Dual-sensory fusion self-powered triboelectric taste-sensing system towards effective and low-cost liquid identification

Rational Design of Triboelectric Materials and Devices for Self-Powered Food Sensing

Against the backdrop of rapid advancements in 5G and Internet of Things (IoT) technologies, there is an urgent need to upgrade food sensing systems to achieve automation, digitalization, and intelligence. However, this transformation process faces numerous challenges. Triboelectric nanogenerators (TENGs), as an emerging energy conversion and sensing technology, play a crucial role in this context. They not only provide power to functional devices but also serve as sensors in multifunctional self-powered food sensing systems, capable of detecting various physical and chemical information. This review explores the development of TENGs in the field of food sensing, focusing on the working principles of their self-powered sensing. The review also systematically organizes and classifies the material and device designs used for TENGs in various food applications. Based on the performance of TENGs, a detailed introduction is provided on the specific applications of self-powered food sterilization, self-powered food quality monitoring, and self-powered taste sensing in the field of food safety. Finally, this paper discusses the challenges and corresponding strategies of TENGs in the food sensing field. The aim is to further promote unmanned and smart services and management in the food sector and to provide new research perspectives.

A self-powered droplet sensor based on a triboelectric nanogenerator toward the concentration of green tea polyphenols

Self-powered liquid droplet sensors based on triboelectric nanogenerators have attracted extensive attention in the field of biochemical sensing applications. Numerous research studies have investigated the effects of factors such as molecular species, molecular concentration, molecular charge, and molecular dipole moment in solution on the output electrical signals of the sensor. In this study, we prepared a self-powered droplet sensor using conductive copper film tape, polytetrafluoroethylene, and conductive aluminum foil tape. The sensor can continuously output pulsed electrical signals with minimal environmental impact. In comparison with other types of sensors, this sensor boasts a rapid response time of 10 ms and excellent sensitivity. The relationship between the friction-induced output current and voltage of the droplets and the concentration of green tea polyphenols (GTPs) was studied using the self-powered liquid droplet sensor with five different green tea samples. It was found that GTPs were the main factor contributing to the changes in output electrical signals in green tea water droplets. Fluorescence spectroscopy was used to reveal that the magnitude of the output current was inversely proportional to the concentration of GTPs in green tea. These results demonstrate the potential application of self-powered liquid droplet sensors in biochemical sensing applications based on concentration-dependent output signals.

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.

Nanocomposite Electret Layer Improved Long-Term Stable Solid-Liquid Contact Triboelectric Nanogenerator for Water Wave Energy Harvesting

With the continuous rise of environmental pollution and energy crisis, the global energy revolution is risen. Development of renewable blue energy based on the emerging promising triboelectric nanogenerators (TENG) has become an important direction of future energy development. The solid-liquid contact triboelectric nanogenerator (TENG) has the advantages of flexible structure, easy manufacture, and long-term stability, which makes it easier to integrate and achieve large-scale conversion of wave mechanical energy. However, the electric power output is still not large enough, which limits its practical applications. In this work, a nanocomposite electret layer enhanced solid-liquid contact triboelectric nanogenerator (E-TENG) is proposed for water wave energy harvesting, which can effectively improve the electric output and achieve real-time power supply of wireless sensing. Through introducing a nanocomposite electret layer into flexible multilayer solid-liquid contact TENG, higher power output is achieved. The E-TENG (active size of 50 mm × 49 mm) shows desired output performance, a power density of 521 mW m-2. The generated electric energy can drive wireless temperature sensing by transmitting wireless signals carrying detection information at the period of ˂5.5 min. This research greatly improves the electric output and provides a solid basis for the industrialization of TENG in blue energy.

Bioinspired integrated triboelectric electronic tongue

An electronic tongue (E-tongue) comprises a series of sensors that simulate human perception of taste and embedded artificial intelligence (AI) for data analysis and recognition. Traditional E-tongues based on electrochemical methods suffer from a bulky size and require larger sample volumes and extra power sources, limiting their applications in in vivo medical diagnosis and analytical chemistry. Inspired by the mechanics of the human tongue, triboelectric components have been incorporated into E-tongue platforms to overcome these limitations. In this study, an integrated multichannel triboelectric bioinspired E-tongue (TBIET) device was developed on a single glass slide chip to improve the device's taste classification accuracy by utilizing numerous sensory signals. The detection capability of the TBIET was further validated using various test samples, including representative human body, environmental, and beverage samples. The TBIET achieved a remarkably high classification accuracy. For instance, chemical solutions showed 100% identification accuracy, environmental samples reached 98.3% accuracy, and four typical teas demonstrated 97.0% accuracy. Additionally, the classification accuracy of NaCl solutions with five different concentrations reached 96.9%. The innovative TBIET exhibits a remarkable capacity to detect and analyze droplets with ultrahigh sensitivity to their electrical properties. Moreover, it offers a high degree of reliability in accurately detecting and analyzing various liquid samples within a short timeframe. The development of a self-powered portable triboelectric E-tongue prototype is a notable advancement in the field and is one that can greatly enhance the feasibility of rapid on-site detection of liquid samples in various settings.

Multimodal Sensors Enabled Autonomous Soft Robotic System with Self-Adaptive Manipulation

Human hands are amazingly skilled at recognizing and handling objects of different sizes and shapes. To date, soft robots rarely demonstrate autonomy equivalent to that of humans for fine perception and dexterous operation. Here, an intelligent soft robotic system with autonomous operation and multimodal perception ability is developed by integrating capacitive sensors with triboelectric sensor. With distributed multiple sensors, our robot system can not only sense and memorize multimodal information but also enable an adaptive grasping method for robotic positioning and grasp control, during which the multimodal sensory information can be captured sensitively and fused at feature level for crossmodally recognizing objects, leading to a highly enhanced recognition capability. The proposed system, combining the performance and physical intelligence of biological systems (i.e., self-adaptive behavior and multimodal perception), will greatly advance the integration of soft actuators and robotics in many fields.

Triboelectric Nanogenerators Based on Fluid Medium: From Fundamental Mechanisms toward Multifunctional Applications

Fluid-based triboelectric nanogenerators (FB-TENGs) are at the forefront of promising energy technologies, demonstrating the ability to generate electricity through the dynamic interaction between two dissimilar materials, wherein at least one is a fluidic medium (such as gas or liquid). By capitalizing on the dynamic and continuous properties of fluids and their interface interactions, FB-TENGs exhibit a larger effective contact area and a longer-lasting triboelectric effect in comparison to their solid-based counterparts, thereby affording longer-term energy harvesting and higher-precision self-powered sensors in harsh conditions. In this review, various fluid-based mechanical energy harvesters, including liquid-solid, gas-solid, liquid-liquid, and gas-liquid TENGs, have been systematically summarized. Their working mechanism, optimization strategies, respective advantages and applications, theoretical and simulation analysis, as well as the existing challenges, have also been comprehensively discussed, which provide prospective directions for device design and mechanism understanding of FB-TENGs.

Synergizing Machine Learning Algorithm with Triboelectric Nanogenerators for Advanced Self-Powered Sensing Systems

The advancement of the Internet of Things (IoT) has increased the demand for large-scale intelligent sensing systems. The periodic replacement of power sources for ubiquitous sensing systems leads to significant resource waste and environmental pollution. Human staffing costs associated with replacement also increase the economic burden. The triboelectric nanogenerators (TENGs) provide both an energy harvesting scheme and the possibility of self-powered sensing. Based on contact electrification from different materials, TENGs provide a rich material selection to collect complex and diverse data. As the data collected by TENGs become increasingly numerous and complex, different approaches to machine learning (ML) and deep learning (DL) algorithms have been proposed to efficiently process output signals. In this paper, the latest advances in ML algorithms assisting solid-solid TENG and liquid-solid TENG sensors are reviewed based on the sample size and complexity of the data. The pros and cons of various algorithms are analyzed and application scenarios of various TENG sensing systems are presented. The prospects of synergizing hardware (TENG sensors) with software (ML algorithms) in a complex environment and their main challenges for future developments are discussed.