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Abstract Shape memory polymer (SMP) systems exhibiting semicrystalline- elastomer blends, such as thermoplastic polyurethane and polylactic acid have been well studied, but their use in biomedical shape memory applications has been limited by their high activation temperature. SMPs are capable of deformation and recovery through the activation of an external stimuli, such as temperature. Critical criteria for SMPs used in biomedical applications is achieving a stimulus temperature close to 37 °C while still experiencing sufficient shape recovery. A polymer’s glass transition temperature has been well defined as the SMP system’s activation temperature and therefore should be decreased to achieve a decreased activation temperature. In this work, a well-known, biocompatible plasticizer, polyethylene oxide (PEO), was added to thermoplastic polyurethane (TPU)—polylactic acid (PLA) SMP blends to observe the plasticizing effect on the structural, thermal, mechanical, and shape memory properties of the polymer blends. Additionally, the geometry of the fabricated SMP samples was tailored to further enhance the shape memory effect through a bowtie honeycomb structure. Our results suggest that the addition of PEO into the SMP system may be an effective method for decreasing the polymer’s glass transition temperature through the alteration of the polymer chain structure. With the addition of 30% PEO, the glass transition temperature of the TPU/PLA blend was successfully decreased from 62.4 °C to 34.6 °C while achieving 86.5% shape recovery when activated at 37 °C, which is only a 5% decrease in shape recovery when activated at 50 °C. These results suggest that the addition of a biocompatible plasticizer may overcome the limitation of employing temperature activated SMP systems in biomedical applications, and enhances the potential of these materials for reconfigurable structures, energy dissipation systems, and structural health monitoring (SHM) in civil engineering applications.
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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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