Thermoset Shape Memory Polymers Research Articles

Hydrophilic and hydrophobic shape memory polymer networks in high-intensity focused ultrasound fields

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 of in vivo HIFU-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.

Effect of crosslinking fraction, hardener functionality and topological quality on stress recovery of thermoset shape memory polymers: a coarse-grained molecular dynamics study

We analyzed the effects of crosslinking fraction and number of functional sites per hardener molecule on the stress recovery and topology of thermoset shape memory polymers (TSMPs) via coarse-grained molecular dynamics simulations. After systematically varying the quality of the crosslinked network by manipulating the number of unique epoxies reacted with each hardener, we found that two fingerprints correlate well with stress recovery of TSMPs. These fingerprints are the fraction of epoxy molecules connected to two distinct hardener molecules, and the fraction of molecules that are part of the largest or main network in the system. Their product can be used as a topological score () to quantify the topological feature of the network. When analyzing stress recovery as a function of , we found a strong correlation between and recovery stress. Moreover, we observed that while a higher crosslinking fraction did frequently lead to a higher stress recovery, many exceptions existed. High functionality hardeners tend to exhibit higher stress recovery at similar , especially at high (>0.65) . These results suggest that increasing the number of functional sites per hardener molecule combined with improving the topology of the network with a method such as semi batch monomer addition can lead to an improvement in the stress recovery of TSMPs.

Simulation study of shape memory polymer networks formed by free radical polymerization

Chemically crosslinked thermoset shape memory polymers (TSMPs) can combine the strength required for load-carrying structures and devices with stimuli-responsive properties that can allow them to perform crack-closing or other multifunctional properties. In particular, UV-curable shape memory polymers can be used for 3D printing, and to enhance the design process, a molecular dynamics (MD) modeling framework was developed to study them. The MD simulations were used to calculate thermophysical, mechanical, and shape memory properties for glycerolate dimethacrylate after UV-curing to form a polymer network. Semiquantitative agreement between simulation and experiment was achieved for the different properties compared. By examining various conditions for network formation, a range of distinct cross-linking topologies were generated and their effects on properties were evaluated. In particular, it was found that the percentage of monomers with the maximum number of possible links with other monomers had a greater correlation with recovery stress than the number of monomers reacted overall. A new topological fingerprint, the average minimum pathway between monomers, was proposed and found to have the strongest correlation with recovery stress than all properties examined.

A multifunctional hybrid extrinsic–intrinsic self-healing laminated composites

Damage healing in fiber reinforced thermoset polymer composites has been generally divided into intrinsic healing by the polymer itself and extrinsic healing by incorporation of external healing agent. In this study, we propose to use a hybrid extrinsic-intrinsic self-healing strategy to heal delamination in laminated composite induced by low velocity impact. Especially, we propose to use an intrinsic self-healing thermoset vitrimer as an external healing agent, to heal delamination in laminated thermoset polymer composites. To this purpose, we designed and synthesized a new vitrimer, machined it into powders, and strategically sprayed a layer of vitrimer powders at the interface between the laminas during manufacturing. Also, a thermoset shape memory polymer with fire-proof property was used as the matrix. As a result, incorporation of about 3% by volume of vitrimer powders made the laminate exhibit multifunctionalities such as repeated delamination healing, excellent shape memory effect, improved toughness and impact tolerance, and decent fire-proof properties. In particular, the novel vitrimer powder imparted the laminate with first cycle and second cycle delamination healing efficiencies of 98.06% and 85.93%, respectively. The laminate also exhibited high recovery stress of 65.6 MPa. This multifunctional composite laminate has a great potential in various engineering applications, for example, actuators, robotics, deployable structures, and smart fire-proof structures.

Two-way thermo-responsive thermoset shape memory polymer based on benzoxazine/urethane alloys using as self-folding structures

A novel two-way shape memory polymers derived from benzoxazine/urethane alloys (poly(BA-a/PU)) are developed in this research. The effect of urethane contents acted as spring elastomer on dynamic mechanical properties and two-way shape memory performance of the alloys was investigated. The experimental test and numerical simulation were also used to study and predict the ability of the developed 2WSMPs. The results showed that shape fixity at room temperature (RT) of poly(BA-a/PU) was enhanced in the range of 80–97% with the addition of PU contents. Shape recovery values to first temporary shape at Tg+20 °C of the specimen were 79–93%, whereas, those to second temporary shape at RT were 82–95%. Furthermore, 2WSMP based on poly(BA-a/PU) was reversely recovered between both temporary shapes more than 7 cycles. The experimental and numerical results revealed that poly(BA-a/PU) exhibited high 2WSMP performance for using as smart materials in self-folding structures application.

Correlation between cyclic topology and shape memory properties of an amine-based thermoset shape memory polymer: a coarse-grained molecular dynamics study

Defects in crosslinked networks have a negative effect on mechanical and functional properties. In this study, an epoxy resin diglycidyl ether of bisphenol A crosslinked by a hardener 4,4-diaminodiphenyl methane with various cyclic topologies was simulated to find correlations between the mechanical/shape memory properties (i.e. glassy/rubbery elastic modulus, shape recovery ratio, and recovery stress) and cyclic topologies (i.e. number of total loops, number of defective loops (DLs), etc). The effect of cyclic topology on shape memory properties was more significant than its effect on mechanical properties, altering recovery stress by more than 25% on average. After analyzing several topological fingerprints such as total number of loops, number of DLs, and number of higher order loops, we found that the effect of cyclic topology on the mechanical/shape memory properties of the systems can be best understood by the fraction of hardeners reacted with four distinct epoxy molecules (tetra-distinctly-reacted (TDR) hardeners). By increasing the number of TDR hardeners, the network is closer to ideal, resulting in an increase in the number of higher order loops and a reduction in the number of DLs, which in turn leads to an increase in rubbery elastic modulus and shape recovery ratio to a lesser degree, but a substantial increase in recovery stress. These results suggest that utilization of experimental techniques such as semibatch monomer addition, which leads to a more expanded and defect-free network, can result in a simultaneous increase in both shape recovery ratio and recovery stress in thermoset shape memory polymers (TSMPs). Moreover, topology alteration can be used to synthesize TSMPs with improved recovery stress without significantly increasing their stiffness.

Thermoset shape memory polymer with permanent shape reconfigurability based on dynamic disulfide bonds

Thermoset shape memory polymer with permanent shape reconfigurability based on dynamic disulfide bonds

Effect of atomistic fingerprints on thermomechanical properties of epoxy-diamine thermoset shape memory polymers

Shape memory polymers (SMP) have been a field of interest for researchers over the past few decades and SMPs with unique characteristics are being developed continuously. Careful, time-consuming design is required for improved materials. Polymer informatics is part of an effort to shorten the development time of polymers by using computation and data-driven approaches such as machine learning. A specific polymer can be described via “fingerprints”, which are a way to characterize molecular structures in a manner understandable by computers. In this paper, we describe combinations of nine epoxies and twenty-two hardeners with twenty fingerprints, and simulated the thermomechanical cycle with molecular dynamics code LAMMPS. Subsequently, we statistically analyzed which fingerprints are most strongly correlated with each shape memory property, specifically recovery stress and shape recovery ratio. This study lays a solid foundation for choosing and understanding atomistic fingerprints in order to discover new SMPs via machine learning.

From Drug Molecules to Thermoset Shape Memory Polymers: A Machine Learning Approach.

Ultraviolet (UV)-curable thermoset shape memory polymers (TSMPs) with high recovery stress but mild glass transition temperature (Tg) are highly desired for 3D/4D printing lightweight load-bearing structures and devices. However, a bottleneck is that high recovery stress usually means high Tg. For a few TSMPs with high recovery stress, their Tg values are close to the decomposition temperature, and thus, the shape memory effect cannot be triggered safely and effectively. While machine learning (ML) has served as a useful tool to discover new materials and drugs, the grand challenge of using ML to discover new TSMPs persists in the very limited data available. Here, we report an enhanced ML approach by combining the transfer learning-variational autoencoder with a weighted-vector combination method. By learning a large data set with drug molecules in a pretraining process, we were able to effectively map the TSMPs to a hidden space that is much closer to a Gaussian distribution. Through this approach, we created a large compositional space and were able to discover five new types of UV-curable TSMPs with desired properties, one of which was validated by the experiments. Our contribution includes (1) representing the features of TSMPs by drug molecules to overcome the barrier of a limited training data set and (2) developing a ML framework that is able to overcome the barrier of mapping the molar ratio information. It is shown that this approach can effectively learn TSMP features by utilizing the relatedness between the data-scarce (and biased) TSMP target and data-abundant drug source, and the result is much more accurate and more robust than the benchmark set by the support vector machine method using direct label encoding and Morgan encoding. Therefore, it is believed that this framework is a state-of-the-art study in the TSMP field. This study opens new opportunities for discovering not only new TSMPs but also other thermoset polymers.

Evaluating sealability of blended smart polymer and fiber additive for geothermal drilling with the effect of fracture opening size

Geothermal formations often contain extensive fracture networks. These fracture networks contribute to the significant loss of drilling fluids during geothermal drilling. Multiple loss circulation materials (LCM) such as fiber, granules, and pills have been proposed to tackle this problem but with only limited success. Recent advances in materials science have led to the development of thermoset shape memory polymers (SMP) to address the lost circulation problem. In this paper, we evaluate a thermoset SMP performance in sealing near wellbore fractures of different sizes in geothermal wells. The SMP performance was assessed using granite disks and cylindrical granite cores having fracture sizes of 1000 μm and 3000 μm. A static filtration test was performed using cedar fiber, CaCO3, and SMP. Results showed cedar fiber performed better than the CaCO3., reducing fluid loss by 89% and improving sealing pressure by 200 psi. A novel dynamic testing unit that allows for high-temperature testing under flowing conditions was used in this study. The analysis showed that 3% by weight SMP and fiber blends could bridge and plug the 1000 μm fracture. For a larger fracture of 3000 μm width, there was a need to increase the weight concentration of the SMP to 6% to plug the fracture opening effectively. We showed the influence of key parameters such as the type of LCM, concentration, and particle size distribution in optimizing the performance of drilling fluid loss treatment.

Investigation of the Long‐Term Storage Stability of Shape Memory Epoxy Prepolymer

Epoxy‐based shape memory polymers (ESMPs) are important types of thermoset shape memory polymers due to their unique thermomechanical properties, which have great potential in applications ranging from smart actuators to deformable structures. The long‐term storage stability is important for ESMPs, especially when pursuing the same batch of materials for alternatives. Stable storage of ESMP preplomyers in the long term and taking it out for curing when needed are of great benefit to engineering applications which highly require the same batch of materials for high production efficiency, such as in deployable space structures, semiconductor, and electronic packaging. However, this important storage stability of ESMP has not been studied so far. Herein, a series of ESMPs with tunable glass transition temperatures (Tgs) ranging from 100 to 170 °C is prepared. The effect of long‐term storage at low temperatures on rheological, thermal, thermomechanical, mechanical, and shape memory properties is systematically investigated. The ESMP after storage shows an improvement of crosslinking density, Tgs, and mechanical strength, while maintaining chemical structures and excellent shape memory properties. This feasible method of low‐temperature storage realizes overall long‐term stability and enhancement of strength in ESMPs, making it more promising for engineering applications.

Machine learning assisted discovery of new thermoset shape memory polymers based on a small training dataset

In the past couple of years, machine learning (ML) has been widely leveraged in discovering functional materials. However, several difficulties seriously impede the application of ML in the field of thermoset shape memory polymers (TSMPs), e.g., the intractable feature identification or fingerprinting, inadequate experimental data on recovery stress, programming stress, strain, and lack of multilength scale structural information. Hence there is currently a lack of studies towards ML-assisted discovery of TSMPs. In this study, we propose a series of methodologies to cope with the difficulties, i.e., adopting the most recently proposed linear notation BigSMILES in fingerprinting, supplementing existing dataset by reasonable approximation, leveraging a mixed dimension (1D and 2D) input model, and a type of dual-convolutional-model framework. By doing these, a new ML framework for predicting the recovery stresses of TSMPs is developed, which is validated by synthesizing and testing two new epoxy networks predicted by the ML model. By forging new TSMPs space with 4,459 samples, the ML model identified and screened 14 mostly unknown TSMPs with greater recovery stress than the known TSMPs. One of the 14 predicted polymers was validated by molecular dynamics (MD) simulation. This study demonstrates the capability of our methodologies for discovering new TSMPs with desired recovery stress by a small training dataset, and may be adopted for discovering new TSMPs with other desired properties.

Quantifying the contributions of energy storage in a thermoset shape memory polymer with high stress recovery: A molecular dynamics study

Molecular dynamics simulations were carried out to understand the mechanical and energy storage properties of bisphenyl-A diglycidyl ether cured with isophorone diamine — a thermoset shape memory polymer (TSMP) with both excellent shape memory and stress memory properties. Different cross-linked systems were created to determine which cross-linking percentage gave best agreement with experimental properties. It was found that for a 50% compression programming strain, higher cross-linking percentages stored more bond energy, but at the expense of a lower shape recovery. While bond energy was stored in the change in bond stretches, angles, and dihedrals, the bond angular and dihedral energies stored were significantly higher than bond stretches. Alkyl cyclic rings were able to store the most angular energy, but little to no stretching or dihedral energy, while non-ring polymer backbone atoms stored a significant amount of stretching, angular, and dihedral energies. Aromatic rings stored dihedral energy, but little angular energy. The results show that the type of functional group has a significant impact on how bond energy is stored, and could potentially be used to finely tune the shape recovery properties of TSMPs.

Hot Embossing of Micro-Pyramids into Thermoset Thiol-Ene Film.

This paper presents the first attempt to texturize a fully crosslinked thermoset shape memory polymer using a hot embossing technique. UV-cured thiol-ene films were successfully embossed with anisotropically-etched Si (100) stamps at a temperature of 100 °C, which is about 50 °C above the glass transition temperature of the polymer. The low storage modulus of the polymer in a rubbery state allowed us to permanently emboss random micro-pyramidal patterns onto the surface of the film with high fidelity by applying 30 MPa pressure for 1 h. Atomic force microscopy (AFM) investigation showed perfect replication of the stamp micropattern with typical height of the largest inverted pyramids close to 0.7 µm and lateral dimensions in the range of 1–2 µm. Changes in surface roughness parameters of the embossed thiol-ene films after annealing them at 100 °C for 1 h or storing for 2 months in air at standard room conditions were negligible. The achieved results open new perspectives for the simple and inexpensive hot embossing technique to be applied for the micropatterning of prepolymerized thermoset shape memory films as an alternative to micropatterning using UV casting.

Loss circulation prevention in geothermal drilling by shape memory polymer

Geothermal formations are naturally fractured with large fracture openings and networks. This can often lead to frequent drilling fluid loss events which is a major contributor to the cost and non-productive time in geothermal drilling. Development of smarter technologies and methods to tackle problem lost circulation is vital for geothermal drilling cost reduction required for geothermal energy to be recognized as a competitive alternative energy source. Mitigation of this problem and other drilling problems such as stuck pipe can minimize overall project cost.In this paper, a thermoset shape memory polymer that can be activated by formation natural heat was assessed to seal near wellbore fractures in geothermal wells. The performance of the shape memory polymer (SMP) was evaluated using artificial fractures created in aluminum discs and cylindrical granite cores. Rheology and particle size distribution were considered. A novel testing setup was built for this work, which allows testing of sealing efficiency under dynamic conditions at high temperature. Analysis showed that the SMP has efficiently succeeded in forming a strong plug inside the fractures and stopped fluid loss at high sealing pressure. This smart loss circulation material can expand within the fractures to reduces non-drilling time and strengthen the wellbore in high-temperature drilling operations.

Effect of the addition of diurethane dimethacrylate on the chemical and mechanical properties of tBA-PEGDMA acrylate based shape memory polymer network

There is a great demand for the synthesis of acrylate based thermoset shape memory polymer (SMP) associated with one monomer and one crosslinker such as tert-butyl acrylate (t-BA) with poly (ethylene glycol) dimethacrylate (PEGDMA). The present work describes the synthesis of a new thermoset SMP wherein a second monomer such as diurethane dimethacrylate (DUDMA) has been added to the existing tBA + PEGDMA SMP matrix. The synthesized thermoset shape memory polymer exhibited a glass transition temperature (Tg) of 55 °C, higher Young's Modulus of 3.23 GPa, transmittance of 95% and 100% shape recovery. The SMP exhibited response to both thermal and chemical stimuli. The shape recovery rate of the SMP network is 20 s compared to 24 s observed for SMP based on tBA + PEGDMA. The obtained SMP is very transparent and possesses higher stiffness (8 MPa) and hence may be suitable for biomedical shape memory lens and orthopedic application.

NIR-light-induced thermoset shape memory polyurethane composites with self-healing and recyclable functionalities

To endow thermoset shape memory polymers with self-healing and recyclable functionalities is highly desirable. In this study, we fabricated Diels-Alder crosslinked polyurethane/functionalized reduced graphene oxide composites (DAPU-FRGOs) through in situ polymerization. The incorporated FRGO with urethane chains could achieve good compatibility with the DAPU matrix, forming physical and chemical crosslinking structures in the composite. And the most effective improvements of mechanical, thermal stability, crystallization-triggered, and photothermal effect were achieved for the DAPU/FRGO2 composite with 2 wt% FRGO. Owing to the outstanding crystallization-induced and photo-thermal effects of FRGO and the thermal reversible behavior of Diels-Alder bonds, the DAPU-FRGO2 composite showed rapid shape memory (<10 s, shape-fixity and recovery ratios were 92.3% and 94.1%, respectively) and self-healing (<120 s, healing efficiencies for tensile strength and elongation at break were 89.3% and 90.4%, respectively) under near-infrared light. Meanwhile, the DAPU-FRGO2 composite exhibited good repeatability of self-healing and shape-memory functionalities, and its shape memory parameters and healing efficiencies were maintained above 92% and 85% after three-cycles, respectively. Further recycling test demonstrated that the mechanical properties of the recycled DAPU-FRGO2 composite could be recovered to more than 82% of the original sample. This work provides a heuristic perspective for designing durable light-responsive thermoset shape memory composite materials.

A Mechanism-Based Four-Chain Constitutive Model for Enthalpy-Driven Thermoset Shape Memory Polymers With Finite Deformation

Abstract Chemically cross-linked thermoset shape memory polymers (TSMPs) are an important branch of smart materials due to their potentially wide applications in deplorable structures, soft robots, damage self-healing, and 4D printing. Further development and design of TSMP structures call for constitutive models. Although the Arruda–Boyce eight-chain model has been very successful and widely used for entropy-driven TSMPs, recent studies found that some new TSMPs, such as those using enthalpy as the primary driving force, show unit cells different from the eight-chain model. Considering that these new epoxy-based TSMP networks consist of a plenty of four-chain features, this study proposes a four-chain tetrahedron structure as the unit cell of the network to construct the constitutive model. In this model, Gibbs free energy is used to formulate the thermodynamic driving force. Then, by introducing a transition of the molecule deformation mechanism from that dominated by bond stretch to that dominated by bond angle opening, the traditional Langevin chain model is modified. It is found that this model can well capture the dramatic modulus change for the new TSMP in the thermomechanical experiments. Moreover, it shows that the original Treloar four-chain model and Arruda–Boyce eight-chain model underestimate the driving force for the enthalpy-driven TSMPs, and thus cannot well capture the thermomechanical behaviors. It is also found that under certain conditions, our four-chain model produces the same Cauchy stress as the eight-chain model does. This study may help researchers understand the thermomechanical response and design a special category of TSMPs with high recovery stress.

A reconfigurable, self-healing and near infrared light responsive thermoset shape memory polymer

Shape memory polymers and self-healing polymers are two types of smart materials, which have great application potential in various fields. In this study, a composite with light-induced shape memory effect, solid-state plasticity and self-healing performance was prepared by incorporating graphene oxide (GO) into a thermoset polyurethane. Based on the great photothermal effect of GO, this composite exhibited excellent light-induced shape memory properties with shape recovery ratio over 95% under irradiation of 1.4 W cm−2 near infrared (NIR) light. Moreover, the composite also possessed light-induced plasticity and light-induced self-healing capability under irradiation of 2.5 W cm−2 NIR light due to the photothermal conversion triggered carbamate exchange reaction in polymer networks. It can be repeatedly reconfigured to new permanent shapes through NIR light irradiating without sacrificing its shape memory properties and remotely controllable healing with fully recovery in structure accompanied by high mechanical properties up to 85% of original value. Based on these special properties, the practicability of this composite as light control actuators, self-healing coatings and optical welding materials was further investigated and the experimental data demonstrated that the composite was qualified for all the aforementioned applications.

Dual-Responsive Shape Memory and Thermally Reconfigurable Reduced Graphene Oxide-Vitrimer Composites

In this paper, we have prepared shape memory reduced graphene oxide-vitrimer composites via in situ reduction of graphene oxide (GO) during the curing reaction. Because of good compatibility between GO and epoxy resin, reduced graphene oxide (rGO) has a good dispersion in the epoxy matrix. Conventional thermoset shape memory polymers can only maintain one permanent shape, while these obtained composites can be randomly reconfigured into other shapes via dynamic covalent transesterification reaction at 200 °C above transesterification temperature (TV). They can recover quickly from fixed shapes to their initial shapes with shape fixity ratio higher than 95% and shape recovery ratio higher than 97%. Besides, the rGO-vitrimers show good mechanical properties and thermal stabilities. In addition, sequent near-infrared (NIR) irradiation can control the shape recovery, because rGO can serve as an energy convertor to convert NIR irradiation into thermal energy.