Large-area, untethered, metamorphic, and omnidirectionally stretchable multiplexing self-powered triboelectric skins.

Large-area metamorphic stretchable sensor networks are desirable in haptic sensing and next-generation electronics. Triboelectric nanogenerator-based self-powered tactile sensors in single-electrode mode constitute one of the best solutions with ideal attributes. However, their large-area multiplexing utilizations are restricted by severe misrecognition between sensing nodes and high-density internal circuits. Here, we provide an electrical signal shielding strategy delivering a large-area multiplexing self-powered untethered triboelectric electronic skin (UTE-skin) with an ultralow misrecognition rate (0.20%). An omnidirectionally stretchable carbon black-Ecoflex composite-based shielding layer is developed to effectively attenuate electrostatic interference from wirings, guaranteeing low-level noise in sensing matrices. UTE-skin operates reliably under 100% uniaxial, 100% biaxial, and 400% isotropic strains, achieving high-quality pressure imaging and multi-touch real-time visualization. Smart gloves for tactile recognition, intelligent insoles for gait analysis, and deformable human-machine interfaces are demonstrated. This work signifies a substantial breakthrough in haptic sensing, offering solutions for the previously challenging issue of large-area multiplexing sensing arrays.

An Environmental-Inert and Highly Self-Healable Elastomer Obtained via Double-Terminal Aromatic Disulfide Design and Zwitterionic Crosslinked Network for Use as a Triboelectric Nanogenerator.

Due to the ongoing development of portable/mobile electronics, sources to power have received widespread attention. Compared to chemical batteries as power sources, triboelectric nanogenerators (TENGs) possess lots of advantages, including the ability to harvest energy via human motions, flexible structures, environment-friendliness, and long-life characteristics. Although many self-healable TENGs are reported, the achievement of a muscle-like elasticity and the ability to recover from inevitable damage under extreme conditions (such as a high/low temperature and/or humidity) remain a challenge. Herein, a "double-terminal aromatic disulfide" on a structure with zwitterions as branched chains is reported to engineer the high-efficient self-healable elastomer for application in a flexible TENG. The as-designed material exhibits a repeatable elastic recovery (at 250% elongation) and a self-healing efficiency with an ultimate tensile stress of 96% over 2 h, representing an improvement on previously reported disulfide-based elastomers. The elastomer can autonomously recover by 50% even at a subzero temperature of -30 °C within 24 h. The elastomer-based TENG, as a self-driven sensor for detecting human behavior, is demonstrated to exhibit stable outputs and self-healing in the temperature range of -30 to 60 °C, and so is expected to promote the development of self-powered electronics for next-generation human-machine communications.

Landmark-Based Scale Estimation and Correction of Visual Inertial Odometry for VTOL UAVs in a GPS-Denied Environment.

With the rapid development of technology, unmanned aerial vehicles (UAVs) have become more popular and are applied in many areas. However, there are some environments where the Global Positioning System (GPS) is unavailable or has the problem of GPS signal outages, such as indoor and bridge inspections. Visual inertial odometry (VIO) is a popular research solution for non-GPS navigation. However, VIO has problems of scale errors and long-term drift. This study proposes a method to correct the position errors of VIO without the help of GPS information for vertical takeoff and landing (VTOL) UAVs. In the initial process, artificial landmarks are utilized to improve the positioning results of VIO by the known landmark information. The position of the UAV is estimated by VIO. Then, the accurate position is estimated by the extended Kalman filter (EKF) with the known landmark, which is used to obtain the scale correction using the least squares method. The Inertial Measurement Unit (IMU) data are used for integration in the time-update process. The EKF can be updated with two measurements. One is the visual odometry (VO) estimated directly by a landmark. The other is the VIO with scale correction. When the landmark is detected during takeoff phase, or the UAV is returning to the takeoff location during landing phase, the trajectory estimated by the landmark is used to update the scale correction. At the beginning of the experiments, preliminary verification was conducted on the ground. A self-developed UAV equipped with a visual-inertial sensor to collect data and a high-precision real time kinematic (RTK) to verify trajectory are applied to flight tests. The experimental results show that the method proposed in this research effectively solves the problems of scale and the long-term drift of VIO.

Filling the gap between topological insulator nanomaterials and triboelectric nanogenerators.

Reliable energy modules and higher-sensitivity, higher-density, lower-powered sensing systems are constantly required to develop wearable electronics and the Internet of Things technology. As an emerging technology, triboelectric nanogenerators have been potentially guiding the landscape of sustainable power units and energy-efficient sensors. However, the existing triboelectric series is primarily populated by polymers and rubbers, limiting triboelectric sensing plasticity to some extent owing to their stiff surface electronic structures. To enrich the current triboelectric group, we explore the triboelectric properties of the topological insulator nanofilm by Kelvin probe force microscopy and reveal its relatively positive electrification charging performance. Both the larger surface potential difference and the conductive surface states of the nanofilms synergistically improve the charge transfer behavior between the selected triboelectric media, endowing the topological insulator-based triboelectric nanogenerator with considerable output performance. Besides serving as a wearable power source, the ultra-compact device array demonstrates innovative system-level sensing capabilities, including precise monitoring of dynamic objects and real-time signal control at the human-machine interface. This work fills the blank between topological quantum matters and triboelectric nanogenerators and, more importantly, exploits the significant potential of topological insulator nanofilms for self-powered flexible/wearable electronics and scalable sensing technologies.

Achieving complementary resistive switching and multi-bit storage goals by modulating the dual-ion reaction through supercritical fluid-assisted ammoniation.

Complementary resistive switching (CRS) is a core requirement in memristor crossbar array construction for neuromorphic computing in view of its capability to avoid the sneak path current. However, previous approaches for implementing CRS are generally based on a complex device structure design and fabrication process or a meticulous current-limiting measurement procedure. In this study, a supercritical fluid-assisted ammoniation (SFA) process is reported to achieve CRS in a single device by endowing the original ordinary switching materials with dual-ion operation. In addition to self-compliant CRS behavior, a multi-bit storage function has also been achieved through the SFA process accompanied by superior retention and reliability. These substantial evolved resistive phenomena are elucidated attentively by our chemical reaction model and physical mechanism model corroborated by the material analysis and current conduction fitting analysis results. The findings in this research present the most efficient way to achieve CRS through only one chemical procedure with significantly improved device performance. Moreover, the supercritical fluid approach envisions tremendous possibilities for further development of materials and electric devices by a low-temperature process, with semiconductor fabrication compatibility and environmental friendliness.

Low-temperature supercritical dehydroxylation for achieving an ultra-low subthreshold swing of thin-film transistors.

Thin-film transistors (TFTs) have been widely used in the increasingly advanced field of displays. However, it remains a challenge for TFTs to overcome the poor subthreshold swing in the fast switching and high-speed applications. Here, we provide a solution to the above-mentioned challenge via supercritical dehydroxylation, which combines a low temperature, environmentally friendly supercritical fluid technology with a CaCl2 treatment. An embedded structure of amorphous indium gallium zinc oxide (a-IGZO) TFTs with double-layer high-k dielectric containing Ta2O5 and SiO2 layers was first manufactured. The subthreshold swing of the fabricated TFTs treated with supercritical dehydroxylation was optimized to an ultra-low value of 72.7 mV dec-1. Moreover, other key figures of merits including threshold voltage, on/off ratio and field effect mobility all improved after the supercritical dehydroxylation. The bandgap of the gate dielectric material increased due to the supercritical dehydroxylation verified by the current conduction mechanism. Besides, numerous material analyses further confirmed that owing to the supercritical dehydroxylation the dominant dehydration reactions can effectively repair the defects introduced in the device manufacture. The ultra-low subthreshold swing with optimized electrical performances can be achieved via the low-temperature supercritical dehydroxylation treatment, enabling its promising potential in realizing ultra-fast and low power electronics.

Energy Harvesting and Storage with Soft and Stretchable Materials.

This review highlights various modes of converting ambient sources of energy into electricity using soft and stretchable materials. These mechanical properties are useful for emerging classes of stretchable electronics, e-skins, bio-integrated wearables, and soft robotics. The ability to harness energy from the environment allows these types of devices to be tetherless, thereby leading to a greater range of motion (in the case of robotics), better compliance (in the case of wearables and e-skins), and increased application space (in the case of electronics). A variety of energy sources are available including mechanical (vibrations, human motion, wind/fluid motion), electromagnetic (radio frequency (RF), solar), and thermodynamic (chemical or thermal energy). This review briefly summarizes harvesting mechanisms and focuses on the materials' strategies to render such devices into soft or stretchable embodiments.

Unveiling the influence of surrounding materials and realization of multi-level storage in resistive switching memory.

Considerable efforts have been made to obtain better control of the switching behavior of resistive random access memory (RRAM) devices, such as using modified or multilayer switching materials. Although considerable progress has been made, the reliability and stability of the devices greatly deteriorate due to dispersed electric field caused by low permittivity surrounding materials. By introducing surrounding materials with a relatively higher dielectric constant, the RRAM devices become promising for cost-effective applications by achieving multilevel storage functionality and improved scalability. A device designed by this principle exhibits multiple distinct and non-volatile conductance states. Moreover, the issue of the increasing forming voltage during device scaling is also solved, improving the capacity of the chips and reducing the power dissipation in the process of the device miniaturization. The COMSOL simulation helps to reveal that the enhanced performance is correlated with a more concentrated electric field around the conductive filament, which is favorable for controlling the connection and rupture of the resistive filament.

Variable-temperature activation energy extraction to clarify the physical and chemical mechanisms of the resistive switching process.

This study investigates the physical and chemical mechanisms during the resistive switching process by means of obtaining the activation energy in the reaction procedure. From the electrochemical and electrical measurement analysis results of HfO2-based resistive random access memory (RRAM), it can be observed that the chemical reaction during the reset process is consistent with the first-order reaction law. The activation energy, Ea, is determined from the reaction rate constant k under a varying-temperature environment in the reset process. The whole reset chemical reaction process can be divided into five phases involving N-O bond breaking, O-O bond breaking and triple-step oxygen ion migration. The methodology of the activation energy determination carried out in this study showcases a distinct approach to elucidate the resistive switching mechanism of RRAM and offers insight into RRAM design for future potential application.

A Deep Reinforcement Learning-Based MPPT Control for PV Systems under Partial Shading Condition.

On the issues of global environment protection, the renewable energy systems have been widely considered. The photovoltaic (PV) system converts solar power into electricity and significantly reduces the consumption of fossil fuels from environment pollution. Besides introducing new materials for the solar cells to improve the energy conversion efficiency, the maximum power point tracking (MPPT) algorithms have been developed to ensure the efficient operation of PV systems at the maximum power point (MPP) under various weather conditions. The integration of reinforcement learning and deep learning, named deep reinforcement learning (DRL), is proposed in this paper as a future tool to deal with the optimization control problems. Following the success of deep reinforcement learning (DRL) in several fields, the deep Q network (DQN) and deep deterministic policy gradient (DDPG) are proposed to harvest the MPP in PV systems, especially under a partial shading condition (PSC). Different from the reinforcement learning (RL)-based method, which is only operated with discrete state and action spaces, the methods adopted in this paper are used to deal with continuous state spaces. In this study,DQN solves the problem with discrete action spaces, while DDPG handles the continuous action spaces. The proposed methods are simulated in MATLAB/Simulink for feasibility analysis. Further tests under various input conditions with comparisons to the classical Perturb and observe (P&O) MPPT method are carried out for validation. Based on the simulation results in this study, the performance of the proposed methods is outstanding and efficient, showing its potential for further applications.

Self-Powered, Self-Healed, and Shape-Adaptive Ultraviolet Photodetectors.

The emergence of self-healing devices in recent years has drawn a great amount of attention in both academics and industry. Self-healed devices can autonomically restore a rupture as unexpected destruction occurs, which can efficiently prolong the life span of the devices; hence, they have an enhanced durability and decreased replacement cost. As a result, integration of wearable devices with self-healed electronics has become an indispensable issue in smart wearable devices. In this study, we present the first self-powered, self-healed, and wearable ultraviolet (UV) photodetector based on the integration of agarose/poly(vinyl alcohol) (PVA) double network (DN) hydrogels, which have the advantages of good mechanical strength, self-healing ability, and tolerability of multiple types of damage. With the integration of a DN hydrogel substrate, the photodetector enables 90% of the initial efficiency to be restored after five healing cycles, and each rapid healing time is suppressed to only 10 s. The proposed device has several merits, including having an all spray coating, self-sustainability, biocompatibility, good sensitivity, mechanical flexibility, and an outstanding healing ability, which are all essential to build smart electronic systems. The unprecedented self-healed photodetector expands the future scope of electronic skin design, and it also offers a new platform for the development of next-generation wearable electronics.

Self-Healing Nanophotonics: Robust and Soft Random Lasers.

Self-healing technology promises a generation of innovation in cross-cutting subjects ranging from electronic skins, to wearable electronics, to point-of-care biomedical sensing modules. Recently, scientists have successfully pulled off significant advances in self-healing components including sensors, energy devices, transistors, and even integrated circuits. Lasers, one of the most important light sources, integrated with autonomous self-healability should be endowed with more functionalities and opportunities; however, the study of self-healing lasers is absent in all published reports. Here, the soft and self-healable random laser (SSRL) is presented. The SSRL can not only endure extreme external strain but also withstand several cutting/healing test cycles. Particularly, the damaged SSRL enables its functionality to be restored within just few minutes without the need of additional energy, chemical/electrical agents, or other healing stimuli, truly exhibiting a supple yet robust laser prototype. It is believed that SSRL can serve as a vital building block for next-generation laser technology as well as follow-on self-healing optoelectronics.

Waterproof Fabric-Based Multifunctional Triboelectric Nanogenerator for Universally Harvesting Energy from Raindrops, Wind, and Human Motions and as Self-Powered Sensors.

Developing nimble, shape-adaptable, conformable, and widely implementable energy harvesters with the capability to scavenge multiple renewable and ambient energy sources is highly demanded for distributed, remote, and wearable energy uses to meet the needs of internet of things. Here, the first single waterproof and fabric-based multifunctional triboelectric nanogenerator (WPF-MTENG) is presented, which can produce electricity from both natural tiny impacts (rain and wind) and body movements, and can not only serve as a flexible, adaptive, wearable, and universal energy collector but also act as a self-powered, active, fabric-based sensor. The working principle comes from a conjunction of contact triboelectrification and electrostatic induction during contact/separation of internal soft fabrics. The structural/material designs of the WPF-MTENG are systematically studied to optimize its performance, and its outputs under different conditions of rain, wind, and various body movements are comprehensively investigated. Its applicability is practically demonstrated in various objects and working situations to gather ambient energy. Lastly, a WPF-MTENG-based keypad as self-powered human-system interfaces is demonstrated on a garment for remotely controlling a music-player system. This multifunctional WPF-MTENG, which is as flexible as clothes, not only presents a promising step toward democratic collections of alternative energy but also provides a new vision for wearable technologies.

Actively Perceiving and Responsive Soft Robots Enabled by Self-Powered, Highly Extensible, and Highly Sensitive Triboelectric Proximity- and Pressure-Sensing Skins.

Robots that can move, feel, and respond like organisms will bring revolutionary impact to today's technologies. Soft robots with organism-like adaptive bodies have shown great potential in vast robot-human and robot-environment applications. Developing skin-like sensory devices allows them to naturally sense and interact with environment. Also, it would be better if the capabilities to feel can be active, like real skin. However, challenges in the complicated structures, incompatible moduli, poor stretchability and sensitivity, large driving voltage, and power dissipation hinder applicability of conventional technologies. Here, various actively perceivable and responsive soft robots are enabled by self-powered active triboelectric robotic skins (tribo-skins) that simultaneously possess excellent stretchability and excellent sensitivity in the low-pressure regime. The tribo-skins can actively sense proximity, contact, and pressure to external stimuli via self-generating electricity. The driving energy comes from a natural triboelectrification effect involving the cooperation of contact electrification and electrostatic induction. The perfect integration of the tribo-skins and soft actuators enables soft robots to perform various actively sensing and interactive tasks including actively perceiving their muscle motions, working states, textile's dampness, and even subtle human physiological signals. Moreover, the self-generating signals can drive optoelectronic devices for visual communication and be processed for diverse sophisticated uses.

Concurrent Harvesting of Ambient Energy by Hybrid Nanogenerators for Wearable Self-Powered Systems and Active Remote Sensing.

Harvesting energy available from ambient environment is highly desirable for powering personal electronics and health applications. Due to natural process and human activities, steam can be produced by boilers, human perspiration, and the wind exists ubiquitously. In the outdoor environment, these two phenomena usually exist at the same place, which contain heat and mechanical energies simultaneously. However, previous studies have isolated them as separate sources of energy to harvest and hence failed to utilize them effectively. Herein, we present unique hybrid nanogenerators for individually/simultaneously harvesting thermal energy from water vapors and mechanical energy from intermittent wind blowing from the bottom side, which consist of a wind-driven triboelectric nanogenerator (TENG) and pyroelectric-piezoelectric nanogenerators (PPENGs). The output power of the PPENG and the TENG can be up to about 184.32 µW and 4.74 mW, respectively, indicating the TENG plays the dominant role. Our hybrid nanogenerators could provide different applications such as to power digital watch and enable self-powered sensing with wireless transmission. The device could also be further integrated into a face mask for potentially wearable applications. This work not only provides a promising approach for renewable energy harvesting but also enriches potential applications for self-powered systems and wireless sensors.

Vitrimer Elastomer-Based Jigsaw Puzzle-Like Healable Triboelectric Nanogenerator for Self-Powered Wearable Electronics.

Functional polymers possess outstanding uniqueness in fabricating intelligent devices such as sensors and actuators, but they are rarely used for converting mechanical energy into electric power. Here, a vitrimer based triboelectric nanogenerator (VTENG) is developed by embedding a layer of silver nanowire percolation network in a dynamic disulfide bond-based vitrimer elastomer. In virtue of covalent dynamic disulfide bonds in the elastomer matrix, a thermal stimulus enables in situ healing if broken, on demand reconfiguration of shape, and assembly of more sophisticated structures of VTENG devices. On rupture or external damage, the structural integrity and conductivity of VTENG are restored under rapid thermal stimulus. The flexible and stretchable VTENG can be scaled up akin to jigsaw puzzles and transformed from 2D to 3D structures. It is demonstrated that this self-healable and shape-adaptive VTENG can be utilized for mechanical energy harvesters and self-powered tactile/pressure sensors with extended lifetime and excellent design flexibility. These results show that the incorporation of organic materials into electronic devices can not only bestow functional properties but also provide new routes for flexible device fabrication.

Shape Memory Polymers for Body Motion Energy Harvesting and Self-Powered Mechanosensing.

Growing demand in portable electronics raises a requirement to electronic devices being stretchable, deformable, and durable, for which functional polymers are ideal choices of materials. Here, the first transformable smart energy harvester and self-powered mechanosensation sensor using shape memory polymers is demonstrated. The device is based on the mechanism of a flexible triboelectric nanogenerator using the thermally triggered shape transformation of organic materials for effectively harvesting mechanical energy. This work paves a new direction for functional polymers, especially in the field of mechanosensation for potential applications in areas such as soft robotics, biomedical devices, and wearable electronics.

Dissolvable and Recyclable Random Lasers.

An integrated random laser based on green materials with dissolubility and recyclability is created and demonstrated. The dissolvable and recyclable random laser (DRRL) can be dissolved in water, accompanying the decay of emission intensity and the increment in lasing threshold. Furthermore, the DRRL can be reused after the process of deionized treatment, exhibiting excellent reproducibility with several recycling processes.

Validation of Energy Expenditure Prediction Models Using Real-Time Shoe-Based Motion Detectors.

OBJECTIVE: This study aimed to verify and compare the accuracy of energy expenditure (EE) prediction models using shoe-based motion detectors with embedded accelerometers. METHODS: Three physical activity (PA) datasets (unclassified, recognition, and intensity segmentation) were used to develop three prediction models. A multiple classification flow and these models were used to estimate EE. The "unclassified" dataset was defined as the data without PA recognition, the "recognition" as the data classified with PA recognition, and the "intensity segmentation" as the data with intensity segmentation. The three datasets contained accelerometer signals (quantified as signal magnitude area (SMA)) and net heart rate (HRnet). The accuracy of these models was assessed according to the deviation between physically measured EE and model-estimated EE. RESULTS: The variance between physically measured EE and model-estimated EE expressed by simple linear regressions was increased by 63% and 13% using SMA and HRnet, respectively. The accuracy of the EE predicted from accelerometer signals is influenced by the different activities that exhibit different count-EE relationships within the same prediction model. CONCLUSION: The recognition model provides a better estimation and lower variability of EE compared with the unclassified and intensity segmentation models. SIGNIFICANCE: The proposed shoe-based motion detectors can improve the accuracy of EE estimation and has great potential to be used to manage everyday exercise in real time.