Deep focusing on the role of microstructures in shape memory properties of polymer composites: A critical review

Shape-Memory Polymers Based on Carbon Nanotube Composites

For the past two decades, researchers have been exploring the potential benefits of combining shape-memory polymers (SMP) with carbon nanotubes (CNT). By incorporating CNT as reinforcement in SMP, they have aimed to enhance the mechanical properties and improve shape fixity. However, the remarkable intrinsic properties of CNT have also opened up new paths for actuation mechanisms, including electro- and photo-thermal responses. This opens up possibilities for developing soft actuators that could lead to technological advancements in areas such as tissue engineering and soft robotics. SMP/CNT composites offer numerous advantages, including fast actuation, remote control, performance in challenging environments, complex shape deformations, and multifunctionality. This review provides an in-depth overview of the research conducted over the past few years on the production of SMP/CNT composites with both thermoset and thermoplastic matrices, with a focus on the unique contributions of CNT to the nanocomposite's response to external stimuli.

New Approach to Shape Memory Polymer Composite Production Using Alkaline Lignin-Reinforced Epoxy-Based Shape Memory Polymers

In the past few decades, there has been continued interest in shape memory polymers (SMPs), and tremendous efforts have been made to develop multifunctional composites of these SMPs to enhance the existing properties of SMPs. Although fossil-based sources are widely used in the production of shape memory polymer composites (SMPCs), the depletion of fossil-based resources and associated environmental problems increase interest toward renewable biobased products synthesized from natural resources. This study aims to produce alkaline lignin-reinforced SMPCs by using alkaline lignin in the SMP matrix. Thermo-mechanical, morphological, and shape memory tests are performed in order to reveal the effect of alkaline lignin usage in the SMP matrix on SMPC production. Differential scanning calorimetry analysis results show that adding alkaline lignin into the SMP matrix with 1 and 3% ratios led to an increase in T _g values, while raising the alkaline lignin ratio to 5% decreased the T _g value. According to the DMA results, increasing the alkaline lignin ratios caused an increase in the storage modulus of SMPCs, and the best storage modulus value was obtained at the 5% alkaline lignin ratio. The results of the three-point bending test also confirmed the results obtained from the DMA analysis, showing that an increasing alkaline lignin ratio caused an increase in the bending modulus. Scanning electron microscopy analysis showed a rough structure in 1 and 3% alkaline lignin supplementation, while a smoother structure was observed in 5% alkaline lignin supplementation. The smoother structure of the sample containing 5% alkaline lignin indicates that alkaline lignin supplementation exhibits a smoother surface by showing a plasticizing effect. As a result, it was observed that increasing the lignin ratio increased the polymer/alkaline lignin interaction, resulting in a harder structure and an increase in the flexural modulus value.

Preparation of PLA-TPU-Nanoclay composites and characterization of their morphological, mechanical, and shape memory properties

In this study, a combination of polylactic acid polymer and thermoplastic polyurethane with the addition of nano-clay particles was used. The reason for using clay nanoparticles and their strength is the low price and availability of this material. Adding nano-clay particles to the polymer composition improves the mechanical properties of the composite as they will interact with functional groups of the polymer. The results of the FTIR spectroscopy confirmed the presence of three components in the compound indicating that no chemical reactions occurred among the three components during the compounding process. The FE-SEM images taken from the compounds showed that TPU and nano-clay particles were evenly distributed in the PLA matrix. The DMTA results were utilized to determine the transfer temperature of the compounds as well as the storage and loss modulus and the shape memory properties. The XRD spectroscopy was used to determine the crystallinity and exfoliation of the nanoparticles. The mechanical properties of the fabricated polymer compounds were determined. It was found that the sample with 3% by weight of clay nanoparticles had the highest strength, and the sample with 5 wt% of clay nanoparticles had the highest toughness among nanocomposites. According to the hardness measurement, the sample with 5% by weight of clay nanoparticles has the highest hardness amongst all prepared composites. The memory properties of the prepared nanocomposites showed a significant improvement with increasing the amount of nanoparticles. This study showed the suitability and efficiency of PLA, TPU, and clay nanoparticle melt mixing methods in achieving a relatively tough shape memory composite. At the same time, this method is also inexpensive and scalable.

Green Copolymers and Nanocomposites from Myrcene and Limonene Using Algerian Nano-Clay as Nano-Reinforcing Filler

In this work, we report a new facile method for the preparation of myrcene-limonene copolymers and nanocomposites using a Lewis acid as a catalyst (AlCl₃) and organo-modified clay as a nano-reinforcing filler. The copolymer (myr-co-lim) was prepared by cationic copolymerization using AlCl₃ as a catalyst. The structure of the obtained copolymer is studied and confirmed by Fourier Transform Infrared spectroscopy, Nuclear Magnetic Resonance spectroscopy, and Differential Scanning Calorimetry. By improving the dispersion of the matrix polymer in sheets of the organoclay, Maghnite-CTA⁺ (Mag-CTA⁺), an Algerian natural organophilic clay, was used to preparenanocomposites of linear copolymer (myr-co-lim). In order to identify and assess their structural, morphological, and thermal properties, the effect of the organoclay, used in varyingamounts (1, 4, 7, and 10% by weight), and the preparation process were investigated. The Mag-CTA⁺ is an organophylic montmorillonite silicate clay prepared through a direct exchange process in which they were used as green nano-reinforcing filler. The X-ray diffraction of the resulting nanocomposites revealed a considerable alteration in the interlayer spacing of Mag-CTA⁺. As a result, interlayer expansion and myr-co-lim exfoliation between layers of Mag-CTA⁺ were observed. Thermogravimetric analysis provided information on the synthesized nanocomposites' thermal properties. Fourier transform infrared spectroscopy and scanning electronic microscopy, respectively, were used to determine the structure and morphology of the produced nanocomposites (myr-co-lim/Mag). The intercalation of myr-co-lim in the Mag-CTA⁺ sheets has been supported by the results, and the optimum amount of organoclay needed to create a nanocomposite with high thermal stability is 10% by weight. Finally, a new method for the preparation of copolymer and nanocomposites from myrcene and limonene in a short reaction time was developed.

Machine Learning for Shape Memory Graphene Nanoribbons and Applications in Biomedical Engineering

Shape memory materials have been playing an important role in a wide range of bioengineering applications. At the same time, recent developments of graphene-based nanostructures, such as nanoribbons, have demonstrated that, due to the unique properties of graphene, they can manifest superior electronic, thermal, mechanical, and optical characteristics ideally suited for their potential usage for the next generation of diagnostic devices, drug delivery systems, and other biomedical applications. One of the most intriguing parts of these new developments lies in the fact that certain types of such graphene nanoribbons can exhibit shape memory effects. In this paper, we apply machine learning tools to build an interatomic potential from DFT calculations for highly ordered graphene oxide nanoribbons, a material that had demonstrated shape memory effects with a recovery strain up to 14.5% for 2D layers. The graphene oxide layer can shrink to a metastable phase with lower constant lattice through the application of an electric field, and returns to the initial phase through an external mechanical force. The deformation leads to an electronic rearrangement and induces magnetization around the oxygen atoms. DFT calculations show no magnetization for sufficiently narrow nanoribbons, while the machine learning model can predict the suppression of the metastable phase for the same narrower nanoribbons. We can improve the prediction accuracy by analyzing only the evolution of the metastable phase, where no magnetization is found according to DFT calculations. The model developed here allows also us to study the evolution of the phases for wider nanoribbons, that would be computationally inaccessible through a pure DFT approach. Moreover, we extend our analysis to realistic systems that include vacancies and boron or nitrogen impurities at the oxygen atomic positions. Finally, we provide a brief overview of the current and potential applications of the materials exhibiting shape memory effects in bioengineering and biomedical fields, focusing on data-driven approaches with machine learning interatomic potentials.

Shape Memory Materials from Rubbers

Smart materials are much discussed in the current research scenario. The shape memory effect is one of the most fascinating occurrences in smart materials, both in terms of the phenomenon and its applications. Many metal alloys and polymers exhibit the shape memory effect (SME). Shape memory properties of elastomers, such as rubbers, polyurethanes, and other elastomers, are discussed in depth in this paper. The theory, factors impacting, and key uses of SME elastomers are all covered in this article. SME has been observed in a variety of elastomers and composites. Shape fixity and recovery rate are normally analysed through thermomechanical cycle studies to understand the effectiveness of SMEs. Polymer properties such as chain length, and the inclusion of fillers, such as clays, nanoparticles, and second phase polymers, will have a direct influence on the shape memory effect. The article discusses these aspects in a simple and concise manner.

Smart TPE Materials Based on Recycled Rubber Shred

Thermo-responsive shape memory materials were developed based on recycled ethylene-propylene-diene (EPDM) rubber shred and thermoplastic elastomers (TPE). Ethylene-1-octene TPEs (Engage 8180, 8411, 8452) with varying degrees of crystallinity and Mooney viscosity were used to prepare the composite materials. To avoid the deterioration of static mechanical properties after mixing recycled EPDM rubber shred (RS) with thermoplastic elastomers, they were partially cured using dicumyl peroxide. The peroxide curing was the most effective for a rubber shred/Engage 8180 blend, where the highest cure rate index (CRI), 1.88 dNm⋅min⁻¹, was observed. The curing caused an approximately 4-fold increase of tensile strength (TS) values for EPDM rubber shred/thermoplastic elastomer blend to the level acceptable for the rubber industry compared with an uncured blend. The incorporation of EPDM rubber shred changed thermoplastic elastomers’ viscoelastic behavior, increasing the values of storage (G′) and loss (G″) modulus. The lowest viscosity of molten Engage 8411 during mixing led to higher compatibility of rubber shred RS/8411 blend, as confirmed by analysis of Cole-Cole plots and the blend morphology. All rubber shred RS/TPE blends showed the shape memory behavior. For the RS/Engage 8452 blend, the highest shape fixity (F) value (94%) was observed, while the shape recovery (RR) was 87%. Studies confirmed that the intelligent materials with shape memory effect could be obtained via selectively chosen thermoplastic elastomers; ethylene-1-octene as a binder for recycled EPDM. Prepared recycled TPE/rubber shred blends can be successfully reused due to their viscoelastic and mechanical properties. Therefore, such a concept can be potentially interesting for the rubber industry.

Elucidating the Role of Interfacial Hydrogen Bonds on Glass Transition Temperature Change in a Poly(Vinyl Alcohol)/SiO2 Polymer-Nanocomposite by Noncovalent Interaction Characterization and Atomistic Molecular Dynamics Simulations

A thorough experimental investigation of polymer-glass transition temperature (T_g ) is performed on poly(vinyl alcohol) (PVA) and fumed silica nanoparticle (SiNP) composite. This is done together with atomistic molecular dynamics simulations of PVA systems in contact with bare and fully hydroxylated silica. Experimentally, PVA-SiNP composites are prepared by simple solution casting from aqueous solutions followed by its characterization using Fourier-transform infrared spectroscopy (FTIR), dynamic mechanical analysis (DMA), and dynamic scanning calorimetry (DSC). Both theoretical and experimentally deduced T_g are correlated with the presence of hydrogen bonding interactions involving OH functionality present on the surface of SiNP and along PVA polymer backbone. Further deconvolution of FTIR data show that inter-molecular hydrogen bonding present between PVA and SiNP surface is directly responsible for the increase in T_g . SiNP filler and PVA matrix ratio is also optimized for a desired T_g increase. An optimal loading of SiNP exists, in order to yield the maximum T_g increase arising from the competition between hydrogen bonding and crowding effect of SiNP.