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Abstract Leveraging robot-assisted technology to manipulate tiny objects has shown significant potential in the fields of engineering, chemistry, and biology. However, achieving high-resolution, non-invasive manipulation of objects shielded by biological barriers remains a major challenge. In this work, we present a robot-assisted acoustic vortex end effector system capable of generating acoustic vortex beams for contactless manipulation of small objects. First, instead of generating a fixed acoustic vortex beam, our acoustic end effector can tune the chirality of the vortex beam by adjusting the topological charge number encoded in the holographic lens, allowing for customization of the size of the corresponding potential well to accommodate various sizes of trapped particle. Second, by leveraging acoustic vortex beams as a non-invasive manipulator, we successfully achieved acoustic manipulation through biomimetic barriers. In a proof-of-concept experiment, we demonstrated the high-resolution contactless acoustic manipulation of a plastic ball (3 mm diameter) within a straight phantom mimic-vessel. Third, by combining the acoustic vortex end effector with a real time ultrasound imaging system, our approach enables continuous, real-time monitoring of the entire acoustic manipulation process. This integration paves the way for acoustic trapping and manipulation in non-transparent environments. Overall, our research demonstrates the advantages of acoustic manipulation technologies in biomedical and clinical applications, offering a biocompatible solution for medical interventions in the future. 

Abstract Ultrasonics structural health monitoring (SHM) is widely recognized as an effective technique that enables early damage detection in large-scale structures and helps prevent potential catastrophic failures. Ultrasonic phased array technology has gained prominence in SHM due to its ability to inspect a large area with high spatial resolution. However, conventional systems often rely on physical wired sensor networks, limiting their deployment for hard-to-access regions. In this study, we present a wireless ultrasonic phased array system capable of dual-mode operation for both wall thickness measurement and structural damage detection. The system integrates wireless power transfer (WPT) modules and customized matching circuits, enabling efficient and flexible deployment. Proof-of-concept experiments demonstrate successful wall thickness evaluation and accurate defect localization in metallic structures using both delay-and-sum (DAS) and minimum variance (MV) imaging methods, with the MV algorithm offering improved imaging resolution. Future work will focus on advancing real-time monitoring through machine learning, enabling 3D imaging, and extending system applicability to anisotropic composite materials. 

Abstract The development of smart materials capable of dynamic shape morphing and rapid responsiveness has garnered significant interest for applications in soft robotics, tissue engineering, programmable materials, and adaptive structures. Hydrogels, owing to their intrinsic biocompatibility and flexibility, are promising candidates for such systems. Embedding micro-scale materials within hydrogel networks can further enhance their mechanical and functional properties. In this study, we present a hybrid fabrication platform that integrates surface acoustic wave (SAW)-based acoustofluidics with digital light processing (DLP) photopolymerization to fabricate smart hydrogel composites with programmable shape-memorable behavior. Using the SAW-induced acoustic potential field, silicon carbide (SiC) micro-whiskers are aligned within a custom UV-curable hydrogel ink and subsequently fixed via high-resolution DLP photopolymerization. This dual-control approach enables independent manipulation of micro-whisker orientation and structural geometry. Numerical simulations and Laser Doppler vibrometry-based validation were employed to characterize the acoustic field. To evaluate shape-memory behavior, the fabricated hydrogels were subjected to dehydration and rehydration cycles. The resulting shape transformations, driven by internal stress gradients within the aligned microparticle framework, enabled humidity-responsive actuation. This work establishes a novel strategy for constructing 4D-printed smart hydrogels, offering a versatile platform for the development of next-generation programmable materials and adaptive structures. 

Abstract Precise manipulation of nanomaterials has shown great potential in facilitating the fabrication of functional hydrogel nanocomposites in applications such as soft robotics, biomedicine, structural health monitoring, and wearable sensing. Surface acoustic wave (SAW)-based acoustofluidics offers a contactless approach for nanoparticle manipulation. Meanwhile, digital light processing (DLP) has been extensively utilized in the hydrogel printing process due to its high-resolution fabrication capabilities. This study presents an innovative SAW acoustofluidics-assisted DLP system, enabling the patterning of nanoparticles embedded in matrix materials while facilitating programmed light exposure for the controllable photopolymerization of customized hydrogel nanocomposites. Instead of utilizing the acoustic potential field generated by SAWs, we leverage the accompanying electric field due to the piezoelectric effect of the lithium niobate (LiNbO3) substrate to generate electric field, enabling the electric field-driven patterning of multi-walled carbon nanotubes (MWCNTs) Laser Doppler vibrometry confirms the SAW-generated acoustic intensity fields. The analytical simulation together with the scanned data predicted the co-current electric field predicted the distribution of MWCNTs. By applying a programmed light pattern, we successfully fabricated hydrogel nanocomposites in the shape of a VT logo and produced hydrogel nanocomposite sensors. The capabilities of printed hydrogel nanocomposite sensors were demonstrated through beam vibration sensing, proving its potential application in structural health monitoring. The fabricated sensors demonstrated the capability to track finger movements, indicating their potential for wearable sensing applications. In summary, this study offers a unique approach for nanocomposites fabricating multi-material integration and material anisotropy control, thereby facilitating advanced smart material development. Future work will focus on exploring the fabrication of hydrogels containing other types of nanomaterials to enhance material conductivity and achieve other functions, aiming with the goal of developing nanocomposite sensors for applications in soft robotics, biomedicine, structural health monitoring, and wearable sensing. 

Abstract The advancement of additive manufacturing has significantly transformed the production process of metal components. However, the unique challenges associated with layer-by-layer manufacturing result in anisotropy in the microstructure and uneven mechanical properties of additive-manufactured metal products. Traditional testing methods often fall short of providing the precise mechanical performance evaluations required to meet industry standards. This paper introduces an innovative approach that combines a nondestructive Lamb wave sensing system with a wavenumber analysis method to characterize the mechanical properties of 3D-printed metal panels in multiple directions. Our method employs piezoelectric actuators (PZT) to generate Lamb waves and utilizes a laser Doppler vibrometer (LDV) for non-contact, two-dimensional grid acquisition of the wavefield. The anisotropic properties of the metal 3D-printed structure will be captured in the wavefield, offering an informative dataset for wavenumber analysis. The proposed analytical method includes multi-directional frequency wavenumber analysis and a least-squares-based dispersion curves regression. The integration of the above advanced analytical tools allows for the accurate characterization of the shear wave velocity and Poisson’s ratio of the plate structure. This precise characterization is crucial for ensuring the structural integrity and consistent mechanical properties of 3D-printed metal components. We validated our method using a 3D-printed stainless-steel plate, demonstrating its capability to effectively characterize the multi-directional mechanical properties of additively manufactured metal plates. We expect that our method can provide a nondestructive, time-efficient, and comprehensive quality control solution for additive manufacturing across various industries. 

The graphic illustrates how an external acoustic field stabilizes the SEI layer by enhancing lithium-ion mass transfer at slip lines and kinks, reducing pit formation and promoting a more uniform SEI, ultimately improving battery performance. 

Abstract Thermoset composites, utilized in additive manufacturing, are distinguished by their excellent thermal and mechanical properties, enabling them to maintain structural integrity even under high-temperature conditions. An accurate method for characterizing the mechanical properties is necessary to ensure the performance parameters, reliability, and safety of materials during and post-manufacturing. However, characterizing 3D-printed thermoset composites is challenging due to the anisotropy introduced by the additive manufacturing process and factors such as delamination and porosity. This also leads to difficulties in accurately characterizing composites with traditional testing methods. To address this, this paper introduces a novel method that combines a non-destructive Piezoelectric transducer-laser Doppler Vibrometer (PZT-LDV) guided wave sensing system with an optimization algorithm-enhanced wavenumber analysis technique. A series of experiments were conducted to validate the concept of measuring the mechanical properties of a 3D-printed thermoset material panel. Our method successfully determined two material properties — shear wave speed and Poisson’s ratio in multiple directions on the test panel. This study aims to establish a precise and rapid non-destructive testing method that can effectively characterize various composite materials and monitor their performance throughout the additive manufacturing process. 

Abstract Thermoset materials have begun to be applied in additive composite manufacturing due to their ability to withstand high temperatures without losing structural integrity. Meanwhile, the characterization of mechanical properties for additively manufactured composites is critical for ensuring material reliability and safety. However, traditional testing methods struggle to accurately and nondestructively characterize additively manufactured composites due to challenges posed by curing processes, microstructural variability, anisotropic properties of thermoset composites, and the risk of damaging these materials during evaluation. For characterizing the mechanical properties of additive-manufactured thermoset composites, this paper presents a novel method that combines a nondestructive PZT-LDV guided wave sensing system and a wavenumber analysis that fuses multidimensional Fourier transform with dispersion curve regression. For proof of concept, we performed an experiment using our method to measure a 3D-printed thermoset composite panel. Based on our nondestructive approach, two material properties (shear wave velocity and Poisson’s ratio) in multiple directions were successfully determined for the tested panel. We expect this research to introduce a non-contact and efficient method for characterizing various composites and monitoring their property changes after additive manufacturing.