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Triboelectrification is a well-known phenomenon that commonly occurs in nature and in our lives at any time and any place. Although each and every material exhibits triboelectrification, its quantification has not been standardized. A triboelectric series has been qualitatively ranked with regards to triboelectric polarization. Here, we introduce a universal standard method to quantify the triboelectric series for a wide range of polymers, establishing quantitative triboelectrification as a fundamental materials property. By measuring the tested materials with a liquid metal in an environment under well-defined conditions, the proposed method standardizes the experimental set up for uniformly quantifying the surface triboelectrification of general materials. The normalized triboelectric charge density is derived to reveal the intrinsic character of polymers for gaining or losing electrons. This quantitative triboelectric series may serve as a textbook standard for implementing the application of triboelectrification for energy harvesting and self-powered sensing. Triboelectric charging is a well-known phenomenon, but triboelectric polarization has only been ranked qualitatively. Here the authors develop a quantified triboelectric series for a wide range of polymers by measuring triboelectric charge density with respect to a liquid metal at well-defined conditions.

Contact-electrification is a universal effect for all existing materials, but it still lacks a quantitative materials database to systematically understand its scientific mechanisms. Using an established measurement method, this study quantifies the triboelectric charge densities of nearly 30 inorganic nonmetallic materials. From the matrix of their triboelectric charge densities and band structures, it is found that the triboelectric output is strongly related to the work functions of the materials. Our study verifies that contact-electrification is an electronic quantum transition effect under ambient conditions. The basic driving force for contact-electrification is that electrons seek to fill the lowest available states once two materials are forced to reach atomically close distance so that electron transitions are possible through strongly overlapping electron wave functions. We hope that the quantified series could serve as a textbook standard and a fundamental database for scientific research, practical manufacturing, and engineering. The mechanism of contact electrification remains a topic of debate. Here, the authors present a quantitative database of the triboelectric charge density and band structure of many inorganic materials, verifying that contact electrification between solids is an electron quantum transition effect.

A large coercive field ( E C ) and ultrahigh piezoelectricity are essential for ferroelectrics used in high-drive electromechanical applications. The discovery of relaxor-PbTiO 3 crystals is a recent breakthrough; they currently afford the highest piezoelectricity, but usually with a low E C . Such performance deterioration occurs because high piezoelectricity is interlinked with an easy polarization rotation, subsequently favoring a dipole switch under small fields. Therefore, the search for ferroelectrics with both a large E C and ultrahigh piezoelectricity has become an imminent challenge. Herein, ternary Pb(Sc 1/2 Nb 1/2 )O 3 –Pb(Mg 1/3 Nb 2/3 )O 3 –PbTiO 3 crystals are reported, wherein the dispersed local heterogeneity comprises abundant tetragonal phases, affording a E C of 8.2 kV/cm (greater than that of Pb(Mg 1/3 Nb 2/3 )O 3 –PbTiO 3 by a factor of three) and ultrahigh piezoelectricity ( d 33 = 2630 pC/N; d 15 = 490 pC/N). The observed E C enhancement is the largest reported for ultrahigh-piezoelectric materials, providing a simple, practical, and universal route for improving functionalities in ferroelectrics with an atomic-level understanding. High-drive electromechanical applications require ferroelectrics accounting for a large coercive field and high piezoelectricity simultaneously but it is still a challenge. Here, the authors demonstrate it in a relaxor-based ferroelectric crystal.

Soil texture is a key determinant shaping bacterial communities in vineyard ecosystems, yet how different soil textures modulate bacterial characteristics in rhizosphere versus bulk soils during grapevine growth remains poorly understood. This study collected rhizosphere and bulk soil samples from five commercial Vitis vinifera cv. Cabernet Sauvignon vineyards in the eastern piedmont of Helan Mountain, Ningxia, China, spanning three distinct textures (gravelly, calcareous, and aeolian sandy soils). Shotgun metagenomic sequencing, soil physicochemical analysis, and four soil enzyme activity (alkaline phosphatase, urease, catalase, and invertase) measurements were conducted, using PERMANOVA and RDA to identify dominant driving factors. The results showed that bacteria accounted for 97.6% of all annotated sequences, representing the dominant group in soil microbial communities. Significant differences in bacterial abundance and alpha diversity (Chao1, ACE, Shannon, and Simpson) were observed in bulk soils across textures, whereas rhizosphere soils showed significant abundance differences but similar diversity levels. However, the 50 cm bulk soil sampling distance may have attenuated the true rhizosphere effect, and these findings should be interpreted with this methodological constraint in mind. Notably, bacterial community structure differed significantly between soils of the same pedogenic type but different textures, confirming that soil texture, rather than pedogenic classification, is the primary driver. Thirteen dominant bacterial phyla (>1% relative abundance) were identified, with Proteobacteria (47.7%), Actinobacteriota (22.9%), and Acidobacteriota (6.5%) as the main taxa. Mantel tests revealed significant correlations between nitrogen, phosphorus, organic matter contents and enzyme activities in rhizosphere soils (r ≥ 0.4, p < 0.01). RDA indicated that total phosphorus (TP), organic matter (OM), alkali-hydrolyzable nitrogen (AN), Mg, pH, available K (AK), and enzyme activities were key drivers of bacterial community structure (p < 0.05). Annotated metabolic functions based on KEGG orthology indicated lower overall metabolic pathway abundances in gravelly soils compared to calcareous and aeolian sandy soils. In conclusion, soil texture, rather than broad pedogenic classification, primarily shapes vineyard bacterial communities, providing a theoretical basis for precision viticulture and sustainable soil management.

The Electro-Hydraulic Servo Actuator for Active Suspensions (ASEHSA) plays a decisive role in shaping the holistic performance of vehicle suspension systems through its dynamic response speed and control precision. However, achieving high-performance control of ASEHSA still faces challenges. On one hand, existing model-based control methods are highly sensitive to parameter uncertainties and unmodeled nonlinear hydraulic dynamics, which can easily lead to reduced robustness in practical applications. On the other hand, traditional model-free strategies have limited time-delay compensation capabilities and often struggle to balance overshoot and settling time under delayed and disturbed conditions. To resolve this challenge, this study proposes an improved model-free adaptive control method that incorporates the differentiation of the tracking error (DE-IMFAC). Within the framework of traditional model-free adaptive control (MFAC), this approach reconfigures the time-delay term from an explicit form in the control law to implicit management, substantially mitigating the influence of time delays on system control performance. At the same time, by refining the performance criterion function and integrating a tracking error differentiation term together with dynamic weighting factors, the dynamic performance and adjustment flexibility of the controller are significantly enhanced. Additionally, by leveraging the characteristic equation of discrete autonomous systems and compression mapping theory, the BIBO stability of the DE-IMFAC control system and the monotonic convergence of the tracking error are rigorously established through theoretical analysis. Simulation and experimental results demonstrate that, compared with PID and traditional MFAC methods, DE-IMFAC significantly reduces integral absolute error, overshoot, settling time, and maximum position tracking error, while improving disturbance rejection capability. This approach does not depend on an accurate mathematical model of the ASEHSA system and maintains robust dynamic performance under complex operating environments characterized by time delays and data disturbances, providing a practical solution for ASEHSA and related industrial control systems.

To address performance degradation and control instability in electro-hydraulic servo active suspension systems due to internal leakage faults arising from wear and aging of hydraulic components, this paper proposes an innovative fuzzy PID fault-tolerant controller based on the Improved Giant Armadillo Optimization (IGAO) algorithm. Specifically, to overcome the limitations of the standard Giant Armadillo Optimization (GAO), which is prone to local optima and exhibits poor convergence performance when handling multi-constraint parameter optimization problems, this study introduces a nonlinear dynamic inertia weight mechanism and a random reflection strategy for out-of-bounds particles to improve the original algorithm’s performance. These enhancements significantly enhance its ability to balance global exploration and local exploitation. Furthermore, this research develops a comprehensive performance evaluation fitness function by quantifying key performance indicators such as body acceleration, suspension dynamic deflection, and tire dynamic load. A quarter-car model incorporating an internal leakage fault was established as a simulation validation platform to demonstrate the reliability of the proposed method. Simulation results indicate that under various road excitation conditions, the proposed IGAO algorithm can rapidly and stably converge to superior parameters for the fuzzy PID controller. Compared to the Particle Swarm Optimization (PSO) and standard GAO algorithm, the control system optimized by IGAO not only significantly more effectively suppresses body vibration and reduces shock amplitude but also exhibits stronger dynamic recovery performance and control robustness under varying degrees of internal leakage faults. This research provides a robust control approach for addressing internal parameter uncertainties in hydraulic systems and offers a new approach to theoretical modeling for enhancing the reliability of design and fault-tolerant control capabilities of active suspension systems.

System time delays and nonlinear unmodeled dynamics severely constrain the control performance of the Active Suspension Electro-Hydraulic Servo Actuator (ASEHSA). To tackle these challenges, this paper presents a Dynamic Error Differentiation-based Model-Free Adaptive Control (DE-MFAC) strategy integrated with an Improved Particle Swarm Optimization (IPSO) algorithm. Established under the Model-Free Adaptive Control (MFAC) framework, the DE-MFAC integrates a dynamic error differentiation mechanism and an implicit expression of time delays, thus removing the dependence on a precise system model. The traditional PSO algorithm is improved by incorporating an inertia weight adjustment strategy and a boundary reflection wall strategy, which effectively mitigates the issues of local optima and boundary stagnation. In AMESim 2021, a 1/4 vehicle active suspension electro-hydraulic actuation system model is constructed. To ensure an impartial evaluation of controller performance, the IPSO algorithm is employed to optimize the parameters of the PID, MFAC, and DE-MFAC controllers, respectively. Co-simulations with Simulink 2023b are conducted under two time delay scenarios using a composite square-sine wave signal as the reference. The results indicate that all three IPSO-optimized controllers realize effective position tracking. Among them, the DE-MFAC controller exhibits the optimal performance, demonstrating remarkable advantages in reducing tracking errors and balancing settling time with overshoot. These findings verify the effectiveness of the proposed control strategy, time delay compensation mechanism, and optimization algorithm. Future research will involve validation on a physical ASEHSA platform, further exploration of the method’s applicability and robustness under diverse operating conditions, and extension to other industrial systems with similar nonlinear time delay features.

Wearable triboelectric nanogenerators (TENGs) have recently attracted great interest because they can convert human biomechanical energy into sustainable electricity. However, there is a need for improvement regarding the output performance and the complex fabrication of TENG devices. Here, a triboelectric nanogenerator in single-electrode mode is fabricated by a simple strategy, which involves a sandwich structure of silicone rubber and silver-coated glass microspheres (S-TENG). The S-TENG exhibits a remarkable performance in harvesting human motion energy and as flexible tactile sensor. By optimizing the device parameters and operating conditions, the maximum open-circuit voltage and short-circuit current of the S-TENG can reach up to 370 V and 9.5 μA, respectively. The S-TENG with good stretchability (300%) can be produced in different shapes and placed on various parts of the body to harvest mechanical energy for charging capacitors and powering LED lights or scientific calculators. In addition, the good robustness of the S-TENG satisfies the needs of reliability for flexible tactile sensors in realizing human–machine interfaces. This work expands the potential application of S-TENGs from wearable electronics and smart sensing systems to real-time robotics control and virtual reality/augmented reality interactions.

The dynamic response accuracy of suspension servo actuators directly determines the vibration-reduction performance of active-suspension systems. However, during long-term service, the system is prone to the influence of compound-degradation faults, such as internal leakage and time delay, leading to a significant decline in control performance. To address this issue, this paper proposes a collaborative control framework combining model-free adaptive control with a differential term of tracking error (DE-MFAC) and an improved particle swarm optimization (IPSO) algorithm. Firstly, to overcome the limitations of traditional model-free adaptive control (MFAC), a DE-MFAC strategy is constructed by implicitly handling the time-delay term and introducing the differential term of tracking error and dynamic weight factor into the performance index. Secondly, to enhance the parameter-tuning effect, the traditional particle swarm optimization (PSO) algorithm is improved (IPSO) by incorporating a dynamic inertia weight and an out-of-bounds random reflection mechanism, thereby strengthening the global optimization capability. On this basis, a suspension servo actuator system model incorporating internal leakage and time-delay faults is established based on the co-simulation platform of Simulink and AMESim, and the proposed method is validated. The simulation results show that, compared with the optimized traditional MFAC, the DE-MFAC tuned by IPSO exhibits superior position-tracking accuracy, faster response speed, and stronger overshoot-suppression capability under various compound-fault conditions. Further analysis indicates that the Integral of Absolute Cubic Error (IACE) function, due to its higher sensitivity to large deviations, can more effectively suppress overshoot and is suitable for engineering scenarios with strict requirements on dynamic performance. In addition, the optimization of control parameters using the IPSO algorithm can effectively compensate for the performance degradation caused by degradation faults, providing a feasible technical approach for extending the service life of actuators through adaptive adjustment.

This paper proposes an optimized control strategy for fuzzy PID control based on an improved particle swarm optimization (IPSO) algorithm, targeting electro-hydraulic servo active suspension systems with internal leakage faults. Using AMEsim and Simulink software, a 1/4 vehicle electro-hydraulic servo active suspension internal leakage model is established. To verify the effectiveness of the proposed control strategy, the paper uses semi-sinusoidal bump road and class C road as model inputs, calculating and comparing the sprung mass acceleration (SMA), dynamic tire load (DTL), and suspension working space (SWS) for both the active suspension system under different internal leakage clearances and the passive suspension system. The results show that the proposed control strategy effectively optimizes the performance indicators of the active suspension system under different internal leakage clearances, thereby enhancing the vehicle’s riding smoothness, road adhesion, and operational safety. In contrast to the conventional particle swarm optimization PID control (PSO-PID), the IPSO-fuzzy PID controller demonstrates superior performance even in the presence of larger annular clearances, ensuring the robustness and dependability of the active suspension system. This research provides important theoretical support for the optimized control and hydraulic actuators structural design of electro-hydraulic servo active suspension systems.