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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 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. 

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. 

Abstract Cell patterning techniques play a pivotal role in the development of three-dimensional (3D) engineered tissues, holding significant promise in regenerative medicine, drug screening, and disease research. Current techniques encompass various mechanisms, such as nanoscale topographic patterning, mechanical loading, chemical coating, 3D inkjet printing, electromagnetic fields, and acoustic waves. In this study, we introduce a unique standing bulk waves-based acoustic cell patterning device designed for constructing anisotropic-engineered glioma tissues containing acoustically patterned human glioblastoma cell U251. Our device features two orthogonal pairs of piezoelectric transducers securely mounted on a customized holder. The energy of standing bulk waves generated from these transducers can be transmitted into the medium in a Petri dish through its bottom wall. Cells in the medium can be directed to the local minima of Gor’kov potential fields and trapped by the resultant acoustic radiation force. Through proof-of-concept experiments, we validate the functionality of our acoustic patterning device and assess the morphology and differentiation of U251 cells within the engineered glioma tissues. Our findings reveal that cells can be arranged in different distributions, such as parallel-line-like and lattice-like patterns. Moreover, the aligned cells exhibit more obvious elongation along the cell alignment orientation compared to the result of a control group. We anticipate that this study will catalyze the advancement in contactless cell patterning technologies within tissue engineering, facilitating the development of engineered tissues for applications in regenerative medicine and disease research. 

Robotic manipulation of small objects has shown great potential for engineering, biology, and chemistry research. However, existing robotic platforms have difficulty in achieving contactless, high-resolution, 4-degrees-of-freedom (4-DOF) manipulation of small objects, and noninvasive maneuvering of objects in regions shielded by tissue and bone barriers. Here, we present chirality-tunable acoustic vortex tweezers that can tune acoustic vortex chirality, transmit through biological barriers, trap single micro- to millimeter-sized objects, and control object rotation. Assisted by programmable robots, our acoustic systems further enable contactless, high-resolution translation of single objects. Our systems were demonstrated by tuning acoustic vortex chirality, controlling object rotation, and translating objects along arbitrary-shaped paths. Moreover, we used our systems to trap single objects in regions with tissue and skull barriers and translate an object inside a Y-shaped channel of a thick biomimetic phantom. In addition, we showed the function of ultrasound imaging–assisted acoustic manipulation by monitoring acoustic object manipulation via live ultrasound imaging. 

Anisotropic collagen-based biomaterials have gained significant attention in the fields of tissue engineering and regenerative medicine. They have shown great potential for wound dressing, corneal grafting, and exploring the mechanism of cancer cell invasion. Various external physical field-based methods for the fabrication of anisotropic collagen-based biomaterials have been developed, including electrospinning, microfluidic shearing, mechanical loading, and so on. In this study, we put forward an acoustic streaming-based method that uses acoustic wave-induced fluid streaming to control collagen self-assembly and fiber arrangement. Our acoustic device leverages a piezoelectric transducer to generate traveling acoustic waves in fluids, and the wave-fluid interaction further induces fluid streaming, known as acoustic streaming. If the fluid contains collagen macromolecules, the acoustic streaming is able to affect the collagen self-assembly process to create biomaterials containing directionally arranged collagen fibers along the streaming velocity direction. Therefore, this acoustic streaming-based method allows for manufacturing collagen hydrogel layers that contain acoustically arranged collagen fibers and have controlled anisotropic material properties. We performed a series of proof-of-concept experiments by using a fabricated acoustic device to control the self-assembly process of collagens loaded in a Petri dish. Our results show the effectiveness of arranging collagen fibers that follow the flow direction of acoustic streaming. To better understand the collagen manipulation mechanism, we used particle image velocimetry to characterize the acoustic wave-induced fluid streaming. We expect this study can contribute to the fabrication of collagen-based anisotropic biomaterials for biomedical applications. 

aser Doppler vibrometry and wavefield analysis have recently shown great potential for nondestructive evaluation, structural health monitoring, and studying wave physics. However, there are limited studies on these approaches for viscoelastic soft materials, especially, very few studies on the laser Doppler vibrometer (LDV)-based acquisition of time–space wavefields of dispersive shear waves in viscoelastic materials and the analysis of these wavefields for characterizing shear wave dispersion and evaluating local viscoelastic property distributions. Therefore, this research focuses on developing a piezo stack-LDV system and shear wave time–space wavefield analysis methods for enabling the functions of characterizing the shear wave dispersion and the distributions of local viscoelastic material properties. Our system leverages a piezo stack to generate shear waves in viscoelastic materials and an LDV to acquire time–space wavefields. We introduced space-frequency-wavenumber analysis and least square regression-based dispersion comparison to analyze shear wave time–space wavefields and offer functions including extracting shear wave dispersion relations from wavefields and characterizing the spatial distributions of local wavenumbers and viscoelastic properties (e.g., shear elasticity and viscosity). Proof-of-concept experiments were performed using a synthetic gelatin phantom. The results show that our system can successfully generate shear waves and acquire time–space wavefields. They also prove that our wavefield analysis methods can reveal the shear wave dispersion relation and show the spatial distributions of local wavenumbers and viscoelastic properties. We expect this research to benefit engineering and biomedical research communities and inspire researchers interested in developing shear wave-based technologies for characterizing viscoelastic materials.