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Abstract Focused ultrasound (FUS) presents unique advantages for noninvasive localized heating, crucial for controlled shape recovery in shape memory polymers (SMPs), especially in biomedical applications. To enhance FUS-driven actuation efficiency, we propose boron nitride (BN)-infused SMP composites (SMPCs) tailored for targeted biomedical interventions. Using tert-butyl acrylate (tBA) and di(ethylene glycol) dimethacrylate as base materials, we integrated BN fillers at varying concentrations (1, 5, and 10 wt.%). A thorough characterization was carried out, including dynamic mechanical analysis, scanning electron microscopy, uniaxial tensile testing, and swelling study. These results show that increasing the BN content improves shape recovery efficiency significantly. Specifically, the 10 wt.% BN composites outperformed plain SMP in terms of shape recovery ratio when activated with FUS, and the highest shape recovery ratio can achieve 75%. However, higher BN content decreases crosslinking density and stiffness, as shown by a lower Young’s modulus and glass transition temperature. This study demonstrates the promise of BN-infused SMPCs for advanced applications in biomedical application, where noninvasive spatiotemporal actuation of SMPs is required. 

ABSTRACT In therapeutic focused ultrasound (FUS), such as thermal ablation and hyperthermia, effective acousto‐thermal manipulation requires precise targeting of complex geometries, sound wave propagation through irregular structures, and selective focusing at specific depths. Acoustic holographic lenses (AHLs) provide a distinctive capability to shape acoustic fields into precise, complex, and multifocal FUS‐thermal patterns. Acknowledging the under‐explored potential of AHLs in shaping ultrasound‐induced heating patterns, this study introduces a roadmap for acousto‐thermal modeling in the design of AHLs. Three primary modeling approaches are studied and contrasted using four distinct shape groups for the imposed target field. They include pressure‐based time reversal (TR) (basic (BSC‐TR) and iterative (ITER‐TR)), temperature‐based (inverse heat transfer optimization (IHTO‐TR)), and machine learning (ML)‐based (generative adversarial network (GaN) and GaN with feature (Feat‐GAN)) methods. Novel metrics, including image quality, thermal efficiency, thermal control, and computational time, are introduced, providing each method's strengths and weaknesses. The importance of evaluating target pattern complexity, thermal and pressure requirements, and computational resources is highlighted. As a further step, two case studies: (1) transcranial FUS and (2) liver hyperthermia, demonstrate the practical use of acoustic holography in therapeutic settings. This paper offers a practical reference for selecting modeling approaches based on therapeutic goals and modeling requirements. Alongside established methods like BSC‐TR and ITER‐TR, new techniques IHTO‐TR, GaN, and Feat‐GaN are introduced. BSC‐TR serves as a baseline, while ITER‐TR enables refinement based on target shape characteristics. IHTO‐TR supports thermal control, GaN offers rapid solutions under fixed conditions, and Feat‐GaN provides adaptability across varying application settings. 

Abstract Acoustic holographic lenses (AHLs) show great potential as a straightforward, inexpensive, and reliable method of sound manipulation. These lenses store the phase and amplitude profile of the desired wavefront when illuminated by a single acoustic source to reconstruct ultrasound pressure fields, induce localized heating, and achieve temporal and spatial thermal effects in acousto-thermal materials like polymers. The ultrasonic energy is transmitted and focused by AHL from a transducer into a particular focal volume. It is then converted to heat by internal friction in the polymer chains, causing the temperature of the polymer to rise at the focus locations while having little to no effect elsewhere. This one-of-a-kind capability is made possible by the development of AHLs to make use of the translation of attenuated pressure fields into programmable heat patterns. However, the impact of acousto-thermal dynamics on the generation of AHLs is largely unexplored. We use a machine learning-assisted single inverse problem approach for rapid and efficient AHLs’ design to generate thermal patterns. The process involves the conversion of thermal information into a holographic representation through the utilization of two latent functions: pressure phase and amplitude. Experimental verification is performed for pressure and thermal measurements. The volumetric acousto-thermal analyses of experimental samples are performed to offer a knowledge of the obtained pattern dynamics, as well as the applicability of holographic thermal mapping for precise volumetric temperature control. Finally, the proposed framework aims to provide a solid foundation for volumetric analysis of acousto-thermal patterns within thick samples and for assessing thermal changes with outer surface measurements. 

Abstract High-intensity focused ultrasound (HIFU) has been investigated as a remote and controlled activation method to noninvasively actuate shape memory polymers (SMPs), specifically in biomedical applications. However, the effects of aqueous environment on shape recoverability ofin vivoHIFU-actuated SMPs have yet to be explored. HIFU directs sound waves into a millimeter-sized tightly focused region. In this study, the response of hydrophilic and hydrophobic photopolymerized thermoset SMP networks under HIFU activation in an aqueous environment was investigated. Acrylate-based SMP networks were copolymerized in specific ratios to produce networks with independently adjusted glass transition temperatures ranging from 40 to 80 °C and two distinct water uptake behaviors. The results link the polymer swelling behavior to shape recoverability in various acoustic fields. The presence of absorbed water molecules enhances the performance of SMPs in terms of their shape memory capabilities when activated by HIFU. Overall, understanding the interplay between water uptake and HIFU-actuated shape recovery is essential for optimizing the performance of SMPs in aqueous environments and advancing their use in various medical applications. 

Fibrous shape memory polymers (SMPs) have received growing interest in various applications, especially in biomedical applications, which offer new structures at the microscopic level and the potential of enhanced shape deformation of SMPs. In this paper, we report on the development and investigation of the properties of acrylate-based shape memory polymer fibers, fabricated by electrospinning technology with the addition of polystyrene (PS). Fibers with different diameters are manufactured using four different PS solution concentrations (25, 30, 35, and 40 wt%) and three flow rates (1.0, 2.5, and 5.0 μL min −1 ) with a 25 kV applied voltage and 17 cm electrospinning distance. Scanning electron microscope (SEM) images reveal that the average fiber diameter varies with polymer concentration and flow rates, ranging from 0.655 ± 0.376 to 4.975 ± 1.634 μm. Dynamic mechanical analysis (DMA) and stress–strain testing present that the glass transition temperature and tensile values are affected by fiber diameter distribution. The cyclic bending test directly proves that the electrospun SMP fiber webs are able to fully recover; additionally, the recovery speed is also affected by fiber diameter. With the combination of the SMP material and electrospinning technology, this work paves the way in designing and optimizing future SMP fibers properties by adjusting the fiber diameter. 

Abstract Locomotion is a critically important topic for soft actuators and robotics, however, the locomotion applications based on two-way shape memory polymers (SMPs) have not been well explored so far. In this work, a crosslinked poly(ethylene-co-vinyl acetate) (cPEVA)-based two-way SMP is synthesized using dicumyl peroxide (DCP) as the crosslinker. The influence of the DCP concentration on the mechanical properties and the two-way shape memory properties is systematically studied. A Venus flytrap-inspired soft actuator is made by cPEVA, and it is shown that the actuator can efficiently perform gripping movements, indicating that the resultant cPEVA SMP is capable of producing large output force and recovering from large deformations. This polymer is also utilized to make a self-rolling pentagon-shaped device. It is shown that the structure will efficiently roll on a hot surface, proving the applicability of the material in making sophisticated actuators. With introducing an energy barrier, jumping can be accomplished when the stored energy is fast released. Finite element simulations are also conducted to further understand the underlying mechanisms in the complex behavior of actuators based on cPEVA SMP. This work provides critical insights in designing smart materials with external stimulus responsive programmable function for soft actuator applications.