Search Results (1,338)

Under low-temperature and humid conditions, icing on airfoil surfaces, such as wind turbine blades, deteriorates the aerodynamic performance and decreases the power generation efficiency. To shorten the de-icing time and reduce the de-icing energy consumption, an ultrasonic de-icing method was used by coupling the longitudinal vibration of a piezoelectric transducer and the bending deformation of an iced plate. The simulation method was used to investigate the distributions and the variations of the stresses at the bond interface. An experimental system for ultrasonic de-icing tests was developed and built, and the de-icing experiments were carried out. The experimental results showed that the present ultrasonic de-icing method had a short de-icing time and low de-icing energy consumption, and the de-icing processes agreed with the simulation results. In the present research, the ice layer with a diameter of 20 mm was removed in the shortest de-icing time and the lowest energy consumption because its diameter was close to that of the transducer, which resulted in the highest shear stress at the bond interface. The present study provides theoretical and experimental foundations for deep research on the surface anti- and de-icing method with ultrasonic techniques. Full article

The construction sector is undergoing a rapid transition toward more resilient, sustainable, and digitally connected systems, creating increasing demand for materials capable of providing functions beyond conventional structural performance. In this context, smart materials have emerged as promising solutions due to their ability to respond to mechanical, thermal, chemical, or electromagnetic stimuli through adaptive behaviors such as self-healing, structural sensing, energy regulation, vibration control, and reversible deformation. Despite growing scientific interest, available knowledge remains fragmented across specific material families and isolated application domains. Therefore, this study presents a PRISMA-based systematic review of smart materials in construction using peer-reviewed journal literature indexed in Scopus during the 2021–2026 period. The review examines the principal smart material families currently applied in construction, including self-healing concretes, self-sensing cementitious systems, Shape Memory Alloys (SMA), piezoelectric materials, phase change materials, adaptive coatings, conductive nanocomposites, and multifunctional geopolymers. Their engineering functions, structural and architectural applications, reported performance characteristics, sustainability contributions, digital integration potential, and implementation barriers are comparatively discussed and qualitatively synthesized based on the reviewed literature. The findings indicate that smart materials can improve durability, structural health monitoring, seismic resilience, thermal efficiency, lifecycle performance, and carbon reduction when properly integrated into buildings and infrastructure. However, large-scale adoption remains constrained by high initial costs, manufacturing scalability, regulatory uncertainty, long-term durability validation, and limited market confidence. The review further shows that the greatest future potential lies in combining material intelligence with IoT platforms, artificial intelligence, BIM environments, and digital twins. Overall, smart materials are positioned as strategic enablers of next-generation low-carbon, adaptive, and intelligent construction systems. Full article

Ultrasonic thickness resonance can be effectively used to detect and quantify the level of corrosion in steel nuclear storage containers as well as other corrosion-prone thin-walled structures, such as pipes and storage tanks. Electro-Magnetic Acoustic Transducers (EMATs) have several advantages over more traditional piezoelectric-based transducers; namely, they can be used in a non-contact fashion on robotic platforms, allowing for measurements regardless of surface conditions or temperature. The major challenge of EMAT application is the power required to counteract the low actuation efficiency, which is achieved with a high-power ultrasonic pulse generator and a transformer circuit. Resonance techniques, in which most of the energy is concentrated near structural resonance frequencies, are preferable to improve efficiency of electro-magnetic acoustic measurements. This methodology was applied to 316L stainless steel thin plates subjected to uniform corrosion as well as pitting corrosion imitating different damage scenarios in a nuclear waste container. The resonant peak frequency shift was found to be proportional to the severity of corrosion for minimally corroded samples. However, the complete disappearance of the resonance peak was observed in the samples with severe corrosion damage. The EMAT liftoff distance was studied to quantify its effect on the amplitude, spread, and frequency of resonant peaks. Recommendations for use of EMATs for assessing corrosion damage are presented. The study demonstrates the success of frequency-based detection of corrosion damage in 316L stainless steel used in fabrication of nuclear waste storage containers. Full article

The spatial organization of microscale fillers is critical for macroscopic performance, yet precise control over their distribution and orientation remains a major challenge in direct ink writing. Here, we present an acoustofluidic capillary nozzle that integrates acoustic manipulation into direct ink writing, enabling programmable in situ assembly of functional fillers during extrusion. By coupling a piezoelectric transducer with a commercial glass capillary, stable acoustic standing waves are established within the flow channel, driving suspended filler particles toward pressure nodes via acoustic radiation forces. Simulations and experiments systematically investigate how capillary geometry and material properties influence acoustic energy distribution and particle assembly behavior. In particular, rectangular capillaries generate stable multi-node standing waves, inducing periodic alignment of nickel-coated carbon fibers into ordered conductive bundles. This acoustically programmed microstructure reduces the percolation threshold from 8 wt% to 2 wt% and enhances electrical conductivity by up to 32.1-fold at identical filler contents. Meanwhile, the composites exhibit pronounced anisotropic conductivity and maintain excellent mechanical flexibility, with stable electromechanical performance under 16% bending strain and cyclic loading. This work demonstrates a simple and scalable acoustofluidic nozzle platform for programmable microstructure engineering in direct ink writing, offering new opportunities for fabricating high-performance multifunctional composites. Full article

The increasing demand of digital technologies and their integration with wearable health devices provides an efficient trigger for next-generation wearable healthcare devices for long-term physiological monitoring. The advancement of energy harvesting mechanism, nanomaterial-based sensor fabrication and their integration with digital technologies have emerged as a promising solution for transforming future of digital health. This study provides a comprehensive summary and framework for wearable self-powered electronic devices, enabling continuous, battery-free health monitoring and advancing the development of sustainable, next-generation digital healthcare systems. This review paper presents a broad and detailed overview of current technologies and sensors advancement in developing low-power wearable, self-powered electronic devices suitable for healthcare applications. The importance and reliable use of key energy harvesting approaches including triboelectric, piezoelectric, thermoelectric, and photovoltaic approaches are systematically presented which focused on development of energy efficient wearable devices. This review further examines the low-power circuit design strategies for flexible electronics focusing personalized healthcare monitoring. Current challenges and limitations related to advanced manufacturing of wearable health devices focusing on large-scale deployment are also analyzed. Finally, the key future research directions are outlined for advancing a next-generation intelligent digital health system. Full article

This study proposes a novel simulation-driven intelligent framework for the performance and reliability assessment of renewable energy-integrated pavement systems by unifying coupled multiphysics finite element modeling, structured dataset generation, and graph-based artificial intelligence within a single computational paradigm. The proposed pavement is formulated as a seven-layer multifunctional infrastructure system comprising the asphalt surface, intermediate binder, base layer, thermoelectric energy layer, piezoelectric insert zone, subbase, and subgrade soil, thereby enabling simultaneous consideration of structural load transfer, thermal gradient-driven energy harvesting, moisture-sensitive support behavior, and reliability-oriented performance interpretation. A three-dimensional thermo-hydro-mechanical Abaqus model was developed to simulate the concurrent effects of moving wheel load, solar heat flux, rainfall infiltration, and internal moisture diffusion, and it was subsequently used to construct an AI-ready dataset containing 6000 simulation cases and 68 variables spanning geometric, material, environmental, traffic, uncertainty, structural, thermal, hydraulic, renewable-energy, and probabilistic reliability descriptors. To preserve the physical hierarchy of the layered pavement within the learning process, a Layer-Coupled Reliability Graph Operator Network (LaRGO-Net) was proposed, in which pavement layers are represented as interacting graph nodes linked through adaptive interlayer coupling and optimized through multi-task, physics-aware, and coupling-consistent learning. Experimental evaluation across nine progressive configurations demonstrated a monotonic improvement from baseline dense and graph-convolution models to the full LaRGO-Net formulation. The final model achieved the best overall performance with mean RMSE = 0.040, mean MAE = 0.028, mean $R^2=0.994$, and reliability prediction accuracy characterized by F1 = 99.21 and AUC = 99.53. These results confirm that the proposed framework provides a highly accurate, physically interpretable, and reliability-aware surrogate for next-generation pavement systems capable of simultaneously supporting structural serviceability, renewable-energy functionality, and intelligent decision-making. Full article

Two semiflexible piezoelectric composite plate structures were developed, incorporating 1 × 9 and 2 × 9 arrays of PZT elements mounted on brass discs and mechanically secured by pop rivets within a thin plastic foil spacer positioned between two copper-clad PCB layers. This configuration provides reliable electrical contact, adequate mechanical compliance, and efficient conversion of mechanical vibration energy into electrical energy. In addition, a multifunctional thermoelectric device was realized, consisting of four cubic modules arranged around a rectangular tube and enabling both handheld operation and coupling to hot or cold surfaces. Each cube is equipped with optimized finned heat sinks and integrates four thermoelectric elements on each face. Experimental results show that each cube generates approximately 6 mW, when handheld and with icy water injected into the central tube, demonstrating its suitability as a compact and versatile thermal energy harvester. Under low-light conditions, a solar panel is supplemented by this hybrid piezoelectric–thermoelectric energy harvesting system that combines the output of a piezoelectric composite plate with the dual outputs of a thermoelectric device using an electronically isolated summing block to ensure source decoupling. Energy storage and management are implemented using a capacitor buffer for the piezoelectric device, two voltage boosters for the thermoelectric outputs, and an automatic ultra-low-power pulse width modulation buck regulator for charging supercapacitors at 5 V. Full article

Cardiovascular diseases remain the leading cause of mortality worldwide, highlighting the urgent need for more effective therapeutic strategies. Despite substantial advances in conventional biomaterials, their limited ability to support functional integration and dynamically interact with the biological microenvironment continues to hinder therapeutic outcomes. Native cardiovascular tissues rely on tightly regulated bioelectrical signaling to coordinate cellular communication, tissue homeostasis, and functional repair. Consequently, recreating these bioelectrical cues has emerged as a key design principle in cardiovascular tissue engineering. Electroactive biomaterials have gained increasing attention as a promising platform to address this challenge by enabling electrical modulation of cellular behavior and tissue function. In this review, we summarize the intrinsic bioelectrical properties of cardiovascular tissues and discuss the roles of electrical stimulation in regulating disease-relevant cellular responses. We further highlight recent advances in the development of conductive, piezoelectric, and other electroactive biomaterials for cardiovascular tissue engineering applications. Finally, we critically discuss the major challenges and future opportunities in the field, including tissue-specific responses, stimulation parameter optimization, long-term safety, and clinical translation. Collectively, electroactive biomaterials represent a promising and rapidly evolving frontier for the development of dynamic, responsive, and next-generation therapies for cardiovascular diseases. Full article

This study presents numerical and experimental investigations of a voltage-controlled active preload adjustment system for an ultrasonic traveling wave piezoelectric motor intended for potential use in space-related systems. The proposed preload system consists of two ring-shaped piezoceramic elements driven by a DC voltage of up to 300 V_DC. The passive conical spring provides the nominal rotor preload, while the piezoelectric ring stack enables open-loop remote fine adjustment of the stator–rotor contact force by modifying the axial compression of the spring. Finite element simulations were performed over a temperature range from −25 °C to 55 °C to evaluate the electromechanical response and thermal sensitivity of the preload system. The numerical results indicated that the active preload system can generate a simulated preload force variation of approximately 0.47 N at 300 V_DC, corresponding to approximately 21.4% of the nominal initial preload force of 2.2 N. Experimental tests were conducted in a thermal vacuum chamber at a pressure of 5.6 × 10⁻⁶ mbar. The measured displacement of the piezoceramic preload stack ranged from 0.33 µm to 2.36 µm and showed good agreement with the numerical displacement results. Motor speed measurements demonstrated that increasing the preload-control voltage from 0 to 300 V_DC resulted in an average angular speed increase of approximately 17–20 RPM, depending on temperature. The results demonstrate that the proposed system can provide compact open-loop preload fine adjustment under thermal vacuum conditions, with preload force variation supported by FEM estimation and experimentally validated displacement response. Full article

Deep learning networks facilitate automated metal material/structural stress identification when employing the electromechanical impedance/admittance (EMI/EMA) of piezoelectric ceramic (PZT) transducers, while insufficient data quantity and low quality usually restrict the performance of data-driven deep networks. To address this problem, this paper innovatively proposed an original data enhancement method using the EMA generative adversarial network (EMAGAN) to overcome measurement data inefficiency and deficiency for deep learning-based stress identification, which is difficult to accomplish using the traditional EMA technique. In this method, a novel data-normalized algorithm was tuned to collaboratively foster the EMAGAN-based dataset generation. Then, the synthetic datasets incorporated with original ones were fed into an adaptively established one-dimensional convolutional neural network (1DCNN) for accurate stress prediction. A validating experiment was performed on an aluminum beam specimen subjected to uniaxial tensile load until failure, which was continuously monitored via two surface-bonded PZT transducers. The efficacy of the generated EMA datasets was evaluated through comparison with the raw ones in terms of statistical errors and deep learning-based aluminum structural stress identification. The results demonstrated that the EMAGAN generated high-accuracy EMA data which exceeded 380 times that of the normal collection method, and the EMAGAN paired with 1DCNN provides a promising way for EMA data-driven metal structural stress identification with high efficiency, intelligence and accuracy. Full article

The long-term stability of dental implants is limited by multiple factors, including peri-implant infection, impaired osseointegration, and poor soft tissue sealing. Compared with conventional passive surface modification strategies, piezoelectric materials can convert mechanical energy into local electrical signals under occlusal loading, cell traction, or ultrasonic stimulation. With the aid of defect engineering, heterostructure construction, and co-catalytic design, these materials can also induce the generation of reactive oxygen species and reactive nitrogen species, thereby enabling on-demand antibacterial activity. This review systematically summarizes the bioelectric basis of bone tissue and clarifies how piezoelectricity and piezocatalysis may be used in dental implant surface engineering. Their applications are discussed in terms of antibiofilm and antibacterial activity, osteogenesis and osseointegration, osteoimmunomodulation, soft tissue healing, and temporally programmed therapy. In addition, this review also discusses issues that remain unresolved, such as polymer-based composite systems, realistic activation windows, evaluation standards, device–material integration, and multi-omics validation. Overall, piezoelectric surface engineering is evolving from a single osteogenesis-oriented strategy into an integrated platform that coordinates infection control, immune remodeling, and osseointegration. However, the actual effectiveness of its clinical application still needs to be determined through more rigorous mechanism analysis, long-term stability assessment, biosafety assessment, and standardized preclinical research. Full article

Structural health monitoring (SHM) of additively manufactured (AM) small and complex components is investigated using a sensor-based signal processing and machine-learning framework. Guided-wave responses acquired from piezoelectric transducers are analyzed to evaluate the performance of sweep-sine and pulse excitation signals, as well as the influence of infill patterns, part geometry, and defect type on system reliability. Test specimens, including dogbone structures and a simulated rocket-nozzle component, were fabricated using AM, and nonstationary guided-wave signals were recorded and processed. Time–frequency signal representations (scalograms) were generated using the Continuous Wavelet Transform (CWT). Convolutional Neural Networks (CNNs) and Gaussian Mixture Models (GMMs) were employed for supervised classification and unsupervised clustering, respectively. Sweep-sine excitation consistently yielded higher classification accuracy, with CNN analysis achieving near-perfect performance and GMM clustering demonstrating improved group separability. In contrast, pulse excitation revealed transient signal features associated with wave interactions, including reflections, mode conversion, and scattering, highlighting its potential for complementary signal-based diagnostics. Importantly, the proposed hybrid supervised–unsupervised learning framework enables the quantification of previously unseen intermediate load states, demonstrating strong adaptability and generalizability beyond the conditions represented in the training data. Full article

In this work, the effect of liquids with different dielectric permittivities and acoustic impedances on the characteristics of the backward antisymmetric A1 Lamb wave propagating in a YX LiNbO3 plate was investigated theoretically, numerically and experimentally for the first time. It was found that the dielectric constant and acoustic impedance (density) of a liquid make independent and separable contributions to measured parameters of interdigital transducers, such as the resonant frequency and Q-factor. It was shown that the backward A1 Lamb wave in a YX LiNbO3 plate can be effectively used as a basis for multiparametric liquid sensors. The results obtained are both of fundamental importance for understanding the physics of propagation of backward acoustic waves in piezoelectric plates with a liquid load and of applied value for the development of a new generation of acousto-electronic sensors based on such waves. Full article

Low-frequency traffic noise below 500 Hz is difficult to mitigate because its long wavelengths require impractically large conventional resonators. Here, we report an active-learning-guided inverse-design approach for scalable phononic-crystal-based acoustic metamaterial resonators that simultaneously suppress low-frequency noise transmission and harvest acoustic energy. The approach combines Gaussian process regression surrogate modeling with genetic algorithm optimization to efficiently explore high-dimensional cavity geometries. By iteratively retraining the surrogate with FEM-validated designs, the active-learning process guides the search toward high-performance structures while reducing costly FEM evaluations compared with conventional GA optimization. After geometric scaling, the 2.5D prototype derived from the nine-point optimized cavity achieved a pressure amplification factor of approximately 20 near 490 Hz, while the revolved 3D cavity exhibited amplification exceeding 30 and a transmission loss of approximately 14 dB near the target frequency. Integrated with a mass-loaded five-PZT stack, the device generated 5.5 V_pp and 0.25 mW under 100 dB SPL, corresponding to a normalized power density of 0.58 μW Pa⁻² cm⁻³. These results demonstrate a route toward multifunctional piezoelectric acoustic devices for noise mitigation, localized energy harvesting, and self-powered sensing. Full article

Dust accumulation on photovoltaic (PV) modules reduces power generation efficiency, and traditional water-based cleaning is impractical in arid regions. Inspired by the classical acoustic phenomenon of Chladni figures—specifically the mechanism where an acoustic standing wave field drives the regular migration and accumulation of particles—this study proposes a waterless dust removal method using low-frequency ultrasonic vibration via piezoelectric excitation. Impedance analysis identifies optimal electromechanical coupling at 28 kHz. Experiments demonstrate that higher driving voltages accelerate cleaning, with recovery rates saturating beyond 125 V. Notably, intense friction and collisions between particles within high-density dust layers consume substantial kinetic energy, significantly multiplying the required cleaning time. Macroscopic transport analysis reveals that dust removal relies on the synergy of vibration-induced adhesion decoupling and gravity-driven transport. Sufficient tangential gravity is crucial for macroscopic particle removal, and tilt angles above 30° provide the necessary downward driving force to ensure smooth particle sliding. Under optimal conditions, the system achieves an over 97% short-circuit current recovery at a low power consumption of ~10 W, providing a theoretical basis for waterless PV self-cleaning systems. Full article