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In piezoelectric energy harvesters (PEHs) with external magnetic coupling, one main challenge is to obtain a precise magnetic force model to calculate the impacts of the external magnetic force on the vibrational response and energy harvesting performance. A tri-stable piezoelectric energy harvester (TPEH) with two external magnets was considered in this paper. An improved magnetic force model based on the magnetic dipoles theory was originally derived to investigate the formation mechanisms for bi- or tri-stability states at first, and then, a distributed-parameter mathematical model based on the energy method was established by considering the derived nonlinear magnetic force, and was used to investigate the nonlinear dynamic behaviors and power generation performance. Bifurcation analyses were also performed for the equilibrium solution of the derived system model. Experiments were subsequently conducted to validate the theoretical analysis. Simulation and experimental results indicate that the improved model for magnetic force is more applicable compared with the magnetic dipoles model used before. Results also show that the TPEH can significantly enhance the energy harvesting performance compared with the conventional bi-stable piezoelectric energy harvester in a wide frequency range.

A novel structure with enhanced energy absorption is proposed by introducing thin-walled tube as the inner liner tube of the cylindrical structures with negative Poisson’s ratio (C-NPR). The energy absorption performances of C-NPR structure with inner tube (C-NPR-IT) are compared to other configurations like the single NPR structure, single thin-walled tube, and C-NPR structure with outer tube (C-NPR-OT) to show its superiority. It is found that the interaction between the NPR structure and inner tube in C-NPR-IT can be enhanced. Then, the parametric analysis of the geometric parameters on the crashworthiness performance of C-NPR-IT structures are performed with finite element method. To achieve the best configuration, the surrogate modeling technique and the multi-objective particle swarm optimization (MOPSO) algorithm are employed to optimize the C-NPR-IT structures. The results show that the optimized structure improves the specific energy absorption ( SEA ) from 3.97 to 10.26 kJ/kg by almost 2.5× by controlling peak crushing force ( PCF ) less than 80 kN. Therefore, the C-NPR-IT structure has an application prospective in the energy absorber.

Rotation-based energy harvesting has attracted considerable interest in recent years. This paper presents a comprehensive theoretical model to analyze a rotational impact energy harvester using the centrifugal softening effect. The harvester is composed of a centrifugal-softening driving beam that impacts two rigid piezoelectric beams to generate electrical energy through the gravity excitation. The theoretical model is derived based on Hamilton’s principle and Hertzian contact theory. An impact force model is used to overcome the limitation of the previous piecewise linear model, which cannot reflect the influence of the deformations of the driving and generating beams on the impact force and the energy output. Furthermore, an analytical impact force model is originally proposed for such a harvester based on Lee’s method to understand the impact mechanism. The proposed analytical model is validated through comparison with Runge–Kutta method. Both numerical and experimental results show that the centrifugal softening effect can amplify the relative motion between the driving and generating beams and increase the impact force, thus improving output power at low rotational frequencies. The maximum output power is increased by 135.5% at 11.5 Hz for the impact gap of 0.75 mm. In addition, with the large impact stiffness, the impact force can successfully prevent the inverted driving beam from continuously deflecting and suffering the static divergence. Based on the validated theoretical model, parametric studies are conducted to further investigate the effects of the impact stiffness and the centrifugal softening coefficient.

Enriched PD-L1 expression in cancer stem-like cells (CSCs) contributes to CSC immune evasion. However, the mechanisms underlying PD-L1 enrichment in CSCs remain unclear. Here, we demonstrate that epithelial–mesenchymal transition (EMT) enriches PD-L1 in CSCs by the EMT/β-catenin/STT3/PD-L1 signaling axis, in which EMT transcriptionally induces N-glycosyltransferase STT3 through β-catenin, and subsequent STT3-dependent PD-L1 N-glycosylation stabilizes and upregulates PD-L1. The axis is also utilized by the general cancer cell population, but it has much more profound effect on CSCs as EMT induces more STT3 in CSCs than in non-CSCs. We further identify a non-canonical mesenchymal–epithelial transition (MET) activity of etoposide, which suppresses the EMT/β-catenin/STT3/PD-L1 axis through TOP2B degradation-dependent nuclear β-catenin reduction, leading to PD-L1 downregulation of CSCs and non-CSCs and sensitization of cancer cells to anti-Tim-3 therapy. Together, our results link MET to PD-L1 stabilization through glycosylation regulation and reveal it as a potential strategy to enhance cancer immunotherapy efficacy. PD-L1 accumulates on cancer stem cells and favours immune evasion but the mechanism underlying this accumulation are unknown. Here the authors show that epithelial-mesenchymal transition induces glycosylation and stabilisation of PD-L1; antagonising this process renders cancer cells sensitive to anti-Tim3-therapy.

The fusion of 4D printing and magneto‐responsive shape memory polymers (SMPs) is unlocking new frontiers in remote actuation, reconfigurable materials, and multifunctional structures. This review provides a comprehensive analysis of the latest advancements in the fabrication, material selection, and application of these smart materials. The discussion encompasses the primary 3D printing techniques utilized for processing magneto‐responsive SMPs, including material extrusion, vat photopolymerization, and powder bed fusion. A critical comparison of fabrication methods highlights the influence of melt mixing and solvent casting on filler dispersion, mechanical performance, and actuation efficiency. Furthermore, various polymer matrices, such as thermoplastics and thermosets, are examined in conjunction with magnetic fillers, including Fe 3 O 4 , carbonyl iron powder (CIP), and neodymium magnet (NdFeB), to evaluate their effects on thermal, mechanical, and functional properties. The review also explores key application areas, such as biomedical engineering, soft robotics, and advanced wearable technology. Challenges related to material stability, actuation speed, and multi‐functional integration are discussed, along with emerging strategies to enhance performance and scalability. This work serves as a timely and in‐depth resource for researchers and engineers aiming to advance magnetic‐responsive materials in 4D printing toward sustainable soft robotic systems, biomedical devices, and flexible electronics.

Reconstructing premorbid gait patterns is critical for developing personalized rehabilitation strategies and assistive devices for patients with movement disorders. To achieve this aim, a predictive model is developed to estimate the walking dynamic features with individual parameters without requiring complex gait tests. First, an empirical kinematic model predicting the joint angle on the basis of leg length and walking cadence is derived. Consequently, dynamic models for the single support phase and double support phase are established, and a linear transformation strategy is proposed in the double support phase for optimization. Using inverse dynamic approaches, the model can ultimately predict the joint angle, joint moment, and ground reaction force across the entire gait cycle using only leg length, body mass, and walking cadence. The dynamic parameters predicted with the model are compared with experimental data for validation, and the results demonstrate the effectiveness of the proposed model.

In this study, a novel quad-stable energy harvester (QEH) is developed, in which its coordinates of equilibrium points can be user-defined like programming. This programmable feature distinguishes the proposed QEH from all reported magnet-type or buckling-type vibration energy harvesters. It has the advantage that it is easy to develop a high-performance QEH by appropriately programming these coordinate points and customizing a personalized QEH for different vibration environments. The dynamic model is established by the Ritz method and the Lagrange equation. The analytical steady periodic response is obtained by the average method. When the excitation acceleration is 2 m/s 2 , the peak power is 575 μW at 8.5 Hz. Also, the influence of the coordinate arrangement of the equilibrium points on the energy harvesting performance is studied. A formula that can quickly determine the equilibrium point coordinates is given, and the QEH designed according to this formula has superior performance. At last, the performance of the designed QEH is compared with other reported vibration energy harvesters. It shows that the QEH has a high average output power (287 μW), high normalized power density (59.8 μW/cm 3 /g 2 ), and wide operating frequency range (8.4 Hz) among these harvesters.

With the rapid development of low-power consumption wireless sensors and wearable electronics, harvesting energy from human motion to enable self-powered sensing is becoming desirable. Herein, a pair of smart insoles integrated with piezoelectric poly(vinylidene fluoride) (PVDF) nanogenerators (NGs) are fabricated to simultaneously harvest energy from human motion and monitor human gait signals. Multi-target magnetron sputtering technology is applied to form the aluminum electrode layers on the surface of the PVDF film and the self-powered insoles are fabricated through advanced 3D seamless flat-bed knitting technology. Output responses of the NGs are measured at different motion speeds and a maximum value of 41 V is obtained, corresponding to an output power of 168.1 μW. By connecting one NG with an external circuit, the influence of external resistance, capacitor, and motion speed on the charging characteristics of the system is systematically investigated. To demonstrate the potential of the smart insoles for monitoring human gait signals, two subjects were asked to walk on a treadmill at different speeds or with a limp. The results show that one can clearly distinguish walking with a limp from regular slow, normal, and fast walking states by using multiscale entropy analysis of the stride intervals.

Potentially applied in low-noise applications such as structural health monitoring (SHM), a 1-axis piezoelectric MEMS accelerometer based on aerosol deposition is designed, fabricated, simulated, and measured in this study. It is a cantilever beam structure with a tip proof mass and PZT sensing layer. To figure out whether the design is suitable for SHM, working bandwidth and noise level are obtained via simulation. For the first time, we use aerosol deposition method to deposit thick PZT film during the fabrication process to achieve high sensitivity. In performance measurement, we obtain the charge sensitivity, natural frequency, working bandwidth and noise equivalent acceleration of 22.74 pC/g, 867.4 Hz, 10–200 Hz (within ±5% deviation) and 5.6 μg/Hz (at 20 Hz). To demonstrate its feasibility for real applications, vibrations of a fan are measured by our designed sensor and a commercial piezoelectric accelerometer, and the results match well with each other. Moreover, shaker vibration measurement with ADXL1001 indicates that the fabricated sensor has a much lower noise level. In the end, we show that our designed accelerometer has good performance compared to piezoelectric MEMS accelerometers in relevant studies and great potential for low-noise applications compared to low-noise capacitive MEMS accelerometers.

The investigation into the distinctive difference of gait is of significance for the clinical diagnosis of neurodegenerative diseases. However, human gait is affected by many factors like behavior, occupation and so on, and they may confuse the gait differences among Parkinson’s disease, amyotrophic lateral sclerosis, and Huntington’s disease. For the purpose of examining distinctive gait differences of neurodegenerative diseases, this study extracts various features from both vertical ground reaction force and time intervals. Moreover, refined Lempel–Ziv complexity is proposed considering the detailed distribution of signals based on the median and quartiles. Basic features (mean, coefficient of variance, and the asymmetry index), nonlinear dynamic features (Hurst exponent, correlation dimension, largest Lyapunov exponent), and refined Lempel–Ziv complexity of different neurodegenerative diseases are compared statistically by violin plot and Kruskal–Wallis test to reveal distinction and regularities. The comparative analysis results illustrate the gait differences across these neurodegenerative diseases by basic features and nonlinear dynamic features. Classification results by random forest indicate that the refined Lempel–Ziv complexity can robustly enhance the diagnosis accuracy when combined with basic features.