Intelligent Cubic-Designed Piezoelectric Node (iCUPE) with Simultaneous Sensing and Energy Harvesting Ability toward Self-Sustained Artificial Intelligence of Things (AIoT)

Enhancing the performance of molecule-based piezoelectric sensors by optimizing their microstructures

By combining the rigidity of inorganic components with the flexibility of organic components, molecule-based ferroelectrics emerge as promising candidates for flexible, self-powered piezoelectric sensors. While it is well known that the performance of piezoelectric sensor devices depends not only on the materials' piezoelectric properties but also on the device architecture, research into enhancing molecule-based piezoelectric sensor performance through microstructure optimization has never been investigated. Here, we report the synthesis of a molecule-based ferroelectric, [(2-bromoethyl) trimethylammonium][GaBr4] ([(CH3)3NCH2CH2Br][GaBr4]) (1), which exhibits a piezoelectric coefficient (d 33) of up to 331 pC N-1. Our investigation reveals that the power density of a composite piezoelectric sensor device made from 1@S-PDMS(800#) (with microstructures) is twelve times that of 1-Flat-PDMS (without microstructures), due to a synergistic combination of piezoelectric and triboelectric effects. Interestingly, this flexible piezoelectric sensor can effectively detect human physiological signals, such as finger bending, breathing, and speech recognition, without the need for an external power supply.

Tactile Sensing and Rendering Patch with Dynamic and Static Sensing and Haptic Feedback for Immersive Communication

Wearable human-machine interface (HMI) with bidirectional and multimodal tactile information exchange is of paramount importance in teleoperation by providing more intuitive data interpretation and delivery of tactilely related signals. However, the current sensing and feedback devices still lack enough integration and modalities. Here, we present a Tactile Sensing and Rendering Patch (TSRP) that is made of a customized expandable array which consists of a piezoelectric sensing and feedback unit fused with an elastomeric triboelectric multidimensional sensor and its inner pneumatic feedback structure. The primary functional unit of TSRP is mainly featured with a soft silicone substrate with compact multilayer structure integrating static and dynamic multidimensional tactile sensing capabilities, which synergistically leverage both triboelectric and piezoelectric effects. Additionally, based on the air chamber created by the triboelectric sensor and the converse piezoelectric effect, it provides pneumatic and vibrational haptic feedback simultaneously for both static and dynamic perception regeneration. With the aid of the other variants of this unit, the array shaped TSRP is capable of simulating different terrains, geometries, sliding, collisions, and other critical interactive events during teleoperation via skin perception. Moreover, immediate manipulation can be done on TSRP through the tactile sensors. The preliminary demonstration of TSRP interface with a completed control module in robotic teleoperation is provided, which shows the feasibility of assisting certain tasks in a complex environment by direct tactile communication. The proposed device offers a potential method of enabling bidirectional tactile communication with enriched key information for improving interaction efficiency in the fields of robot teleoperation and training.

AI-Assisted Self-Powered Vehicle-Road Integrated Electronics for Intelligent Transportation Collaborative Perception

Collaborative perception between a vehicle and the road has the potential to enhance the limited perception capability of autonomous driving technologies. With this background, self-powered vehicle-road integrated electronics (SVRIE) with a multilevel fractal structure is designed to play a dual role, including a SVRIE device integrated into vehicle tires and a SVRIE array embedded into a road surface. The pressure sensing capability and anti-crosstalk performance of the SVRIE array are characterized separately to validate the feasibility of applying the SVRIE in a cooperative vehicle-infrastructure system. It is demonstrated that the SVRIE based on the multi-layered fractal structure exhibits maximum performance in collaborative sensing and interaction between vehicles and road information, such as vehicle motion, road surface condition, and tire life cycle health monitoring. Traditional data analysis methods are often of questionable accuracy. Therefore, a convolutional neural network is used to classify the vehicle and road conditions with accuracy of at least 88.3%. The transfer learning model is constructed to enhance the road surface identification capabilities with 100% accuracy. The accuracies of the vehicle tire motion recognition and tire health monitoring are 97% and 99%, respectively. This work provides new ideas for collaborative perception between vehicles and roadsides.

Piezoelectric Hinged Beam with Arc Mass Stopper for Vibration and Human Motion Energy Harvesting

Compact reliable structure and strong electromechanical coupling are hot pursuits in piezoelectric vibration energy harvester (PVEH) design. PVEH with a static arc stopper makes piezoelectric stress uniformly distributed and widens the frequency band by collision but wastes space. This Article proposes a hinged PVEH with two arc mass stoppers (AS-H-PVEH). Two arc stoppers as movable masses increase the vibration energy and the effective electromechanical coupling coefficient to achieve strong electromechanical coupling. AS-H-PVEH generates a 4.1 mW power output at 11.6-12.0 Hz and 0.2 g. AS-H-PVEH sustains 4 g acceleration vibration for 10 min without attenuation. To offset the resonance frequency increase caused by arc contact, we discuss the magnetic coupling, and axial force effects are discussed. The design of the arc stopper radius, nonlinear electromechanical coupling model, and system parameter identification method are presented. The displacement varied mechanical quality factor and effective electromechanical coupling coefficient are considered in the modified model for the first time. The model obtained good agreement under experiments. The power generation and driven wireless sensor performance of AS-H-PVEH was verified. This research has important theoretical and application value for the performance optimization of PVEH with an arc stopper.

Exploring Novel Sensor Design Ideas through Concentration-Induced Conformational Changes in PEG Single Chains

Polyethylene glycol (PEG) is an artificial polymer with good biocompatibility and a low cost, which has a wide range of applications. In this study, the dynamic response of PEG single chains to different ion concentrations was investigated from a microscopic point of view based on single-molecule force spectroscopy, revealing unique interactions that go beyond the traditional sensor-design paradigm. Under low concentrations of potassium chloride, PEG single chains exhibit a gradual reduction in rigidity, while, conversely, high concentrations induce a progressive increase in rigidity. This dichotomy serves as the cornerstone for a profound understanding of PEG conformational dynamics under diverse ion environments. Capitalizing on the remarkable sensitivity of PEG single chains to ion concentration shifts, we introduce innovative sensor-design ideas. Rooted in the adaptive nature of PEG single chains, these sensor designs extend beyond the traditional applications, promising advancements in environmental monitoring, healthcare, and materials science.

Mechanosensor for Proprioception Inspired by Ultrasensitive Trigger Hairs of Venus Flytrap

Mechanosensors, as the core component of a proprioceptive system, can detect many types of mechanical signals in their surroundings, such as force signals, displacement signals, and vibration signals. It is understandable that the development of an all-new mechanosensory structure that can be widely used is highly desirable. This is because it can markedly improve the detection performance of mechanosensors. Coincidentally, in nature, optimized microscale trigger hairs of Venus flytrap are ingeniously used as a mechanosensory structure. These trigger hairs are utilized for tactile mechanosensilla to efficiently detect external mechanical stimuli. Biological trigger hair-based mechanosensilla offer an all-new bio-inspired strategy. This strategy utilizes the notch structure and variable stiffness to enhance the perceptual performance of mechanosensors. In this study, the structure-performance-application coupling relationship of trigger hair-based mechanosensors is explored through experiment and analysis. An artificial trigger hair-based mechanosensor is developed by mimicking the deformation properties of the Venus flytrap trigger hair. This bio-inspired mechanosensor shows excellent performance in terms of mechanical stability, response time, and sensitivity to mechanical signals.

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.

Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform

The fourth industrial revolution has led to the development and application of health monitoring sensors that are characterized by digitalization and intelligence. These sensors have extensive applications in medical care, personal health management, elderly care, sports, and other fields, providing people with more convenient and real-time health services. However, these sensors face limitations such as noise and drift, difficulty in extracting useful information from large amounts of data, and lack of feedback or control signals. The development of artificial intelligence has provided powerful tools and algorithms for data processing and analysis, enabling intelligent health monitoring, and achieving high-precision predictions and decisions. By integrating the Internet of Things, artificial intelligence, and health monitoring sensors, it becomes possible to realize a closed-loop system with the functions of real-time monitoring, data collection, online analysis, diagnosis, and treatment recommendations. This review focuses on the development of healthcare artificial sensors enhanced by intelligent technologies from the aspects of materials, device structure, system integration, and application scenarios. Specifically, this review first introduces the great advances in wearable sensors for monitoring respiration rate, heart rate, pulse, sweat, and tears; implantable sensors for cardiovascular care, nerve signal acquisition, and neurotransmitter monitoring; soft wearable electronics for precise therapy. Then, the recent advances in volatile organic compound detection are highlighted. Next, the current developments of human-machine interfaces, AI-enhanced multimode sensors, and AI-enhanced self-sustainable systems are reviewed. Last, a perspective on future directions for further research development is also provided. In summary, the fusion of artificial intelligence and artificial sensors will provide more intelligent, convenient, and secure services for next-generation healthcare and biomedical applications.

A Wearable Multidimensional Motion Sensor for AI-Enhanced VR Sports

Regular exercise paves the way to a healthy life. However, conventional sports events are susceptible to weather conditions. Current motion sensors for home-based sports are mainly limited by operation power consumption, single-direction sensitivity, or inferior data analysis. Herein, by leveraging the 3-dimensional printing technique and triboelectric effect, a wearable self-powered multidimensional motion sensor has been developed to detect both the vertical and planar movement trajectory. By integrating with a belt, this sensor could be used to identify some low degree of freedom motions, e.g., waist or gait motion, with a high accuracy of 93.8%. Furthermore, when wearing the sensor at the ankle position, signals generated from shank motions that contain more abundant information could also be effectively collected. By means of a deep learning algorithm, the kicking direction and force could be precisely differentiated with an accuracy of 97.5%. Toward practical application, a virtual reality-enabled fitness game and a shooting game were successfully demonstrated. This work is believed to open up new insights for the development of future household sports or rehabilitation.