Microcantilever Biosensors

This paper focuses on the use of AI in various MEMS (Micro-Electro-Mechanical System) biosensor types. Al increases the potential of Micro-Electro-Mechanical System biosensors and opens up new opportunities for automation, consumer electronics, industrial manufacturing, defense, medical equipment, etc. Micro-Electro-Mechanical System microcantilever biosensors are currently making their way into our daily lives and playing a significant role in the advancement of social technology. Micro-Electro-Mechanical System biosensors with microcantilever structures have a number of benefits over conventional biosensors, including small size, high sensitivity, mass production, simple arraying, integration, etc. These advantages have made them one of the development avenues for high-sensitivity sensors. The next generation of sensors will exhibit an intelligent development trajectory and aid people in interacting with other objects in a variety of scenario applications as a result of the active development of artificial intelligence (AI) and neural networks. As a result, this paper examines the fundamentals of the neural algorithm and goes into great detail on the fundamentals and uses of the principal component analysis approach. A neural algorithm application in Micro-Electro-Mechanical System microcantilever biosensors is anticipated through the associated application of the principal com-ponent analysis approach. Our investigation has more scientific study value, because there are currently no favorable reports on the market regarding the use of AI with Micro-Electro-Mechanical System microcantilever sensors. Focusing on AI and neural networks, this paper introduces Micro-Electro-Mechanical System biosensors using artificial intelligence, which greatly promotes the development of next-generation intelligent sensing systems, and the potential applications and prospects of neural networks in the field of microcantilever biosensors.

Machine learning-enabled optimization of extrusion-based 3D printing

Machine learning (ML) and three-dimensional (3D) printing are among the fastest-growing branches of science. While ML can enable computers to independently learn from available data to make decisions with minimal human intervention, 3D printing has opened up an avenue for modern, multi-material, manufacture of complex 3D structures with a rapid turn-around ability for users with limited manufacturing experience. However, the determination of optimum printing parameters is still a challenge, increasing pre-printing process time and material wastage. Here, we present the first integration of ML and 3D printing through an easy-to-use graphical user interface (GUI) for printing parameter optimization. Unlike the widely held orthogonal design used in most of the 3D printing research, we, for the first time, used nine different computer-aided design (CAD) images and in order to enable ML algorithms to distinguish the difference between designs, we devised a self-designed method to calculate the "complexity index" of CAD designs. In addition, for the first time, the similarity of the print outcomes and CAD images are measured using four different self-designed labeling methods (both manually and automatically) to figure out the best labeling method for ML purposes. Subsequently, we trained eight ML algorithms on 224 datapoints to identify the best ML model for 3D printing applications. The "gradient boosting regression" model yields the best prediction performance with an R-2 score of 0.954. The ML-embedded GUI developed in this study enables users (either skilled or unskilled in 3D printing and/or ML) to simply upload a design (desired to print) to the GUI along with desired printing temperature and pressure to obtain the approximate similarity in the case of actual 3D printing of the uploaded design. This ultimately can prevent error-and-trial steps prior to printing which in return can speed up overall design-to-end-product time with less material waste and more cost-efficiency.

Artificial tactile perception smart finger for material identification based on triboelectric sensing

Tactile perception includes the direct response of tactile corpuscles to environmental stimuli and psychological parameters associated with brain recognition. To date, several artificial haptic-based sensing techniques can accurately measure physical stimuli. However, quantifying the psychological parameters of tactile perception to achieve texture and roughness identification remains challenging. Here, we developed a smart finger with surpassed human tactile perception, which enabled accurate identification of material type and roughness through the integration of triboelectric sensing and machine learning. In principle, as each material has different capabilities to gain or lose electrons, a unique triboelectric fingerprint output will be generated when the triboelectric sensor is in contact with the measured object. The construction of a triboelectric sensor array could further eliminate interference from the environment, and the accuracy rate of material identification was as high as 96.8%. The proposed smart finger provides the possibility to impart artificial tactile perception to manipulators or prosthetics.

The Progress of Research into Flexible Sensors in the Field of Smart Wearables

In modern society, technology associated with smart sensors made from flexible materials is rapidly evolving. As a core component in the field of wearable smart devices (or 'smart wearables'), flexible sensors have the advantages of excellent flexibility, ductility, free folding properties, and more. When choosing materials for the development of sensors, reduced weight, elasticity, and wearer's convenience are considered as advantages, and are suitable for electronic skin, monitoring of health-related issues, biomedicine, human-computer interactions, and other fields of biotechnology. The idea behind wearable sensory devices is to enable their easy integration into everyday life. This review discusses the concepts of sensory mechanism, detected object, and contact form of flexible sensors, and expounds the preparation materials and their applicability. This is with the purpose of providing a reference for the further development of flexible sensors suitable for wearable devices.

Red carbon dot directed biocrystalline alignment for piezoelectric energy harvesting

Herein, using chitin-derived chitosan, we first demonstrate the luminous carbon dot-directed large-scale biocrystalline piezo-phase alignment. This further significantly facilitates the piezo-energy harvesting of Earth-abundant natural biopolymers. A very small, yet moderate, number of red-emission carbon quantum dots (R-CQDs) allow a highly preferential macroscopic alignment of chitosan based, electrospun hybrid nanofibers and a highly preferential microscopic alignment of internal chitosan piezo-phase crystalline lamellae. Meanwhile, R-CQD hybridized bionanofibers maintain the long-wavelength photoluminescence excitation/emission of encapsulated, monodisperse R-CQDs. The piezoelectric voltage output and piezoelectric current output of hybrid bionanofibers reach up to 125 V cm^-3 and 1.5 μA cm^-3, respectively. They are more than 5 and 6 times higher than those of the state-of-the-art pristine ones, respectively. Moreover, the proof-of-concept red-emission bionanofibrous piezoelectric nanogenerator shows a highly durable, highly stable, and highly reproducible piezoresponse in over 10 000 continuous load cycles. As a reliable renewable energy source, it demonstrates the fast charging of external capacitors and the direct operation of commercial electronics. In particular, as a self-powered wearable tactile healthcare sensor, it attains ultrahigh mechanosensitivity in sensing a broad range of human biophysiological pressures and strains.

A versatile acoustically active surface based on piezoelectric microstructures

We demonstrate a versatile acoustically active surface consisting of an ensemble of piezoelectric microstructures that are capable of radiating and sensing acoustic waves. A freestanding microstructure array embossed in a single step on a flexible piezoelectric sheet of polyvinylidene fluoride (PVDF) leads to high-quality acoustic performance, which can be tuned by the design of the embossed microstructures. The high sensitivity and large bandwidth for sound generation demonstrated by this acoustically active surface outperform previously reported thin-film loudspeakers using PVDF, PVDF copolymers, or voided charged polymers without microstructures. We further explore the directivity of this device and its use on a curved surface. In addition, high-fidelity sound perception is demonstrated by the surface, enabling its microphonic application for voice recording and speaker recognition. The versatility, high-quality acoustic performance, minimal form factor, and scalability of future production of this acoustically active surface can lead to broad industrial and commercial adoption for this technology.