Search Results (597)

The Triboelectric Nanogenerator (TENG), as an emerging self-powered sensor, is widely used in the field of rotating machinery bearing fault diagnosis. Due to its working principle based on frictional electrification and electrostatic induction effects, the surface morphology and charge transfer efficiency of the friction layer have a significant impact on the output performance of TENGs. Under long-term mechanical motion, the friction layer may experience wear, and continuous wear can lead to surface morphology damage and even damage to the friction layer structure, gradually destroying the TENG’s signal acquisition and output capabilities, causing signal degradation and bearing fault feature deviation, which results in a decrease in bearing fault diagnosis accuracy. Traditional solutions focus on material properties and structure. This article derives the mechanism of the influence of friction layer thickness on the output signal through the TENG output voltage formula and simulates different degrees of wear with friction layers of different thicknesses to conduct deep learning-based bearing fault diagnosis experiments. The experimental results show that although the CNN model can recognize TENG signals well for bearing fault classification, the bearing fault features of the worn signals shift, and the accuracy of CNN diagnosis decreases. The introduction of a one-dimensional self-attention-enhanced convolutional neural network model and an incremental learning method improved the accuracy of bearing fault diagnosis after wear and tear. This study provides theoretical support and practical solutions for long-term, stable bearing fault diagnosis in TENG under wear conditions. Full article

Contact electrification (CE), also referred to as triboelectrification, describes electron transfer occurring at the interface of dissimilar materials. Its microscopic mechanism remains unclarified due to the complex coupling of multiple physical fields, yet the rapid development of triboelectric nanogenerators (TENGs) has rendered CE a prominent research hotspot in tribology on account of its promising application prospects. Metal/polymer combinations have been widely employed for CE research due to their significant differences in electron gain and loss. Nevertheless, most existing studies focus solely on saturated polymers, and systematic comparative analyses between saturated and unsaturated molecular structures are rarely reported. Accordingly, the intrinsic microscopic origin of enhanced interfacial electrification performance induced by unsaturated groups has not been fully understood. In this work, first-principles calculations based on density functional theory (DFT) are implemented to establish interfacial models consisting of an Al substrate and three types of PTFE single chains: fully saturated-PTFE, PTFE with unsaturated bonds at the chain terminus, and PTFE with unsaturated bonds in the middle of the chain. The inherent mechanism governing the modulation of CE behaviors by unsaturated structures are comprehensively revealed from multiple perspectives, including charge transfer, electrostatic potential, and frontier orbital distribution. Computational results demonstrate that unsaturated groups drastically elevate local electrostatic potential and strengthen the electron-trapping capability of molecular chains, thereby substantially boosting CE performance. Moreover, this modulation effect exhibits remarkable position dependence, where unsaturated structures located in the middle of molecular chains deliver better performance improvement than terminal unsaturated moieties. The electron-donating and electron-accepting properties of materials are dominated by the energy level characteristics of the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO), respectively. This study elucidates the microscopic mechanism of CE at unsaturated polymer/metal interfaces at the molecular scale, and provides theoretical support for optimizing the output performance of TENGs through surface modification strategies. Full article

Tear analysis plays a crucial role in the early screening and diagnosis of ophthalmic diseases. However, conventional methods are often limited by poor real-time performance, low portability, and insufficient capability for multi-parameter detection. Here, we present a bioinspired triboelectric electronic tongue integrated with a microfluidic chip for multimodal detection of tear pH and disease-related biomarkers. The system combines three triboelectric nanogenerator (TENG) modes, including droplet-based, dual-electrode sliding, and single-electrode sliding configurations. The droplet-based TENG converts gravitational potential energy into electrical energy, generating a maximum output voltage of 65 V. The sliding TENG further expands the sensing dimensions by characterizing droplet flow behavior and viscosity-related properties. Benefiting from the high sensitivity of the dual-electrode mode and the waveform differentiation capability of the single-electrode mode, the platform enables enhanced sample discrimination. After optimizing key parameters, including droplet height and chip inclination angle, the output stability errors for all three TENG modes were maintained within ±10%. Combined with a random forest algorithm, the multimodal sensing system achieved a classification accuracy exceeding 96.6% for artificial tears with different pH values. Moreover, distinct electrical response patterns were observed for ophthalmic disease-related biomarkers, including Lysozyme, Interleukin-6 (IL-6), and Chlamydia, demonstrating excellent type identification and concentration detection capability. This work provides a self-powered and miniaturized strategy for intelligent tear analysis and multiple-parameter sensing, offering significant potential for ophthalmic disease diagnosis. Full article

This study explores the energy-harvesting performance of aluminum (Al)- and iron (Fe)-filled styrene–butadiene rubber (SBR) composites, with a focus on their mechanical durability and triboelectric properties. Comprehensive mechanical characterization—including tensile strength, elongation at break, fracture toughness, and elasticity—reveals that Fe-filled composites exhibit significantly enhanced reinforcement compared to Al-filled systems at equivalent filler loadings. Raman spectroscopy indicates that Fe atoms can coordinate with the benzene rings of SBR chains through stronger physicochemical bonding, a feature less present in Al-based composites. In addition to improved mechanical properties, Fe-filled composites demonstrate higher electrical conductivity and superior triboelectric energy-harvesting performance. Notably, the composite containing 15 vol% Fe under 1% cyclic compressive strain achieves a peak current density of 127.05 µA/m², a total generated charge of 5.01 nC, and a peak power density of 48.22 µW/m². These values represent substantial enhancements of 246%, 236%, and 2398%, respectively, compared to Al-filled counterparts. Cyclic energy-harvesting tests confirm stable performance with negligible degradation in output or mechanical integrity over repeated cycles. Rubber composite shows good humidity resistance in current and voltage outputs. Furthermore, a layer-by-layer triboelectric nanogenerator (TENG) based on the Fe-filled composite produces output signals of approximately ±1.0 µA and ±5 V under biomechanical hand patting. The superior performance of Fe-based composites is attributed to stronger filler–rubber interactions, likely facilitated by electrostatic interactions, which enhances interfacial charge transfer during mechanical deformation. Overall, Fe-filled SBR composites demonstrate strong potential for cost-effective, environmentally friendly, and durable self-powered energy-harvesting applications. Full article

Achieving sustainable structural health monitoring remains a critical challenge for intelligent railway infrastructures, where distributed sensing networks require continuous power supply and long-term maintenance. Although low-frequency railway bridge vibrations simultaneously contain harvestable mechanical energy and structural state information, existing systems generally exploit these functionalities independently, resulting in increased system complexity and limited energy utilization efficiency. Here, we present an integrated triboelectric vibration energy harvesting and displacement monitoring device (THM) for low-frequency railway bridge vibrations. By incorporating a quasi-zero-stiffness (QZS) mechanism, the energy harvesting unit achieves an enhanced low-frequency response, delivering an open-circuit voltage of 280 V, a short-circuit current of 28 μA, and a peak power of 9 mW. The device charges a 22 μF capacitor to 4 V within 45 s under 1.5 Hz excitation, demonstrating its capability to power low-power electronics. Simultaneously, a freestanding triboelectric sensing unit enables real-time girder–pier displacement monitoring, displacement-direction identification, and structural safety warning, exhibiting excellent linearity (R² = 0.9886) and stable operation over 20,000 cycles. This work provides an integrated strategy for simultaneously harvesting energy and monitoring structural displacement from low-frequency railway bridge vibrations, offering a promising route toward self-sustained intelligent bridge health monitoring systems. Full article

Accurate ocean wave sensing in polar and other low-temperature marine environments is of great significance for marine environmental observation, climate research, and navigation safety. However, conventional wave sensors rely on external power supplies and suffer from poor stability under low-temperature and high-salinity conditions, making long-term self-powered waves sensing a significant challenge. Herein, a highly stable composite eutectogel electrode is developed by integrating sodium lignosulfonate, Fe³⁺ crosslinking, Zn²⁺-carboxylate coordination interactions, and a choline chloride/urea deep eutectic solvent (DES). The DES effectively suppresses solvent crystallization and endows the gel with excellent low-temperature tolerance, while the synergistic effect of metal coordination and multiple non-covalent interactions constructs a robust ion-conducting network with enhanced structural stability. Furthermore, eutectogel-based composite electrode architecture is designed to improve electrical conductivity and charge collection efficiency, thereby enabling stable electrical output under harsh marine conditions. Based on the as-prepared eutectogel electrode, a self-powered solid–liquid triboelectric nanogenerator is fabricated for ocean wave-motion sensing. The device can detect the wave amplitude, with an accuracy of 0.2 cm, and sense the frequency of waves ranging from 0.2 Hz to 1.6 Hz. More importantly, the SL-TENG exhibits excellent environmental adaptability, operating reliably in 3.5 wt% simulated seawater and at 0 °C. The current retention ratio reaches approximately 91% at 0 °C, which is significantly higher than that of the hydrogel-based device (≈6%). The remarkably low-temperature and salt-tolerant performance originates from the stable ion-transport network and anti-freezing characteristics of the eutectogel electrode. This work provides an effective strategy for constructing environmentally resilient eutectogel-based triboelectric devices and offers a promising route toward self-powered wave sensing systems for long-term deployment in harsh marine environments. Full article

Lead-ion (Pb²⁺) contamination in drinking water poses a serious threat to public health, but conventional laboratory-based methods rely on bulky equipment and are unsuitable for on-site monitoring. Here, we develop a wireless monitoring system based on a dual-channel liquid–solid triboelectric nanogenerator probe (TENG probe) for detecting Pb²⁺ in drinking water. Based on the dynamic contact electrification at the liquid–solid interface, the sliding of a water droplet containing Pb²⁺ on the FEP surface is converted into an electrical signal for Pb²⁺ detection. A wireless acquisition circuit transmits the electrical signals via Wi-Fi to a computer, enabling remote and wireless detection. By integrating a one-dimensional convolutional neural network (1D CNN) deep learning model, the TENG probe achieved a detection accuracy of 99.62% and was capable of detecting Pb²⁺ in drinking water at the ppb level, exceeding the national standard. This work opens a way for safeguarding drinking-water quality. Full article

This study investigates flexoelectricity in the pyramid- and truncated-pyramid-patterned contact interface of a NOA-63/Ecoflex (N/E) triboelectric nanogenerator (TENG) operating in contact–separation mode. Microscale pyramidal and truncated pyramidal arrays were fabricated using silicon molds and paired with an Ecoflex™ 00-10 elastomer substrate, and the structural integrity of the arrays was confirmed by scanning electron microscopy. Combining experimental results with established knowledge of soft-polymer indentation mechanics and hard-to-hard flexoelectric behavior, the surface charge density (σ) and flexoelectric coefficient (μ_flexo) were determined to be 8.48 × 10⁻⁶ C/m² and 2.95 × 10⁻¹¹ C/m, respectively. These parameters were incorporated into a total charge equation to estimate charge output for both pyramidal and truncated pyramidal N/E TENG arrays under varying applied loads. The proposed model can adequately predict the charge output of pyramidal and truncated pyramidal N/E TENGs. Full article

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