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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.
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This content will become publicly available on December 25, 2025
Machine Learning‐Driven Discovery of Thermoset Shape Memory Polymers With High Glass Transition Temperature Using Variational Autoencoders
ABSTRACT The discovery of high‐performance shape memory polymers (SMPs) with enhanced glass transition temperatures (Tg) is of paramount importance in fields such as geothermal energy, oil and gas, aerospace, and other high‐temperature applications, where materials are required to exhibit shape memory effect at extremely high‐temperature conditions. Here, we employ a novel machine learning framework that integrates transfer learning with variational autoencoders (VAE) to efficiently explore the chemical design space of SMPs and identify new candidates with high Tg values. We systematically investigate the effect of different latent space dimensions on the VAE model performance. Several machine learning models are then trained to predict Tg. We find that the SVM model demonstrates the highest predictive accuracy, withR2values exceeding 0.87 and a mean absolute percentage error as low as 6.43% on the test set. Through systematic molar ratio adjustments and VAE‐based fingerprinting, we discover novel SMP candidates with Tg values between 190°C and 200°C, suitable for high‐temperature applications. These findings underscore the effectiveness of combining VAEs and SVM for SMP discovery, offering a scalable and efficient method for identifying polymers with tailored thermal properties.
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- Award ID(s):
- 2418415
- PAR ID:
- 10562061
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Journal of Polymer Science
- Volume:
- 63
- Issue:
- 5
- ISSN:
- 2642-4150
- Format(s):
- Medium: X Size: p. 1095-1107
- Size(s):
- p. 1095-1107
- Sponsoring Org:
- National Science Foundation
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