Smart Materials and Structures

This paper introduces a novel piezoelectric actuator (PEA)-driven two-degree-of-freedom micro/nano-positioning stage for high-precision adjustments in small-space situations. To enhance stage compactness and dynamic performance while significantly reducing parasitic motions, the composite leaf-type double parallelogram mechanism and the leaf-type double parallelogram mechanism are presented for implementation in guided and decoupled mechanisms, respectively. The composite bridge arm structure and compact double parallelogram design enable the amplification mechanism to provide significant lateral rigidity and safeguard the PEA.The orthogonal arrangement of the composite bridge type amplification mechanism and the dual parallelogram decoupling guiding mechanism are integrated to achieve full decoupling of the stage at both the input and output ends. The compliance matrix method and Lagrange’s equation were employed to establish mathematical analytical models of the static and dynamic characteristics, which were subsequently validated through finite element analysis. The stage’s static and dynamic performances were thoroughly assessed through experiments. The closed-loop control of the PEA utilizing integral strain gauges effectively eliminates the nonlinear hysteresis issue on the stage and exhibits proficient trajectory tracking capability. The experimental findings indicate that the motion range of the precision positioning stage is 51.71 × 49.56 µm², the output coupling displacement error is under 1%, the displacement resolution is 12 nm, and the natural frequencies of the X-axis and Y-axis are 429 Hz and 438 Hz, respectively.

Inertial piezoelectric drive devices play an important role in manufacturing, but there are many problems such as backward motion, low displacement accuracy, and poor stability. A small backward piezoelectric bimorph actuator is proposed, which uses the principle of inertial drive with the Z-shape dual flexible hinge mechanism (ZDFHM). The ZDFHM makes the actuator have a simple driving signal and circuit, stable output, and small backward ratio. Dynamic model, simulation and experiment are conducted on the ZDFHM and the actuator, comparing with preliminary experimental data, the maximum displacement error between simulation and experiment is 9.4%, which verifies the dynamic model and simulation are reasonable. Experimental results have shown that the proposed actuator can be driven by low-frequency electrical signals, the minimum operating frequency can reach 1 Hz. When using the ZDFHM, the displacement resolution measured at a working frequency of 1 Hz is 0.32 μm, with the backward ratio of the actuator 3.9%.

Graded lattice structures have garnered significant attention for their ability to combine lightweight properties with superior mechanical performance. Their tailored structures / properties under in-plane loading make them ideal for impact resistance and energy absorption applications in aerospace, automotive, and biomedical engineering. Despite the research advances in this field, research into out-of-plane of graded lattice structures is relatively limited. This study investigates the out-of-plane energy absorption performance of three common lattice structures, namely, hexagonal honeycomb (HEX), auxetic re-entrant (REE), and double arrowhead (DAH). Unique grade patterns including bottom thick (BOT), mid thick (MID), and ends thick (EDG), were introduced in the lattices’ out-of-plane direction and their mechanical behaviour was examined under quasi-static compression conditions using both experimental and finite element analysis. Parametric investigations were conducted to examine the effects of grade ratio, cell wall thickness, and grade patterns, aiming to optimize mechanical energy absorption and crashworthiness of each structure. Results show that the specific energy absorption of all structures follows the trend MID > EDGE > BOT > UNI and HEX_MID demonstrated the highest SEA value amongst all designs. On the other hand, all graded lattice structures demonstrated significantly enhanced crash force efficiency, with HEX_BOT, REE_EDGE, and DAH_EDGE approaching ~ 90% of the maximum performance, showcasing their potential for efficient energy absorption.

The promotion of smart agriculture is key to sustainable agriculture and reducing environmental impact. In order to achieve the development of self-powered sustainable smart agriculture, in this paper, a piezoelectric-electromagnetic hybrid energy harvester (PEHEH) is proposed, which based on the magnetic coupling. Composed of a magnetic turntable, coil, driving magnet, response magnet, and piezoelectric cantilever beam, the PEHEH integrates electromagnetic and piezoelectric energy harvesting through magnetic coupling. By fully exploiting magnetic coupling, the PEHEH combines these two energy harvesters, ensuring efficient energy conversion. The non-contact magnetic coupling force excites the piezoelectric cantilever beam, facilitating efficient energy harvesting, enhancing conversion efficiency, and prolonging the operational lifespan. A series of experiments were conducted to investigate the impact of response magnet size and position on the output voltage of the PEHEH. The results show that the prototype, when assembled in accordance with the optimal structural parameters, is capable of providing a continuous and stable power supply for smart agricultural low-power sensors and small electronic devices. In particular, the PEHEH demonstrates high stability and reliability in soil temperature and humidity sensor monitoring, LED light compensation applications. It provides a new power supply solution for sustainable smart agriculture.

Multifunctional mechanical metamaterials can implement multiple mechanical properties, such as both negative Poisson’s ratio (NPR) and compression-twist coupling (CTC) effect, can be applied in complex scenarios in aerospace, biomedical engineering, and other engineering applications. A novel re-entrant Z-shaped (ReZ) structure combining the NPR re-entrant structure and the CTC Z-shaped structure has been proposed. The effects of the geometrical parameters on the equivalent Poisson’s ratio and torsion angle are analyzed by finite element analysis and experiments. The results show that the ReZ structure can exhibit NPR as well as CTC effects by adjusting the geometric parameters. Both NPR and the CTC effect can be tuned over a wide range by modifying the degree of concavity and the height of the unit cell of structure. Finally, two types of enhanced ReZ structures are proposed and discussed. The proposed ReZ structure can enhance the design flexibility and serve as a reference for the design of new multifunctional mechanical metamaterials.

Negative stiffness honeycomb unit cells (NSH-UCs), which employ negative stiffness beams (NSBs), are capable of absorbing impact energy and are reusable, making them promising for energy-absorbing structural applications. However, limited research has addressed their operational lifespan and manufacturing variability, both of which are critical for practical implementation. This study aims to establish a design optimization framework for NSH-UCs that accounts for both performance metrics—such as energy absorption—and operational aspects like reusability and manufacturing-induced uncertainty. To this end, NSH-UC samples with varying dimensions were fabricated using fused filament fabrication (FFF) with PLA/PHA filaments, and their mechanical behavior was evaluated through quasi-static and cyclic compression tests. A surrogate-based optimization method was then applied to improve energy absorption and extend service life, while considering geometric and material uncertainties inherent to the additive manufacturing process. The proposed framework led to a significant improvement in energy absorption (EA) and end-of-life (EOL) performance compared to the initial design, despite only modest changes in specific energy absorption (SEA). These findings demonstrate the feasibility of incorporating performance, reliability, and manufacturing variability into early-stage design, highlighting the framework’s potential for structural health monitoring (SHM) and prognostic health management (PHM) applications.

Satellites are subjected to various complex and harsh time-varying loads during the launch phase. These loads will cause satellites’ broadband violent vibration. It is an effective method to reduce the vibration response of satellites by installing dynamic vibration absorbers (DVAs) on the vibration isolation platform of the whole-spacecraft. In this paper, the concept of variable damping and stiffness is introduced into DVAs to broaden the vibration absorption bandwidth of traditional DVAs. The structural scheme of the variable damping and stiffness magnetor-heological DVA (VDS-MRDVA) is proposed. MR fluid porous fabric is utilized to achieve the regulation of damping and stiffness. It can effectively solve the sedimentation problem of MR fluid, and it does not require sealing, making the structure of the VDS-MRDVA more compact. The mechanical models of the VDS-MRDVA are established and analyzed. Subsequently, the key structural parameters of the VDS-MRDVA are designed. And the output mechanical properties of the VDS-MRDVA are tested, the test results reveal that the VDS-MRDVA has excellent controllable variable damping and stiffness characteristics. The adjustable damping ratio and stiffness ratio are 24.47 and 1.904 respectively. Finally, the whole-spacecraft vibration absorption simulation and test are carried out. The simulation results demonstrate the advantages and effectiveness of the VDS-MRDVA. And the experimental results show that the VDS-MRDVA exhibits distinct vibration absorption performance under different stiffness and damping. The peak acceleration is reduced by 32.75% when the variable damping current is 0 A, while the acceleration at the satellite’s resonance frequency decreased by 44.74% when the variable stiffness current is 2 A. These results demonstrate significant vibration absorption effectiveness.

In response to the complex nonlinear characteristics of magnetorheological (MR) dampers, which make it difficult to achieve strict vibration reduction requirements in time-varying working environments due to uncertainties, this paper aims to develop a refined model for the MR damper and improve the deep reinforcement learning network based on its dynamic characteristics to optimize the control strategy. Firstly, a refined dynamic model for the MR damper is designed, and its Simulink model is established and corrected based on the characteristic data of the custom damper. Secondly, an optimization framework for suspension control is constructed, where the suspension and road surface serve as the environment, and the proximal policy optimization (PPO) algorithm is used as the agent. Next, the nonlinear relationship between the damping force of the damper and its performance indicators is explored, and a PPO-neural ordinary differential equations (NODEs) algorithm is proposed by combining NODE with PPO. This approach allows for continuous fitting of state variables, enabling high-precision nonlinear fitting between the state space and policy representation. Finally, a quarter-car test bench is set up using the custom MR damper, and the simulation and experimental results of passive suspension, fuzzy PID, original PPO, and the proposed method are compared. The experimental results show that the proposed method demonstrates significant advantages: under class C road conditions, the body acceleration in simulation and test bench experiments is reduced by 30.22% and 77.51% respectively compared to passive suspension; 20.55% and 37.82% compared to fuzzy PID; and 11.26% and 25.53% compared to the original PPO. Additionally, the simulation results on Class B random road further validate the good generalization performance of the proposed method.

Composite materials have been extensively employed in industrial fields due to their superior properties. Structural health monitoring and non-destructive testing techniques based on Lamb waves have been used for damage detection in composite laminates. Recently, deep learning algorithms have attracted attention and been utilized to extract damage features from Lamb waves. To fully exploit damage characteristics in Lamb waves and improve the performance of trained models, an long short-term memory (LSTM)-based method for damage localization and novel loss functions for model training are proposed in this study. Firstly, an LSTM-based network is established to extract features from sequential Lamb wave signals. Then, numerical simulated signals are generated to construct a training dataset. During model training, location-based and sparsity-based loss functions are designed for the optimization of network parameters. Since the proposed loss functions have physical interpretability to some extent, they are expected to accelerate the convergence of model training and increase the generalization ability of trained networks. The experiment is implemented on a carbon fiber reinforced plastics plate with simulated damage. The results demonstrate that the networks trained with numerical data can localize damage in the experimental specimen with good generalization ability, which validates the effectiveness of the proposed approach for damage localization in composite laminates.

The regulation of the grain morphology and orientation in Ni-Mn-Ga shape memory alloys (SMAs) has been extensively employed to facilitate the coordinated deformation of grains and enhance the inherent brittleness of the material, enabling the attainment of maximum shape memory strain and the elimination of strain hysteresis. Herein, a highly preferred orientation oligocrystallized Ni53Mn23Ga24 (at. %) microwire with a phase transition temperature near room temperature demonstrates exceptional tensile mechanical properties. Under a stress-controlled tensile mode, the multi-step superelastic behavior was confirmed at stress-temperature coupling condition within a temperature span of 268 to 313 K. Furthermore, the augmentation of the abnormal apparent elastic modulus with increasing temperature was ascribed to the martensite-austenite transformation. A phase diagram delineating the relationship between the critical stress and temperature was meticulously constructed. Under a strain-controlled tensile mode, the cyclic stress-strain curves exhibited fully reversible strain, path effect, and multi-step phase transition behavior at room temperature. The enhanced mechanical characteristics of the NiMnGa SMAs under both stress- and strain-controlled tensile modes were also investigated.

Artificial ferroelectric superlattices demonstrate exceptional piezoelectric and dielectric properties, which have garnered substantial research interest. Accurate characterization of these properties is indispensable for the application of ferroelectric superlattices. Since most ferroelectric materials are brittle, a comprehensive understanding of the fracture behavior is essential to ensure the reliability of superlattices. This study develops a coupled phase field model to simulate crack propagation with domain evolution in PbTiO3/SrTiO3 superlattices. In this work, we examine the superlattice beam under three-point bending load. At the initial stage, a stable array of polarization vortices exists within the PbTiO3 layers. Despite the occurrence of localized stretching, merging, or domain transformation of the polarization vortex near the crack tip, the polarization in PbTiO3 gradually evolves back into a vortex array after the crack passes through. This process also contributes to a toughening effect, which delays crack propagation. This work contributes to the assessment of the structural reliability of ferroelectric devices for engineering applications.

Negative stiffness mechanical metamaterials (NSMMs) have enormous application potential in fields such as energy absorption due to their unique mechanical properties. Currently, a large number of NSMMs with novel configurations have been proposed, greatly enriching the concept of such structural materials. However, the hierarchical structural design approach, commonly used in periodic structures, has not been applied in the design of NSMMs. This study proposes a new type of NSMM with hierarchical structural characteristics. Through experiments and numerical simulations, the mechanical properties and energy absorption performance of this hierarchical negative stiffness metamaterial were studied. The effects of structural parameters on the performance characteristics were analyzed, and a comparison was made between this hierarchical structure and traditional structures. The study shows that the hierarchical design can effectively optimize the mechanical response curve of NSMMs, constructing a flatter stress platform, which promotes the enhancement of energy absorption performance for such structures.

This is a roadmap article with multiple contributors on different aspects of embodying intelligence and computing in the mechanical domain of functional materials and structures. Overall, an IOP roadmap article is a broad, multi-author review with leaders in the field discussing the latest developments, commissioned by the editorial board. The intention here is to cover various topics of adaptive structural and material systems with mechano-intelligence in the overall roadmap, with twelve sections in total. These sections cover topics from materials to devices to systems, such as computational metamaterials, neuromorphic materials, mechanical and material logic, mechanical memory, soft matter computing, physical reservoir computing, wave-based computing, morphological computing, mechanical neural networks, plant-inspired intelligence, pneumatic logic circuits, intelligent robotics, and embodying mechano-intelligence for engineering functionalities via physical computing. In this paper, we view all the sections with equal contributions to the overall roadmap article and thus list the authorship on the front page via alphabetical order of their last names. On the other hand, for each individual section, the authors decide on their own the order of authorship. (Abstract written by Guest Editors Kon-Well Wang (aka K W Wang) and Suyi Li.)

Recently, additive manufacturing fabrications are commonly applied to produce acoustic metamaterials or phononic crystals as tools for complex geometrical designs. However, the material properties of those additive manufactured materials are less involved in the core portion of those phononic crystal designs. Here we report a purely materials-driven, temperature switchable PnC in which Bragg gaps appear or vanish as the lattice medium toggles between liquid water and solid ice. Six widely used additive manufacturing polymers were acoustically characterized, where stereolithography resins showed an impedance mismatch of ≈50 % with water but <1 % with ice, whereas inkjet agar gel exhibited the opposite trend. A 10 × 10 stereolithography resin PnC therefore displayed >20 dB on/off contrast at 145 kHz and around 300 kHz when cycled across 0 °C, confirmed experimentally and with plane wave and simulation models. Unlike previous thermally tuned phononic crystals that depend on volumetric swelling or liquid metal infiltration, the present approach preserves geometry, requires no external actuators and operates with sub 1 °C stability. This simple, robust strategy lays the foundation for band pass filters, steerable lenses and non-reciprocal acoustic circuits that can be frozen or thawed on demand.

In this work, we proposed a differential method based on the quantitative electromechanical impedance (Q-EMI) technique to measure elastic moduli and internal frictions under ultrahigh temperature. Different from traditional Q-EMI method using one specimen, here two cylindrical specimens are put in the furnace, one longer and another shorter, with matching resonance frequencies. The mechanical properties under high temperature are determined by the difference between the two measurements. This method avoids the usage of high-temperature adhesive for bonding, which may break during cooling. Firstly, a two-component differential Q-EMI method is introduced, and the modulus and internal friction measurement results for yttria-stabilized zirconia and 99% alumina were presented from room temperature to 1200 °C. Then, a three-component differential Q-EMI method is introduced, with the modulus and internal friction measurement results for Ti-6Al-4V from room temperature to 1100 °C. The proposed differential Q-EMI method is very suitable for mechanical characterization of high-temperature ceramics/alloys and is expected to be widely used in future.

A simplified model of magnetomechanical coupling for hard magnetoactive elastomers (h-MAEs) is developed, notable for its computational speed and good agreement with experiments. The model is based on the forces acting on magnetic dipoles in a magnetic field. It utilizes the finite element method, with each element represented as a magnetic dipole. The fabrication process and the simulation of magnetic-field-induced deformation of a soft gripper made of h-MAE are conducted. The mechanical properties are measured using tensile testing and the magnetic characteristics are evaluated through magnetic hysteresis loops. The deformation of the gripper is analyzed with digital image correlation (DIC) and compared to the simulation results. The model provides efficient and fast computation with reliable results, facilitating the design of soft robots with various functionalities.

Identifying the dynamic parameters of bridges based on real-time monitoring data enables the timely detection of abnormal structural changes. Modal frequency, a critical parameter in characterizing bridge dynamic properties, serves as a key indicator of structural health condition. However, modal frequencies derived from bridge monitoring data are often influenced by external environmental factors, such as temperature and vehicle loads. These influences can obscure or distort the identification of structural damage, leading to inaccurate evaluations of the bridge's health status. To address this issue, a hybrid method combining autoencoders with Particle Swarm Optimization Backpropagation (AE-PSO-BP) artificial neural networks was proposed, where temperature and vehicle load parameters were simultaneously adjusted. The autoencoder was employed for dimensionality reduction and decoupling of the original variables, which were then used as inputs to train a Particle Swarm Optimization Backpropagation (PSO-BP) model for predicting modal frequencies. This approach effectively eliminates the influences of temperature and vehicle loads on bridge modal frequencies, as validated through numerical simulations. Subsequently, this method was applied to actual bridge data, resulting in corrected modal frequencies after eliminating the effects of temperature and vehicle loads. Meanwhile, scoring criteria were formulated to assess the dynamic characteristics of bridges based on the corrected modal frequencies.

In the field of precision positioning and micro-manipulation, achieving high-precision motion with minimal hysteresis remains a significant challenge. In this paper, a symmetric high-precision 2-DOF linear piezoelectric platform (PEP) is proposed to address this issue. A single bonded-type piezoelectric actuator (PEA), utilizing fewer piezoelectric ceramic slices, drives the flexible stage to achieve two-dimensional motion with reduced hysteresis. The symmetric structural design eliminates long-term unbalanced loading, thereby enhancing mechanical stability. A hysteretic dynamics hybrid model of the PEP is established, and it is used to design the feedforward controller to achieve high accuracy. A prototype PEP has been fabricated for tests to verify its performances. The open-loop experiment results show that the proposed PEP has low hysteresis (7% hysteresis rate), high displacement resolution (3 nm), and can output ±9.7 μm × ±9.7 μm stroke. The closed-loop experimental results show that the repeated positioning precision of the PEP is ±6 nm, the hysteresis rate is 0.1%, and the relative trajectory tracking error is less than 0.07%. This work can broaden the application prospects of the PEP in precision positioning and micro-manipulation.

Twisted polymer artificial muscle is a promising low-cost soft actuation that can mimic natural muscles by sensing external stimuli and generate torsional deformation. Here, we investigate the effect of draw ratio on the torsional actuation performances of twisted Nylon-6 fiber from an experimental and theoretical perspective. Experiments show that increasing the draw ratio enhances the fiber orientation and significantly increases Young’s modulus and shear modulus. The thermal anisotropy of the precursor fiber and the recovered torque generated by twisted fiber increase dramatically with increasing draw ratio. The Yang-Li thermal-mechanical actuation model is used to predict the torsional actuation performance of twisted fiber with different draw ratios. The theoretical predictions of recovered torque align well with the experimental results. Analysis of stress distributions within the cross-section of the twisted fiber shows that the higher draw ratio results in larger stress. This study sheds light on the structural optimization design of thermally-activated artificial muscles.

Quantifying reliability is essential in evaluating and assessing inspection technologies, whether they fall under nondestructive evaluation or structural health monitoring methods. A key challenge in computer vision is uncovering the underlying dynamics of objects within image sequences, especially in scenarios involving damage. Lead Zirconate Titanate (PZT) is a piezoelectric material that is highly effective in generating ultrasonic waves for various engineering and scientific purposes. An experimental method utilizing Coulomb coupling has been developed to visualize ultrasonic wave interactions with microscale defects in PZT. A modified Convolutional Autoencoder-based Dynamic Mode Decomposition (CAE-DMD) algorithm was developed and applied to spatiotemporal data from healthy and damaged conditions to capture image dynamics. The modified CAE-DMD algorithm is applied in a reference-free manner, enabling the identification of both the background mode and the specific damage location without the need for healthy state data. The framework was validated under two scenarios: one involving only damaged specimens and another incorporating both damaged and healthy specimens. The algorithm demonstrated exceptional performance in both cases, achieving a damage detection and localization accuracy of 96% , verified through visual evaluation. Including an image registration framework and damage localization techniques based on K-means clustering and contour detection further improved the system’s precision and reliability.

This study investigates the compression behavior, energy absorption, shape memory properties, and reusability of 4D-printed smart mechanical metamaterials. Four structural configurations, i.e. honeycomb, re-entrant, and two modified re-entrant designs were developed to assess microstructure effects. Samples were fabricated using Polylactic Acid (PLA), a widely used shape memory polymer (SMP) in 4D printing, and polyethylene terephthalate glycol (PETG), an emerging SMP with demonstrated shape memory performance in recent studies. Cold-programming-induced shape recovery was evaluated at room temperature, simulating real-world conditions. Finite element simulations of compression and shape memory cycles matched experimental results well. Auxetic samples with negative Poisson’s ratios showed superior energy absorption. However, only PETG demonstrated sufficient reusability, while PLA proved unsuitable for reusable designs. The PETG-3 modified re-entrant structure exhibited the best performance, with high energy absorption, delayed densification onset, and shape recovery and reusability factors of 0.95 and 0.96, respectively. Findings highlight the importance of considering both shape recovery and reusability when designing smart structures to address industrial challenges.

Structural vibrations pose significant challenges in engineering applications, necessitating efficient solutions for both vibration suppression and energy harvesting. Conventional absorbers struggle to optimise these two aspects simultaneously. This study introduces stiffened amplifier energy harvesting vibration absorbers (SAEHVAs), which leverage mass and stiffness amplification mechanisms to enhance vibration mitigation while maximising energy harvesting efficiency. Using the H_2 optimisation scheme, closed-form expressions for optimal absorber parameters were derived, and their performance was evaluated under harmonic and random excitations. The results demonstrated that CSAEHVA, NSAEHVA, and LSAEHVA reduced structural displacement by 56.60 %, 52.59 %, and 43.60 %, respectively, outperforming conventional tuned mass dampers. Under random excitations, these absorbers exhibited even greater effectiveness, achieving vibration reductions of 77.19 %, 73.15 %, and 61.20 %, respectively. Compared to conventional inerter-based energy harvesting absorbers, CSAEHVA, NSAEHVA, and LSAEHVA achieved superior suppression of 97.08 %, 96.56 %, and 95.64 %, respectively, while also significantly enhancing energy harvesting capabilities. These findings highlight the potential of SAEHVAs as dual-function solutions for vibration control and energy harvesting, offering promising applications in smart infrastructure, renewable energy systems, and self-powered monitoring technologies.

Frequency-dependent stiffness and damping are critical properties of magnetorheological elastomer (MRE) isolators, significantly influencing their vibration response, modeling and control applications. Existing methods are limited to acquiring dynamic characteristics at a single-frequency per test, making it challenging to obtain continuous frequency-dependent properties of MRE isolators. This study proposes a novel method to determine the continuous frequency-dependent stiffness and damping of MRE isolator via one-time test. Based on a theoretical analysis of the MRE isolator-mass system model, the continuous frequency-dependent stiffness and damping are derived and analyzed, through a series of real and imaginary component transformations on the reciprocal of transfer function. The proposed method identifies the continuous frequency-dependent dynamic characteristics using only a single sweep test result, without additional tooling and test system. The results reveal that the dynamic stiffness of MRE isolator generally increases with frequency across various current levels, while the damping consistently decreases. Furthermore, the trough and inflection point in the frequency-dependent stiffness and damping curves effectively capture the resonance information of MRE isolation system. Beyond enhancing the practical application and performance analysis of MRE isolators, this method is also universal for the frequency-dependent analysis of other actuators, such as MR dampers, rubber isolators, etc.

Polymer-ceramic piezoelectric composites offer a promising solution for applications requiring flexible, lightweight materials with enhanced electromechanical properties. By combining the flexibility of polymers with piezoelectric ceramics, these composites can exhibit superior dielectric and piezoelectric behavior, making them ideal for use in sensors, actuators, and energy harvesting devices. In the present study, poly (vinylidene fluoride) (PVDF) was reinforced with 2 to 10 wt.% of aluminum nitride (AlN) nanoparticles to enhance its thermal and piezoelectric properties, which is important for integration with microelectromechanical systems (MEMS) manufacturing and applications. X-ray diffraction revealed the formation of polar-phase PVDF, with improved crystallinity due to the nucleating effect of AlN nanoparticles. Dielectric measurements, performed across various frequencies and temperatures, showed that AlN improved the thermal stability of PVDF, making it more suitable for use in electronic films at elevated temperatures. Furthermore, the poled nanocomposite with 10 wt.% of AlN showed a higher d33 value of 16.3 pC/N, and maintained a higher piezoelectric performance, compared to pristine PVDF (6.5 pC/N), under similar thermal conditions. Moreover, the fabricated PVDF/10AlN nanocomposite energy harvesting device displayed a higher peak-to-peak voltage of ~26.6 V and a peak power density of 9.9 μW/cm2 under an applied force. Consequently, the nanocomposite device maintained a stable piezoelectric performance at higher temperatures, demonstrating its potential for practical energy harvesting applications at elevated temperatures.

This study systematically investigates the thermomechanical characterization of crosslinked poly(ethylene-co-vinyl acetate) (cEVA) nanocomposites reinforced with multi-walled carbon nanotubes (MWCNTs). Through melt-blending and thermo-curing, nanocomposites with 1–5 wt% MWCNTs were synthesized and rigorously evaluated via differential scanning calorimetry, dynamic mechanical analysis, and thermomechanical tests. The nanocomposites exhibit dual-responsive one-way (1W-SME) and two-way (2W-SME) shape memory effects under constant stress/strain conditions, driven by crystallization-induced elongation and melting-induced contraction mechanisms, with effective transition temperatures deviating from DSC-measured values due to mechanical loading. Large reversible strains in 2W-SMEs and good shape fixity/recoverability in 1W-SMEs were achieved. The study further elucidates the influence of MWCNT content, cooling/heating rates, and external loads on the thermomechanical responses, providing experimental and theoretical foundations for designing programmable actuators and adaptive structures. The integration of nanofiller-mediated crystallization control and thermomechanical coupling mechanisms highlights a novel pathway to tailor high-performance smart materials with predictable recovery and scalability.