Recent Progress in the Energy Harvesting Technology-From Self-Powered Sensors to Self-Sustained IoT, and New Applications

Construction of semiconductor nanocomposites for room-temperature gas sensors

Gas sensors are essential for ensuring public safety and improving quality of life. Room-temperature gas sensors are notable for their potential economic benefits and low energy consumption, and their expected integration with wearable electronics, making them a focal point of contemporary research. Advances in nanomaterials and low-dimensional semiconductors have significantly contributed to the enhancement of room-temperature gas sensors. These advancements have focused on improving sensitivity, selectivity, and response/recovery times, with nanocomposites offering distinct advantages. The discussion here focuses on the use of semiconductor nanocomposites for gas sensing at room temperature, and provides a review of the latest synthesis techniques for these materials. This involves the precise adjustment of chemical compositions, microstructures, and morphologies. In addition, the design principles and potential functional mechanisms are examined. This is crucial for deepening the understanding and enhancing the operational capabilities of sensors. We also highlight the challenges faced in scaling up the production of nanocomposite materials. Looking ahead, semiconductor nanocomposites are expected to drive innovation in gas sensor technology due to their carefully crafted design and construction, paving the way for their extensive use in various sectors.

A Review of Recent Advances in Human-Motion Energy Harvesting Nanogenerators, Self-Powering Smart Sensors and Self-Charging Electronics

In recent years, portable and wearable personal electronic devices have rapidly developed with increasing mass production and rising energy consumption, creating an energy crisis. Using batteries and supercapacitors with limited lifespans and environmental hazards drives the need to find new, environmentally friendly, and renewable sources. One idea is to harness the energy of human motion and convert it into electrical energy using energy harvesting devices-piezoelectric nanogenerators (PENGs), triboelectric nanogenerators (TENGs) and hybrids. They are characterized by a wide variety of features, such as lightness, flexibility, low cost, richness of materials, and many more. These devices offer the opportunity to use new technologies such as IoT, AI or HMI and create smart self-powered sensors, actuators, and self-powered implantable/wearable devices. This review focuses on recent examples of PENGs, TENGs and hybrid devices for wearable and implantable self-powered systems. The basic mechanisms of operation, micro/nano-scale material selection and manufacturing processes of selected examples are discussed. Current challenges and the outlook for the future of the nanogenerators are also discussed.

A High-Reliability Piezoelectric Tile Transducer for Converting Bridge Vibration to Electrical Energy for Smart Transportation

Piezoelectric energy transducers offer great potential for converting the vibrations of pedestrian footsteps or cars moving on a bridge or road into electricity. However, existing piezoelectric energy-harvesting transducers are limited by their poor durability. In this paper, to enhance this durability, a piezoelectric energy transducer with a flexible piezoelectric sensor is fabricated in a tile protype with indirect touch points and a protective spring. The electrical output of the proposed transducer is examined as a function of pressure, frequency, displacement, and load resistance. The maximum output voltage and maximum output power obtained were 6.8 V and 4.5 mW, respectively, at a pressure of 70 kPa, a displacement of 2.5 mm, and a load resistance of 15 kΩ. The designed structure limits the risk of destroying the piezoelectric sensor during operation. The harvesting tile transducer can work properly even after 1000 cycles. Furthermore, to demonstrate its practical applications, the tile was placed on the floor of an overpass and a walking tunnel. Consequently, it was observed that the electrical energy harvested from the pedestrian footsteps could power an LED light fixture. The findings suggest that the proposed tile offers promise with respect to harvesting energy produced during transportation.

Multilayered Helical Spherical Triboelectric Nanogenerator with Charge Shuttling for Water Wave Energy Harvesting

As an important part of natural resources, islands support the marine economy and build a blue barrier for marine ecological civilization. However, the power supply on these islands is difficult, limiting the development of marine internet of things (IoTs). In order to break the status quo, this work applies triboelectric nanogenerators (TENGs) to island power supply and ecological monitoring. A spherical TENG with two multilayered helical units is designed to harvest water wave energy, in which the space utilization rate reaches 92.5%. Then a charge shuttling mechanism is developed to improve the electrical output. The output current and power of a single TENG without power management reach 200.3 µA and 16.2 mW respectively, corresponding to a peak power density of 23.2 W m^-3 . Moreover, a scheme of the power managed TENG is proposed for realizing large-scale wave energy harvesting. The TENG is demonstrated to successfully power a water quality detector, a Bluetooth thermo-hygrometer, and an intelligent wireless alarm system for remote environmental monitoring. This work not only proposes a new type of TENG for water wave energy harvesting with improved performance, but also provides a new strategy for intelligent ocean IoTs, which even contributes to the carbon neutralization.

Piezoelectric energy harvesting systems using mechanical tuning techniques

In this review, we review the recent research progress and results of piezoelectric energy harvesters applying mechanical tuning techniques in terms of literature background, methods of mechanical tuning, and practical applications. In the past few decades, piezoelectric energy harvesting techniques and mechanical tuning techniques have received increasing attention and made significant progress. Mechanical-tuning techniques are those that allow the resonant vibration energy harvesters the mechanical resonant frequency values to be adjusted to coincide with the excitation frequency. According to the different tuning methods, this review classifies mechanical-tuning techniques based on magnetic action, different piezoelectric materials, axial load, the variable center of gravity, various stresses, and self-tuning and summarizes the corresponding research results, comparing the differences between the same methods. In addition, the current application of the mechanical-tuning techniques is introduced, and the future development of mechanical tuning techniques is analyzed, facilitating the reader to better understand how mechanical-tuning techniques can improve the output performance of energy harvesters.

Machine learning-augmented surface-enhanced spectroscopy toward next-generation molecular diagnostics

The world today is witnessing the significant role and huge demand for molecular detection and screening in healthcare and medical diagnosis, especially during the outbreak of COVID-19. Surface-enhanced spectroscopy techniques, including Surface-Enhanced Raman Scattering (SERS) and Infrared Absorption (SEIRA), provide lattice and molecular vibrational fingerprint information which is directly linked to the molecular constituents, chemical bonds, and configuration. These properties make them an unambiguous, nondestructive, and label-free toolkit for molecular diagnostics and screening. However, new issues in molecular diagnostics, such as increasing molecular species, faster spread of viruses, and higher requirements for detection accuracy and sensitivity, have brought great challenges to detection technology. Advancements in artificial intelligence and machine learning (ML) techniques show promising potential in empowering SERS and SEIRA with rapid analysis and automatic data processing to jointly tackle the challenge. This review introduces the combination of ML and SERS/SEIRA by investigating how ML algorithms can be beneficial to SERS/SEIRA, discussing the general process of combining ML and SEIRA/SERS, highlighting the molecular diagnostics and screening applications based on ML-combined SEIRA/SERS, and providing perspectives on the future development of ML-integrated SEIRA/SERS. In general, this review offers comprehensive knowledge about the recent advances and the future outlook regarding ML-integrated SEIRA/SERS for molecular diagnostics and screening.

A near-zero energy system based on a kinetic energy harvester for smart ranch

Smart ranch relying on sensor systems to realize monitoring of animals and the environment has emerged with the promotion of the Internet of Things (IoT). This paper proposes a near-zero energy system (NZES) based on a kinetic energy harvester (KEH) for smart ranch. The KEH is based on motion enhancement mechanism (MEM) for kinetic energy recovery from animal movement to realize self-powered applications of smart ranch. The MEM realizes the input and enhancement of weak kinetic energy based on bistable inertial swing. The KEH is analyzed theoretically and experimentally based on cattle leg movement. Under weak excitation (low-frequency and amplitude swing), the maximum voltage growth rate of the KEH based on the MEM reaches 103.7% compared with the linear KEH. The results of application feasibility tests, dressing field experiments, and application outlook show that the KEH has the potential to realize self-powered applications in the NZES of smart ranch.

Advanced Implantable Biomedical Devices Enabled by Triboelectric Nanogenerators

Implantable biomedical devices (IMDs) play essential roles in healthcare. Subject to the limited battery life, IMDs cannot achieve long-term in situ monitoring, diagnosis, and treatment. The proposal and rapid development of triboelectric nanogenerators free IMDs from the shackles of batteries and spawn a self-powered healthcare system. This review aims to overview the development of IMDs based on triboelectric nanogenerators, divided into self-powered biosensors, in vivo energy harvesting devices, and direct electrical stimulation therapy devices. Meanwhile, future challenges and opportunities are discussed according to the development requirements of current-level self-powered IMDs to enhance output performance, develop advanced triboelectric nanogenerators with multifunctional materials, and self-driven close-looped diagnosis and treatment systems.

Requirements for Energy-Harvesting-Driven Edge Devices Using Task-Offloading Approaches

Energy limitations remain a key concern in the development of Internet of Medical Things (IoMT) devices since most of them have limited energy sources, mainly from batteries. Therefore, providing a sustainable and autonomous power supply is essential as it allows continuous energy sensing, flexible positioning, less human intervention, and easy maintenance. In the last few years, extensive investigations have been conducted to develop energy-autonomous systems for the IoMT by implementing energy-harvesting (EH) technologies as a feasible and economically practical alternative to batteries. To this end, various EH-solutions have been developed for wearables to enhance power extraction efficiency, such as integrating resonant energy extraction circuits such as SSHI, S-SSHI, and P-SSHI connected to common energy-storage units to maintain a stable output for charge loads. These circuits enable an increase in the harvested power by 174% compared to the SEH circuit. Although IoMT devices are becoming increasingly powerful and more affordable, some tasks, such as machine-learning algorithms, still require intensive computational resources, leading to higher energy consumption. Offloading computing-intensive tasks from resource-limited user devices to resource-rich fog or cloud layers can effectively address these issues and manage energy consumption. Reinforcement learning, in particular, employs the Q-algorithm, which is an efficient technique for hardware implementation, as well as offloading tasks from wearables to edge devices. For example, the lowest reported power consumption using FPGA technology is 37 mW. Furthermore, the communication cost from wearables to fog devices should not offset the energy savings gained from task migration. This paper provides a comprehensive review of joint energy-harvesting technologies and computation-offloading strategies for the IoMT. Moreover, power supply strategies for wearables, energy-storage techniques, and hardware implementation of the task migration were provided.

Recent trends in 2D materials and their polymer composites for effectively harnessing mechanical energy

Self-powered wearable devices, with the energy harvester as a source of energy that can scavenge the energy from ambient sources present in our surroundings to cater to the energy needs of portable wearable electronics, are becoming more widespread because of their miniaturization and multifunctional characteristics. Triboelectric and piezoelectric nanogenerators are being explored to harvest electrical energy from the mechanical vibrations. Integration of these two effects to fabricate a hybrid nanogenerator can further enhance the output efficiency of the nanogenerator. Here, we have discussed the importance of 2D materials which plays an important role in the fabrication of nanogenerators because of their distinct characteristics, such as, flexibility, mechanical stability, nontoxicity, and biodegradability. This review mainly emphasizes the piezoelectric, triboelectric, and hybrid nanogenerator based on the 2D materials and their van der Waals heterostructure, as well as the effect of polymer-2D composite on the output performance of the nanogenerator.