Search Results (589)

Self-powered monitoring systems capable of scavenging ambient mechanical energy are a highly desirable solution to eliminate the reliance on batteries and grid power in remote and distributed atmospheric sensing networks. However, the widespread adoption of such systems is severely hindered by the insufficient output power density of current energy harvesters, which struggle to simultaneously drive environmental sensors, data acquisition units, and wireless transmission modules. In this work, we report a highly integrated hybrid power generation system that couples a triboelectric nanogenerator (TENG) and an electromagnetic generator (EMG) to efficiently harvest low-frequency mechanical energy from the surroundings. Through systematic structural optimization and synergistic matching of the two transduction mechanisms, the device achieves an outstanding volumetric power density of 129.9 W·m⁻³, which represents one of the highest values ever reported for hybrid nanogenerators targeting self-powered environmental applications. The output characteristics of both the TENG and EMG units under varying load impedances are thoroughly characterized, revealing the optimal operating points for maximum power extraction. A tailored power management module, consisting of rectification, energy storage, and regulation circuits, is designed to convert the irregular alternating output into a stable direct-current supply. To demonstrate the practical viability of the system, we construct a complete self-powered atmospheric environment monitoring node, which integrates multiple environmental sensors, a data acquisition module, and a wireless transmission module. Driven exclusively by the hybrid TENG–EMG generator under ambient mechanical excitation, the node successfully performs real-time sensing, signal processing, and remote data communication without any external power input. This work not only provides a record-high power density among hybrid generators for environmental monitoring, but also establishes a feasible pathway toward maintenance-free, widely distributed, and truly autonomous atmospheric sensing networks. The presented strategy of maximizing volumetric power density through hybrid design and impedance engineering can be readily extended to other self-powered systems. Full article

Pyroelectric nanogenerators (PyNGs) have attracted extensive attention for converting thermal energy into electricity, yet their low output power remains a critical bottleneck hindering practical use. This review summarizes various pyroelectric materials and device structures, elucidates the working principle, and discusses their output performances and application scenarios. The correlation between device output and key factors, including intrinsic material properties, electrode dimensions, and external thermal excitation, is systematically examined. Hybrid nanogenerators (HNGs) that couple pyroelectric with piezoelectric, triboelectric, and photovoltaic effects are also reviewed. In addition, the evaluation criteria for pyroelectric energy conversion efficiency are examined, highlighting the need for more systematic studies in this aspect. Finally, key challenges and corresponding strategies are discussed to facilitate the practical deployment of PyNGs in areas such as wearable electronics and self-powered sensors. Full article

The Droplets Triboelectric Nanogenerator (DTENG) possess distinctive merits in harvesting ambient hydropower into usable electricity. Nevertheless, droplet spreading, contact separation behavior, and dynamic interfacial interactions on insulating film surfaces are extremely sensitive to external environmental factors, giving rise to complicated nonlinear output characteristics. Herein, this work reports a droplet-driven TENG based on fluorinated ethylene propylene (FEP) thin films. We systematically explore how electrode geometry, droplet falling height, substrate inclination angle, and droplet flow rate modulate electrical output performance, and further clarify the fluid-triboelectric electron transfer between droplet hydrodynamic evolution and electric signal generation. Notably, we identify the retraction current during droplet recession, a signal largely neglected in previous solid–liquid TENG research, which complements the fundamental mechanism of interfacial charge transfer. This work not only provides a systematic experimental basis for understanding the working mechanism of DTENG, but also lays a theoretical and practical foundation for developing efficient and controllable water energy collection and self-powered sensor systems. Full article

During coal mine fracturing operations, real-time monitoring of the vibration frequency of the drilling assembly is crucial for assessing crack development, optimizing fracturing parameters, and ensuring the safety of downhole equipment. However, traditional active vibration sensors are limited by their reliance on external power supplies in the complex environment of underground mining, reducing their operational efficiency and effectiveness. Accordingly, a self-powered Z-shaped vibration sensor based on hybrid triboelectric and electromagnetic mechanisms was developed for monitoring coal mine fracturing drilling. This sensor utilizes the vibrations of the drilling tool to induce frictional electric pulse signals that correspond to the vibration frequency, enabling simultaneous vibration monitoring and energy generation. Experimental results demonstrate the stable performance of the proposed sensor under thermal conditions up to 150 °C and moisture levels reaching 90% relative humidity. The proposed sensor exhibits an operating frequency range of 0 to 11 Hz, with the measurement deviation constrained within a 5% threshold. Under optimal impedance matching, the triboelectric and electromagnetic units deliver peak power outputs of 0.04 mW and 110.5 mW when connected to external loads of 10⁸ Ω and 3.3 × 10² Ω respectively. The proposed hybrid self-powered sensor uses the high-amplitude pulsed voltage signals generated by the TENG unit for vibration frequency identification, while the EMG unit harvests mechanical energy from low-frequency vibrations, thereby enhancing the self-powered capability of the sensor for underground vibration monitoring in coal-mine hydraulic fracturing drilling. Full article

Regenerated cellulose is a promising tribopositive material for sustainable triboelectric nanogenerators (TENGs), although its electrical output remains sensitive to surface and interfacial properties. In this study, regenerated cellulose was modified using atomic layer deposition (ALD) of Al₂O₃, TiO₂, and ZnO to investigate how nanoscale oxide coatings influence triboelectric performance against a tribonegative PTFE counter layer. Two deposition regimes were examined: 7 ALD cycles, representing the early stage of ALD growth, and 200 cycles, representing a more developed coating regime. Triboelectric measurements, dielectric spectroscopy, structural characterization and contact angle analysis, were used to evaluate how ALD modification influences the electrical response of regenerated cellulose. All ALD-modified samples exhibited increased surface charge density and power output compared to unmodified cellulose, while also showing improved retention of triboelectric performance at elevated relative humidity. The 7-cycle samples consistently outperformed the corresponding 200-cycle coatings under low-humidity conditions, whereas the 200-cycle ZnO sample exhibited the highest humidity stability. No direct correlation between wettability and triboelectric output was observed. The results suggest that relatively small interfacial modifications introduced by ALD are sufficient to influence both the triboelectric response and humidity-dependent charge dissipation behavior of regenerated cellulose. Full article

Triboelectric nanogenerators (TENG) show significant potential in pressure sensing by converting mechanical disturbances into electrical signals positively correlated with the magnitude of the applied force, yet their development as practical pressure sensors is severely hindered by the major drawback of only detecting transient mechanical inputs. Additionally, traditional dual-mode pressure sensors have typically required complex multilayer structures and time-consuming fabrication processes. Here, a simple dual-mode pressure sensor of novel structure integrated with TENG and anodic aluminum oxide (AAO) for both dynamic and static pressure detection is proposed. Nanoporous AAO is directly grown on an aluminum substrate to simplify the traditionally complex multi-layer structure of dual-mode pressure sensors. The AAO layer serves a dual functionality by acting as an active triboelectric layer that significantly enhances the triboelectric output performance while concurrently functioning as the capacitive dielectric layer. A polydimethylsiloxane (PDMS) film is employed as the elastic counterpart to pair with the AAO substrate. The influence of PDMS thickness on the charge accumulation and extraction of the TENG mode is investigated to optimize the device output. Under optimal configurations, the streamlined Al-AAO/PDMS sensor demonstrates good sensitivity and linearity (R² > 0.99) for both dynamic triboelectric voltage (1.05 V/kPa) and static capacitance (5.56 pF/kPa) over a wide sensing range of 1–73 kPa. This dual-mode sensor effectively overcomes the transient limitation of conventional single-mode TENGs and shows significant potential for future smart tactile applications. Full article

Disk triboelectric nanogenerator (TENG) design pursues high structural figure of merit (${\mathrm{FOM}}_S$), yet nominal peak designs often sit in regions with steep geometric gradients; under a controlled $\pm 10\%$ symmetric perturbation proxy, worst-case ${\mathrm{FOM}}_S$ retention near the peak frontier falls to 2.7%. We decompose ${\mathrm{FOM}}_S$ into a charge-transfer channel ($Q_{\mathrm{sc},\mathrm{MACRS}}$) and a capacitance channel ($C_{\mathrm{sum}}^{-1}$), and train a multi-output surrogate with a physics consistency constraint on 1944 COMSOL simulations to jointly predict $Q_{\mathrm{sc},\mathrm{MACRS}}$, $C_{\mathrm{sum}}^{-1}$, and ${\mathrm{FOM}}_S$ across electrode-pair number, dielectric-thickness-to-radius ratio ($h/R$), air-gap-to-radius ratio ($d/R$), and dielectric constant. Evaluating 7776 design points reveals that 58.6% of the explored space is charge-dominant, 36.1% mixed, and 5.3% capacitance-dominant; raising dielectric constant shifts the mechanism toward capacitance-limited behavior, while a larger air gap reinforces charge-limited behavior. Mixed-regime windows tolerate the same perturbation proxy far better than peak-${\mathrm{FOM}}_S$ candidates, supplying candidate design windows for pre-fabrication screening within the validated simulation domain. The surrogate reaches pooled out-of-distribution ${\mathrm{FOM}}_S$$R_{{\log }_{10}}^2=0.914$ on 43 unseen structural and dielectric combinations. Delivered through an open-source Streamlit interface, the channel decomposition, mechanism mapping, and tolerance screening let designers identify the limiting mechanism and select candidate designs that are expected to tolerate geometric variation within the validated simulation domain, prior to fabrication. Full article

The triboelectric nanogenerator (TENG), which converts mechanical energy into electrical energy through the coupled effect of triboelectrification and electrostatic induction, has garnered significant interest among researchers due to its portability and self-powered characteristics. Despite its evident development potential, TENG continues to face challenges, including the necessity to enhance its triboelectric performance through the optimization of structures, materials, and manufacturing techniques to improve energy conversion efficiency. Additionally, its environmental stability and durability also need to be improved. TENGs designed inspired by non-human animals and plants offer feasible solutions to address these limitations. These bio-inspired TENGs optimize the structural design of TENGs and the materials of the triboelectric layers by imitating the structures, functions, and behaviors of organisms, thereby further improving the energy conversion efficiency, sensitivity, wear resistance, adaptability to special environments, biocompatibility, and wearing comfort of TENGs. This paper expounds on the progress of TENGs inspired by non-human animals and plants applied in environmental energy harvesting, human health and motion monitoring. It also discusses the current challenges, with a view to providing insights for the interdisciplinary integration and development of bionics and TENGs. Full article

Real-time monitoring of marine ecosystems is crucial for global climate change research. In extreme marine environments such as the westerly regions in the Arctic and Antarctic, monitoring buoys and platforms often suffer from severe challenges, including insufficient energy supply, limited battery life, and difficult maintenance. Triboelectric nanogenerators (TENGs) offer a promising strategy for self-powered marine sensing. However, conventional tubular liquid–solid triboelectric nanogenerators (LS-TENGs) suffer from low efficiency of interfacial charge transfer due to limited contact area and excessive internal resistance, which restricts their output. In this study, a multi-tube nested liquid–solid triboelectric nanogenerator (MLS-TENG) is proposed, and the suitable filling ratio is determined through comparative experiments on structural parameters. This design significantly increases the effective contact area, reduces internal resistance, and improves synergistic charge transfer at multiple interfaces. Experimental results demonstrate that the MLS-TENG exhibits substantially improved electrical output compared with the corresponding single-tube structures. When integrated with a power management module, the capacitor charging efficiency is improved by approximately 120 times. In real sea trials, an array composed of MLS-TENG units successfully drives a self-powered sensing system, achieving stable 4G transmission of environmental parameters. This work provides a scalable structural optimization strategy for constructing high-performance blue energy-harvesting self-powered nodes for the marine Internet of Things. Full article

Rotor imbalance and abnormal vibration are classical operating conditions in rotating machinery and can often be identified by conventional vibration analysis. However, the development of low-power, self-powered, and distributed sensing nodes remains important for long-term condition monitoring, particularly in scenarios where external power supply, wiring, and maintenance are constrained. Existing vibration sensors, including piezoelectric and capacitive types, are constrained by power consumption and degraded performance under low-frequency and weak excitation. To address this issue, a magnetically repulsive cushion triboelectric nanogenerator (MRCT) is proposed to enable self-powered vibration sensing. The magnetic-repulsion cushion allows the upper friction layer to undergo stable contact–separation motion under a non-contact restoring force, while the microstructured strip electrode array (MSEA) enhances the triboelectric output and signal stability. A hybrid convolutional neural network–gated recurrent unit (CNN-GRU) deep-learning model is employed to extract time-domain and frequency-domain features from the collected signals, enabling real-time identification of rotor vibration amplitude, frequency, and imbalance weight. Experimental results show that the MRCT provides stable output, a high signal-to-noise ratio, and an identification accuracy above 98% for predefined rotor imbalance-weight states under laboratory conditions. In addition, a shaft-misalignment-related abnormal vibration condition was examined on the motor platform. The corresponding time-domain and frequency-domain analyses show that the MRCT voltage signal exhibits distinguishable signal variations under normal and misalignment-related conditions, including spectral changes around the 2× rotational frequency. A laboratory-scale AIoT-oriented demonstration further verifies the feasibility of integrating MRCT signal acquisition, CNN-GRU inference, wireless transmission, and GUI-based visualization. It should be noted that the present work mainly focuses on imbalance-state recognition, while the misalignment-related experiment provides an additional sensor-response verification. Broader validation involving mechanical looseness, bearing defects, variable-speed operation, cross-machine testing, and long-term industrial conditions remains necessary. Full article

This comprehensive review aims to cover the surface tribology and triboelectric properties of additively manufactured (AM) natural biopolymers, including cellulose, chitosan (CS) and silk fibroin (SF), in biomedical interface engineering. While these sustainable materials exhibit innate biocompatibility and tribopositivity, their baseline triboelectric performance demands targeted surface engineering. We synthesize key physical mechanisms governing charge generation, emphasizing how controlled surface roughness, hierarchical porosity and nanoscale architectures maximize contact electrification. Furthermore, distinct dielectric and polarity modulation strategies are evaluated across the biopolymer families: cellulose relies heavily on chemical functionalization to overcome weak native polarity; chitosan utilizes ionic coordination and fillers to elevate its relatively low charge density; and silk fibroin achieves exceptional power outputs via highly porous three-dimensional nanocomposite aerogels. AM technologies afford unprecedented spatial control over these biointerfaces but introduce severe processing constraints. Techniques such as those based on extrusion impose strict shear-thinning rheology and rapid crosslinking for cellulose and chitosan, while SF frequently suffers from crystallization-induced nozzle clogging, necessitating photocurable derivatives. Full article

Polyvinyl alcohol (PVA)-based hydrogels suffer from insufficient mechanical strength, while aramid nanofibers (ANF) have intrinsic insulation that limits their sensing applications, and the synergistic effect of composite fillers remains underexplored. This study aims to develop a multifunctional PVA/ANF/NaCl composite organohydrogel for high-performance flexible sensors. The gel was fabricated via freeze–thaw crosslinking, solvent exchange and NaCl impregnation, with systematic investigations of its microstructure, mechanical, electrical and multifunctional sensing properties, and a corresponding triboelectric nanogenerator (TENG) and self-powered handwriting recognition system were constructed. Results show that 2% ANF significantly enhances the gel’s mechanical performance, 0.5 M NaCl achieves optimal mechanical-electrical balance, the gel-based sensor exhibits excellent distance, pressure and strain sensing with high cyclic stability, the TENG delivers stable electrical output, and the recognition system achieves 95% accuracy on the test set. This work provides a new material and design strategy for advanced flexible electronic devices. Full article

(1) Background: Gait analysis technologies have advanced; however, traditional systems like optical motion capture are lab-bound and costly, limiting rehabilitation monitoring. This cross-sectional study evaluates self-powered triboelectric nanogenerator (TENG) insoles combined with IMU sensors to assess gait asymmetry, plantar pressure signatures, age effects and injury history in rehabilitation patients, aiming to enable portable, battery-free phenotyping. (2) Methods: Fifty-three patients (22 females, 31 males; age, 29 ± 26 years) from Astana clinics with trauma histories (e.g., spine, ankle, fractures) and 10 healthy references underwent a 2 min walk test (2MWT). TENG insoles captured plantar loading; ankle/knee IMUs measured spatiotemporal parameters (cadence, asymmetry). The data were normalized; the analyses used an ANOVA and correlations (Python 3.14.3). (3) Results: The TENG sensors showed force/frequency linearity (up to 10 V at 20 N). The cadence averaged 101 ± 10 steps/min, declining with age (r = −0.31, p = 0.03) and fractures (r = −0.23, p = 0.04). The asymmetry varied (−54% to +31%) without category differences. Flatfoot (55%) was linked to lateral loading shifts; condition-specific waveform signatures emerged (e.g., lateral heel in ankle issues). (4) TENG-IMU systems feasibly capture gait phenotypes in heterogeneous cohorts, supporting out-of-lab monitoring for personalized rehabilitation without batteries. Prospective validation is required for further practical implications. Full article

To address dependence on external power and the limited capability of conventional hydroelectric units to detect low-amplitude vibrations, this work introduces a self-contained, highly accurate monitoring device. The design incorporates a magnetically levitated configuration, with triboelectric films placed on both the upper and lower faces of the floating magnet. Under minor oscillations, magnetic repulsion increases the relative displacement between the friction layers, producing a substantial voltage that permits low-level vibration sensing. A surrounding induction coil responds to the levitated pole’s vertical motion; this motion intersects the magnetic flux, generating a current that provides stable energy for wireless data transmission. Experimental outcomes confirm a detection limit of 0.1 mm. At an amplitude of 1 mm and a load of 1000 Ω, the system achieves a maximum output of 9 mW and a power density of 1.587 W/m², ensuring reliable power. This configuration provides a new pathway for monitoring vibrations in hydroelectric turbine generators. Full article

Against the backdrop of global energy structure decarbonization, distributed transformation, and the rapid development of low-power electronic devices and sensor networks, micro-energy supply and intelligent sensing have emerged as critical bottlenecks limiting their large-scale application. Triboelectric nanogenerators (TENGs), leveraging advantages such as compatibility with diverse materials and adaptability to flexible and miniaturized fabrication, can efficiently harvest widely available low-frequency, low-amplitude distributed mechanical energy in the environment. Additionally, they exhibit self-powered sensing characteristics, where output signals are directly correlated with external physical quantities, demonstrating unique strengths in the fields of micro-/nano-energy and intelligent monitoring. This article systematically reviews the research progress in TENGs; elucidates their working modes and power generation principles; summarizes material design, structural optimization, and performance enhancement strategies for efficient energy harvesting; and outlines the current state of self-powered sensing technologies. It highlights their engineering applications in intelligent monitoring scenarios such as drones, marine environments, infrastructure, and wearable devices. Addressing the existing technical bottlenecks and theoretical challenges in integrated energy harvesting–sensing–monitoring systems, the paper envisions future trends toward high performance, integration, and intelligence, providing valuable insights for fundamental research on and engineering applications of TENGs in micro-energy supply and intelligent monitoring. Full article