Smart farming with outdoor monitoring systems is critical to address food shortages and sustainability challenges. These systems facilitate informed decisions that enhance efficiency in broader environmental management. Existing outdoor systems equipped with energy harvesters and self-powered sensors often struggle with fluctuating energy sources, low durability under harsh conditions, non-transparent or non-biocompatible materials, and complex structures. Herein, a multifunctional hydrogel is developed, which can fulfill all the above requirements and build self-sustainable outdoor monitoring systems solely by it. It can serve as a stable energy harvester that continuously generates direct current output with an average power density of 1.9 W m−3 for nearly 60 days of operation in normal environments (24 °C, 60

Recent advances in virtual reality technologies accelerate the immersive interaction between human and augmented 3D virtual worlds. Here, the authors discuss olfactory feedback technologies that facilitate interaction with real and virtual objects and the evolution of wearable devices for immersive VR/AR applications. Recent advances in virtual reality technologies accelerate the immersive interaction between human and augmented 3D virtual worlds. Here, the authors discuss olfactory feedback technologies that facilitate interaction with real and virtual objects and the evolution of wearable devices for immersive VR/AR applications.

With growing interest in physical activities, wearable device sales have been increasing each year due to their help in tracking movement. However, keeping these devices running for a long time is still a key challenge. This paper presents an AI-enhanced backpack equipped with a hybrid vibration energy converter that integrates piezoelectric (PE) and electromagnetic (EM) technologies, featuring a double frequency-up conversion (FUC) mechanism. The PE unit enables efficient motion recognition, while the EM unit extends battery life via kinetic energy harvesting. The double FUC mechanism boosts human motion signal strength and resolution by converting sub-Hertz frequencies to hundreds of Hertz. This makes the device particularly effective in environments with performance constraints and interference. By adopting deep learning techniques, the system maintains a high motion recognition accuracy of 97.33% even under low data sampling rates and significant noise interference. The EM unit effectively harvests kinetic energy, generating an average output power of 4.5mW during activities such as running at 4.5Hz, despite of the device’s compact size. Additionally, a power management circuit with human motion-stimulated switch optimizes battery life by managing the system’s sleep and active states. This innovative energy management approach ensures that during human activity, the system conserves energy by remaining in sleep state for at least 30% of the time, significantly extending battery life and enhancing the backpack’s suitability for outdoor applications.

Electronic skins (E-Skins) are crucial for future robotics and wearable devices to interact with and perceive the real world. Prior research faces challenges in achieving comprehensive tactile perception and versatile functionality while keeping system simplicity for lack of multimodal sensing capability in a single sensor. Two kinds of tactile sensors, transient voltage artificial neuron (TVAN) and sustained potential artificial neuron (SPAN), featuring self-generated zero-biased signals are developed to realize synergistic sensing of multimodal information (vibration, material, texture, pressure, and temperature) in a single device instead of complex sensor arrays. Simultaneously, machine learning with feature fusion is applied to fully decode their output information and compensate for the inevitable instability of applied force, speed, etc, in real applications. Integrating TVAN and SPAN, the formed E-Skin achieves holistic touch awareness in only a single unit. It can thoroughly perceive an object through a simple touch without strictly controlled testing conditions, realize the capability to discern surface roughness from 0.8 to 1600 µm, hardness from 6HA to 85HD, and correctly distinguish 16 objects with temperature variance from 0 to 80 °C. The E-skin also features a simple and scalable fabrication process, which can be integrated into various devices for broad applications.

To implement digital-twin smart home applications, the mat sensing system based on triboelectric sensors is commonly used for gait information collection from daily activities. Yet traditional mat sensing systems often miss upper body motions and fail to adequately project these into the virtual realm, limiting their specific application scenarios. Herein, triboelectric mat multimodal sensing system is designed, enhanced with a commercial infrared imaging sensor, to capture diverse sensory information for sleep and emotion-relevant activity monitoring without compromising privacy. This system generates pixel-based area ratio mappings across the entire mat array, solely based on the integral operation of triboelectric outputs. Additionally, it utilizes multimodal sensory intelligence and deep-learning analytics to detect different sleeping postures and monitor comprehensive sleep behaviors and emotional states associated with daily activities. These behaviors are projected into the metaverse, enhancing virtual interactions. This multimodal sensing system, cost-effective and non-intrusive, serves as a functional interface for diverse digital-twin smart home applications such as healthcare, sports monitoring, and security.

To face the increasing requirement for grains as the global population continues to grow, improving both crop yield and quality has become essential. Plant health directly impacts crop quality and yield, making the development of plant health-monitoring technologies essential. Variable sensing technologies for outdoor/indoor farming based on different working principles have emerged as important tools for monitoring plants and their microclimates. These technologies can detect factors such as plant water content, volatile organic compounds (VOCs), and hormones released by plants, as well as environmental conditions like humidity, temperature, wind speed, and light intensity. To achieve comprehensive plant health monitoring for multidimensional assessment, multimodal sensors have been developed. Non-invasive monitoring approaches are also gaining attention, leveraging biocompatible and flexible sensors for plant monitoring without interference with its natural growth. Furthermore, wireless data transmission is crucial for real-time monitoring and efficient farm management. Reliable power supplies for these systems are vital to ensure continuous operation. By combining wearable sensors with intelligent data analysis and remote monitoring, modern agriculture can achieve refined management, resource optimization, and sustainable production, offering innovative solutions to global food security and environmental challenges.

Electromagnetically induced transparency (EIT) and absorption (EIA) are quantum interference phenomena occurring in multilevel atoms and inducing a sharp peak or valley within a broad transmission dip. Herein, we realize both EIA and EIT effects in hybrid mid-IR nanoantennas, and, for the first time, utilize the two inverse effects to enhance the signal strength of nanoantennas, thereby making breakthroughs in ultrasensitive sensing without additional fabrication complexity and cost. As demonstrated through CO2 gas detection, the performance of our EIA-EIT-integrated platform including signal strength, sensitivity, and limit of detection is comprehensively improved. Furthermore, the idea of using two inverse effects for sensing performance improvement is generally applicable to almost all EIA/EIT-like classical systems. Our work promises to have a profound impact on the spectroscopic sensing field.

With increasing requirements of the Internet of things (IoT) functioning with wireless sensor networks (WSN), a self-sustainable power supply has become an important pursuit in long-term working. The cantilever-based energy harvester is one of the most widely used devices for converting vibrations into electrical energy, while still challenged by restricted frequency bands toward practical applications. Here, a novel, feasible and cost-effective strategy for tuning the cantilever's resonant frequency is proposed. By applying 3D printed thin sheets as extensions, the original single vibration mode is promoted into two relatively independent vibration modes tuned with extensions' type, length, thickness, and proof masses. Hybridized piezoelectric-triboelectric nanogenerators are introduced to improve capacitor charging ability and potential vibration detection. Furthermore, a four-cantilever coupling design with different tuning parameters is investigated and enables wireless demonstrations of monitoring environmental temperature/humidity and carbon dioxide concentration, which is prospective as self-sustainable protection for workers in tunnels or underground constructions.

In this manuscript, we reported a self-generated e-skin with multimodal perception sensing enhanced by artificial intelligence. Inspired by the human skin, the proposed system contains the self-generated triboelectric sensor to mimic the fast adapting (FA) mechanoreceptors and the self-generated potentiometric sensor to mimic the slow adapting (SA) mechanoreceptors and thermoreceptors. By introducing deep learning with data fusion to extract features from multiple channels, the multimodal outputs can achieve more sensing functions and calibrate each other to overcome instability, such as applied force and speed. The surface temperature, roughness, texture, hardness, and material type measuring with high accuracy performed by a single element placed on the fingertip have been realized.

Matched loss and high-intensity near-field are critical for plasmonic nanoantennas to achieve ultrasensitive molecular detection. However, the increment in losses during the loss-matching process weakens the intensity of the near-field due to the dissipation of the radiated field. Herein, we develop a multi-hotspot (up to 6) strategy by coupling dark-mode antennas to bright-mode antennas to, for the first time, achieve loss matching while keeping high-intensity near-field simultaneously, which provides ultrasensitive optical elements for molecular detection. Furthermore, amino groups are introduced into metal-organic frameworks (MOFs) through post-synthetic modification to expand the chemisorption function of the MOFs while maintaining their structural integrity for physisorption. Augmented by the physi-chemisorption of MOF-polymer films, our hybrid platform achieves sub-ppm level gas detection. This work provides a new methodology for nanoantenna design and ultrasensitive infrared spectroscopy.

Facing the energy consumption of a huge number of distributed wireless Internet of Things (IoT) sensor nodes, scavenging energy from the ambient environment to power these devices is considered to be a promising method. Moreover, abundant energy sources of various types are widely distributed in the surrounding environment, which can be converted into electrical energy by micro-nano energy harvesters based on different mechanisms. In this review paper, we briefly introduce the development of different energy harvesters according to the classification of target energy sources, including microscale and nanoscale energy harvesters for vibrational energy sources, microscale energy harvesters for non-vibrational energy sources, and micro-nano energy harvesters for hybrid energy sources. Furthermore, the current advances and future prospects of the applications of micro-nano energy harvesters in event-based IoT systems and self-sustained systems are discussed.

The evolution of artificial intelligence of things (AIoT) drastically facilitates the development of a smart city via comprehensive perception and seamless communication. As a foundation, various AIoT nodes are experiencing low integration and poor sustainability issues. Herein, a cubic-designed intelligent piezoelectric AIoT node iCUPE is presented, which integrates a high-performance energy harvesting and self-powered sensing module via a micromachined lead zirconate titanate (PZT) thick-film-based high-frequency (HF)-piezoelectric generator (PEG) and poly(vinylidene fluoride-co-trifluoroethylene) (P(VDF-TrFE)) nanofiber thin-film-based low-frequency (LF)-PEGs, respectively. The LF-PEG and HF-PEG with specific frequency up-conversion (FUC) mechanism ensures continuous power supply over a wide range of 10-46 Hz, with a record high power density of 17 mW/cm3 at 1 g acceleration. The cubic design allows for orthogonal placement of the three FUC-PEGs to ensure a wide range of response to vibrational energy sources from different directions. The self-powered triaxial piezoelectric sensor (TPS) combined with machine learning (ML) assisted three orthogonal piezoelectric sensing units by using three LF-PEGs to achieve high-precision multifunctional vibration recognition with resolutions of 0.01 g, 0.01 Hz, and 2° for acceleration, frequency, and tilting angle, respectively, providing a high recognition accuracy of 98%-100%. This work proves the feasibility of developing a ML-based intelligent sensor for accelerometer and gyroscope functions at resonant frequencies. The proposed sustainable iCUPE is highly scalable to explore multifunctional sensing and energy harvesting capabilities under diverse environments, which is essential for AIoT implementation.

This paper has proposed a vibration energy harvester (VEH) with double frequency-up conversion (FUC) mechanism which can be applied to harvest energy from vibrations at ultra-low frequency. The device can initially up-convert the external vibrations with sub-Hertz into tens of Hertz, and further convert to hundreds of Hertz by the second FUC mechanism, which gives a high conversion ratio of 8400. A comprehensive dynamic model has been proposed and verified both from the theoretical analysis and COMSOL simulation to analyze the frequency conversion process and output voltage. When excited by a frequency of 0.2 Hz, an average output power of 75 μW could be obtained with a compact size. Benefiting from the non-contact design, the device could be applied in some sealed scenarios for smart city construction. Herein, a wireless humid-temperature sensor node in the pipeline was successfully powered by harvesting energy from humans walking with the proposed VEH, showing promising application prospects.

The advancement of the Internet of Things/5G infrastructure requires a low‐cost ubiquitous sensory network to realize an autonomous system for information collection and processing, aiming at diversified applications ranging from healthcare, smart home, industry 4.0 to environmental monitoring. The triboelectric nanogenerator (TENG) is considered the most promising technology due to its self‐powered, cost‐effective, and highly customizable advantages. Through the use of wearable electronic devices, advanced TENG technology is developed as a core technology enabling self‐powered sensors, power supplies, and data communications for the aforementioned applications. In this review, the advancements of TENG‐based electronics regarding materials, material/device hybridization, systems integration, technology convergence, and applications in healthcare, environment monitoring, transportation, and smart homes toward the future green earth are reported.

With the development of 5G and Internet of Things (IoT), the era of big data‐driven product design is booming. In addition, artificial intelligence (AI) is also emerging and evolving by recent breakthroughs in computing power and software architectures. In this regard, the digital twin, analyzing various sensor data with the help of AI algorithms, has become a cutting‐edge technology that connects the physical and virtual worlds, in which the various sensors are highly desirable to collect environmental information. However, although existing sensor technologies, including cameras, microphones, inertial measurement units, etc., are widely used as sensing elements for various applications, high‐power consumption and battery replacement of them is still a problem. Triboelectric nanogenerators (TENGs) as self‐powered sensors supply a feasible platform for realizing self‐sustainable and low‐power systems. Herein, the recent progress on TENG‐based intelligent systems, that is, wearable electronics, robot‐related systems, and smart homes, followed by prospective future development enabled by sensor fusion technology, is focused on. Finally, how to apply artificial intelligence to the design of intelligent sensor systems for the 5G and IoT era is discussed.

Smart toilet provides a feasible platform for the long-term analysis of person’s health. Common solutions for identification are based on camera or radio-frequency identification (RFID) technologies, but it is doubted for privacy issues. Here, we demonstrate an artificial intelligence of toilet (AI-toilet) based on a triboelectric pressure sensor array offering a more private approach with low cost and easily deployable software. The pressure sensor array attached on the toilet seat is composed of 10 textile-based triboelectric sensors, which can leverage the different pressure distribution of individual users' seating manner to get the biometric information. 6 users can be correctly identified with more than 90% accuracy using deep learning. The signals from pressure sensors also can be used for recording the seating time on the toilet. The system integrates a camera sensor to analyze the simulated urine by comparing with urine chart and classify the types and quantities of objects using deep learning. All information including two-factor user identification and entire seating time using pressure sensor array, and data from the urinalysis and stool analysis were automatically transferred to a cloud system and were further shown in user's mobile devices for better tracking their health status.

The increasing population of the elderly and motion-impaired people brings a huge challenge to our social system. However, the walking stick as their essential tool has rarely been investigated into its potential capabilities beyond basic physical support, such as activity monitoring, tracing, and accident alert. Here, we report a walking stick powered by ultra-low-frequency human motion and equipped with deep-learning-enabled advanced sensing features to provide a healthcare-monitoring platform for motion-impaired users. A linear-to-rotary structure is designed to achieve highly efficient energy harvesting from the linear motion of a walking stick with ultralow frequency. Besides, two kinds of self-powered triboelectric sensors are proposed and integrated to extract the motion features of the walking stick. Augmented sensing functionalities with high accuracies have been enabled by deep-learning-based data analysis, including identity recognition, disability evaluation, and motion status distinguishing. Furthermore, a self-sustainable Internet of Things (IoT) system with global positioning system tracing and environmental temperature and humidity amenity sensing functions is obtained. Combined with the aforementioned functionalities, this walking stick is demonstrated in various usage scenarios as a caregiver for real-time well-being status and activity monitoring. The caregiving walking stick shows the potential of being an intelligent aid for motion-impaired users to help them live life with adequate autonomy and safety.

In this paper, electret based vibration energy harvesters are designed and fabricated with perforated electrode based on MEMS technology. Through-holes are distributed on the fixed electrode in the device to optimize the energy harvesting process. The effect of the holes on the output power of the device are analyzed and discussed both in the finite element method (FEM) simulation and the experiments. It can be noticed that the through-holes can effectively lower the squeeze-film air damping force on the moveable proof mass at the atmosphere. Therefore, the energy loss due to the air damping could be reduced, and the output power of the device increases. The effects of the hole diameter and numbers on the output power of our device are also investigated in detail. By optimizing the configuration of the holes, the perforated device with the hole diameter of $400~\mu \text{m}$ and depth of $100~\mu \text{m}$ exhibits the highest power output at the low acceleration of 1.84 m/s 2 , which proves promising application for self-powered electronics in the future. [2020-0380]

In this manuscript, we reported two multi-functional units aiming at providing a promising monitoring platform applied in walking sticks for elderly and motion impaired people. One rotational unit equipped with an electromagnetic generator (EMG) and linear-to-rotary structure is proposed to harvest the ultra-low frequency linear motion of a walking stick and serve as the sustainable power supply for an Internet of Things (IoT) sensing system. And one hybridized unit further integrated with two self-powered triboelectric sensors to extract the motion features of the walking stick is designed to achieve multi-functional monitoring of users with deep learning technology. Promisingly, the walking stick equipped with proposed units shows a great potential of being an intelligent aid for motion-impaired users to help them live a life with adequate autonomy and safety.

Along with the arrival of the 5G era, sustainable and renewable energy supplies have become urgent demands towards plentifully distributed devices utilized for constructing smart cities. On the one hand, although the low entropy fossil energies are constantly transformed into high-entropy energies and consumed through various batteries, the large-scale combustion inevitably brings a huge carbon footprint and severe climate changes. On the other hand, mechanical energies are widely distributed in the urban environment in types of vibrations and rotations, and natural areas in types of water waves, rainfalls, and winds. With the development of mechanical energy harvesting technologies like triboelectric, piezoelectric, electromagnetic, etc., various mechanical energy harvesters with optimized structures and selected materials, including triboelectric nanogenerator (TENG), piezoelectric nanogenerator (PENG), electromagnetic generator (EMG), etc., have been demonstrated for efficiently extracting electric power from ubiquitous existed mechanical energy sources like wind, water waves/ flows, raindrops, vibrations, human motions, organs, and so on. With a comprehensive review of energy harvesting-assisted Internet of Things (IoT) applications among smart environmental monitoring (wind, ocean, and agriculture), smart transportations (drivers, vehicles, ships, roads, and bridges), smart homes (windows, floors, accessories, and human-machine interfaces), smart healthcare (wearable/portable devices, and implantable devices), the concept of smart cities are being promoted to conform with requirements of carbon neutrality and environment-friendly. Moreover, by combining developed self-powered sensor nodes, self-sustainable wireless sensor nodes, and self-charging energy storage units, the concept of IoT will be reinforced by increasing 5G endpoints and accelerates digitalization in smart cities.