Light-responsive shape memory polymer composites

Nature-Inspired Intelligent Cotton Fabrics with Excellent Shape Memory and Superhydrophobic Properties

Driven by technological advancements and rising living standards, the demand for high-performance cotton textiles continues to grow. Drawing inspiration from the stimuli-responsive behavior of Mimosa pudica and the inherent superhydrophobicity of lotus leaf surfaces, this study presents the development of a new class of smart cotton fabrics integrating superhydrophobicity, shape memory functionality, and wear resistance. The engineered smart cotton fabrics incorporate Eucommia ulmoides gum (EUG) and surface-tailored sepiolite particles as core functional elements. Central to this work is an innovative surface modulation strategy leveraging shape memory effects to dynamically control material hydrophobicity through thermoresponsive structural reconfiguration. Fabricated via a scalable dip-coating technique, these composites achieve tunable wettability without fluorine-based chemicals, marking a departure from conventional approaches. The innovation of this manuscript also lies in the cotton fabric's fluorine-free composition and its eco-friendly preparation process. These characteristics enable cotton fabrics to adjust their surface wettability based on different usage environments and needs, offering vast possibilities for creating and designing intelligent products.

Stress-Free Two-Way Shape Memory Polymers with Dual-Crystalline Phase Based on Poly(Tetramethylene Ether Glycol) and Poly(ε-Caprolactone)

Two-way shape memory polymers (2W-SMPs) are a class of smart materials and can undergo spontaneously reversible deformation after specific stimuli. It is crucial to develop 2W-SMPs to achieve precise control of two-way shape memory behavior without external forces and reveal their structure-property relationships. In this study, dual-crystalline phase crosslinked polymer networks based on poly(tetramethylene ether glycol) (PTMEG) and poly(ε-caprolactone) (PCL) are fabricated via thiol-ene click reactions. The networks with two independent melting temperatures are gained by adjusting the ratio of the two segments and the two-way shape memory is enabled using the temperature difference between the two phases. The effects of network composition, pre-tensile strain, and actuation temperature on the two-way shape memory properties are investigated and the two-way shape memory mechanism of dual-crystalline phase polymers is further elucidated. Among the various compositions of networks, PTMEG8-PCL2 exhibits the optimal two-way shape memory properties, with the actuation strain of 24.25% and reversible strain of up to 10.35% at the actuation temperature and pre-stretch strain of 45 °C and 15%, respectively, which is potential for soft robotics applications. It is believed that this work guides the design of semicrystalline networks with two-way shape memory properties.

Photo-responsive organogelator based on cholesterol incorporated sugar-azobenzene derivatives

In this report, the design and synthesis of cholesterol-based sugar azobenzene derivatives as photo-responsive organogelators have been carried out. The gel formation in different solvents was examined, and a minimum CGC of 0.5 % (w/v) was attained in toluene. The mechanism of self-assembly of organogels was characterized by using 1H NMR, UV-Vis, FT-IR, and PXRD. These organogelators show photo-isomerization and photo-reversible properties, which were studied using absorption spectra. Computational studies studied the optimized dimer structures and geometries of the trans and cis isomers. The prominent nature of this designed structure makes it an outstanding, intelligent soft material with plausible applications.

Efficient Preparation of Core-Shell Particles via Photopolymerization for Toughening Photocurable Resins

Core-shell particles have been widely used in improving the toughness of resin, but a convenient and efficient large-scale preparation method for core-shell particles is lacking currently. In this work, an efficient and simple method has been developed to fabricate core-shell particles by combining photopolymerization and phase separation inside the emulsion droplet. In this strategy, two photocurable prepolymers with different hydrophilicity were dissolved in a cosolvent and dispersed in an aqueous solution to form an emulsion. Upon the removal of the cosolvent, phase separation occurred inside the emulsion droplet. The hydrophilic prepolymer migrated toward the oil-water interface and converted to a shell under UV irradiation, while the hydrophobic prepolymer migrated to the interior of the emulsion droplet and converted to a core. In addition, the core-shell ratio and size of the particle could be easily adjusted, which provided an idea for the efficient and large-scale preparation of the core-shell particle. The influence of the structure and contents of the core-shell particles on the toughening effect was investigated. Under the optimal parameters (the core-shell ratio of 1:1, the size of 1 μm, and the concentration of 7.5 wt %), the toughness, impact strength, flexural strength, and fracture toughness (KIC) of the composites were enhanced by 52, 8.4, 30.8, and 288.4% compared to the pure resin system, respectively. Furthermore, the Tg value and the viscosity of the resin system were not greatly influenced after the addition of core-shell particles, i.e., an outstanding toughening effect was achieved without sacrificing its thermal property and processability.

The application of machine learning in 3D/4D printed stimuli-responsive hydrogels

The integration of machine learning (ML) in materials fabrication has seen significant advancements in recent scientific innovations, particularly in the realm of 3D/4D printing. ML algorithms are crucial in optimizing the selection, design, functionalization, and high-throughput manufacturing of materials. Meanwhile, 3D/4D printing with responsive material components has increased the vast design flexibility for printed hydrogel composite materials with stimuli responsiveness. This review focuses on the significant developments in using ML in 3D/4D printing to create hydrogel composites that respond to stimuli. It discusses the molecular designs, theoretical calculations, and simulations underpinning these materials and explores the prospects of such technologies and materials. This innovative technological advancement will offer new design and fabrication opportunities in biosensors, mechatronics, flexible electronics, wearable devices, and intelligent biomedical devices. It also provides advantages such as rapid prototyping, cost-effectiveness, and minimal material wastage.

Smart polymers: key to targeted therapeutic interventions

Smart polymers represent a class of advanced materials that undergo reversible changes in their physical or chemical form and are known as responsive polymers. These polymers show transitions when external stimuli, such as temperature and pH, come into play. Smart polymers are being increasingly applied in various fields, such as drug delivery to a targeted site and gene therapy. They also play a pivotal role in tissue engineering, environmental sensors, and the development of shape memory polymers. Despite their major challenges, they remain effective in overcoming significant barriers. It can be said that these polymers have the potential to revolutionize various fields. This review highlights the underlying types and applications of smart polymers, emphasizing their roles in the future.

Evaluation of Shape Recovery Performance of Shape Memory Polymers with Carbon-Based Fillers

This study focuses on enhancing the thermal properties and shape recovery performance of shape memory polymers (SMPs) through the application of carbon-based fillers. Single and mixed fillers were used to investigate their effects on the glass transition temperature (Tg), thermal conductivity, and shape recovery performance. The interaction among the three-dimensional (3D) structures of mixed fillers played a crucial role in enhancing the properties of the SMP. These interactions facilitated efficient heat transfer pathways and conserved strain energy. The application of mixed fillers resulted in substantial improvements, demonstrating a remarkable 290.37% increase in thermal conductivity for SMPCs containing 60 μm carbon fiber (CF) 10 wt% + graphite 20 wt% and a 60.99% reduction in shape recovery time for SMPCs containing CF 2.5 wt% + graphite 2.5 wt%. At a content of 15 wt%, a higher graphite content compared to CF improved the thermal conductivity by 37.42% and reduced the shape recovery time by 6.98%. The findings demonstrate that the application of mixed fillers, especially those with high graphite content, is effective in improving the thermal properties and shape recovery performance of SMPs. By using mixed fillers with high graphite content, the performance of the SMP showed significant improvement in situations where fast response times were required.

Development of a magnified sunlight responsive shape memory bio-composite: effects of titanium nitride (TiN) nanoparticles on a bio-based benzoxazine/epoxy copolymer

This study uniquely explored the effects of loading titanium nitride (TiN) nanoparticles in a bio-based benzoxazine/epoxy copolymer on the shape memory performance of the resulting composite using normal and magnified sunlight irradiation stimuli scenarios. Additionally, the effects of loading the TiN nanoparticles in the copolymer on light absorbance capacity, thermal stability, visco-elastic properties, and tensile properties of the composites were analysed. Results reveal that the different loading amounts (1 to 7 wt%) of TiN dispersed well within the copolymer matrix and produced excellent composite samples (TiN-1(wt%), TiN-3(wt%), TiN-5(wt%), and TiN-7(wt%)). Interestingly, the obtained samples were found to exhibit improved light absorbance in the wavelength range of 200-900 nm, giving the samples greater sunlight absorbing capacity. Moreover, the thermal stability of the composites increases with an increase in the loading amount; for instance, the initial degradation temperature increased from 316 °C to 324 °C. Meanwhile, visco-elastic and tensile properties increased and reached the optimum for TiN-5(wt%), where 3.1 GPa and 10.4 MPa were recorded as storage modulus and tensile stress, respectively. Consequent to these improvements in the properties of the composites, the shape memory performance of the composites was positively impacted. For instance, average shape fixity ratio, shape recovery ratio, and recovery time of 95%, 96%, and 38 seconds, respectively, were achieved with TiN-7(wt%), which represents 19%, 17%, and 38% improvements, respectively, compared to when the neat copolymer (TiN-0(wt%)) was used using magnified sunlight irradiation stimulus. Overall, this finding provides the basis for the utilization of magnified sunlight irradiation stimulus to achieve excellent shape memory performance with TiN-filled polymer composites.

AI-Driven Data Analysis of Quantifying Environmental Impact and Efficiency of Shape Memory Polymers

This research investigates the environmental sustainability and biomedical applications of shape memory polymers (SMPs), focusing on their integration into 4D printing technologies. The objectives include comparing the carbon footprint, embodied energy, and water consumption of SMPs with traditional materials such as metals and conventional polymers and evaluating their potential in medical implants, drug delivery systems, and tissue engineering. The methodology involves a comprehensive literature review and AI-driven data analysis to provide robust, scalable insights into the environmental and functional performance of SMPs. Thermomechanical modeling, phase transformation kinetics, and heat transfer analyses are employed to understand the behavior of SMPs under various conditions. Significant findings reveal that SMPs exhibit considerably lower environmental impacts than traditional materials, reducing greenhouse gas emissions by approximately 40%, water consumption by 30%, and embodied energy by 25%. These polymers also demonstrate superior functionality and adaptability in biomedical applications due to their ability to change shape in response to external stimuli. The study concludes that SMPs are promising sustainable alternatives for biomedical applications, offering enhanced patient outcomes and reduced environmental footprints. Integrating SMPs into 4D printing technologies is poised to revolutionize healthcare manufacturing processes and product life cycles, promoting sustainable and efficient medical practices.

Advancements in Soft Robotics: A Comprehensive Review on Actuation Methods, Materials, and Applications

The flexibility and adaptability of soft robots enable them to perform various tasks in changing environments, such as flower picking, fruit harvesting, in vivo targeted treatment, and information feedback. However, these fulfilled functions are discrepant, based on the varied working environments, driving methods, and materials. To further understand the working principle and research emphasis of soft robots, this paper summarized the current research status of soft robots from the aspects of actuating methods (e.g., humidity, temperature, PH, electricity, pressure, magnetic field, light, biological, and hybrid drive), materials (like hydrogels, shape-memory materials, and other flexible materials) and application areas (camouflage, medical devices, electrical equipment, and grippers, etc.). Finally, we provided some opinions on the technical difficulties and challenges of soft robots to comprehensively comprehend soft robots, lucubrate their applications, and improve the quality of our lives.

Shape-memory microfluidic chips for fluid and droplet manipulation

Fluid manipulation is an important foundation of microfluidic technology. Various methods and devices have been developed for fluid control, such as electrowetting-on-dielectric-based digital microfluidic platforms, microfluidic pumps, and pneumatic valves. These devices enable precise manipulation of small volumes of fluids. However, their complexity and high cost limit the commercialization and widespread adoption of microfluidic technology. Shape memory polymers as smart materials can adjust their shape in response to external stimuli. By integrating shape memory polymers into microfluidic chips, new possibilities for expanding the application areas of microfluidic technology emerge. These shape memory polymers can serve as actuators or regulators to drive or control fluid flow in microfluidic systems, offering innovative approaches for fluid manipulation. Due to their unique properties, shape memory polymers provide a new solution for the construction of intelligent and automated microfluidic systems. Shape memory microfluidic chips are expected to be one of the future directions in the development of microfluidic technology. This article offers a summary of recent research achievements in the field of shape memory microfluidic chips for fluid and droplet manipulation and provides insights into the future development direction of shape memory microfluidic devices.

Photoinduced deformation behavior of poly(aryl ether)s with different azobenzene groups in the side chain

Azobenzene-containing poly(aryl ether)s are a potential type of photoinduced deformable high-performance polymer. However, research on photoinduced deformation of azobenzene-containing poly(aryl ether)s focuses mainly on poly(aryl ether)s containing azobenzene groups in the main chain. In this paper, the photoinduced deformation of poly(aryl ether)s containing azobenzene groups in the side chain was studied for the first time. Two novel poly(aryl ether)s containing azobenzene groups in the side chain were synthesized, and their photoinduced isomerization behavior and photoinduced deformation behavior were studied. It could be seen that the match of the excitation luminescence to the maximum absorption peak of the azobenzene groups was more compatible, and the photoinduced motion of the polymers was faster. In addition, poly(aryl ether)s containing azobenzene groups in the side chain showed highly stable photoinduced deformation. The results of this work will be helpful for designing polymers which could be controlled by lasers of different wavelengths.

Emerging Functional Polymer Composites for Tactile Sensing

In recent years, extensive research has been conducted on the development of high-performance flexible tactile sensors, pursuing the next generation of highly intelligent electronics with diverse potential applications in self-powered wearable sensors, human-machine interactions, electronic skin, and soft robotics. Among the most promising materials that have emerged in this context are functional polymer composites (FPCs), which exhibit exceptional mechanical and electrical properties, enabling them to be excellent candidates for tactile sensors. Herein, this review provides a comprehensive overview of recent advances in FPCs-based tactile sensors, including the fundamental principle, the necessary property parameter, the unique device structure, and the fabrication process of different types of tactile sensors. Examples of FPCs are elaborated with a focus on miniaturization, self-healing, self-cleaning, integration, biodegradation, and neural control. Furthermore, the applications of FPC-based tactile sensors in tactile perception, human-machine interaction, and healthcare are further described. Finally, the existing limitations and technical challenges for FPCs-based tactile sensors are briefly discussed, offering potential avenues for the development of electronic products.

4D printing light-/thermo-responsive shape memory composites based on thermoplastic polyurethane/polylactic acid/polyaniline blends

In this work, a series of polylactic acid/thermoplastic polyurethane/polyaniline (PLA/TPU/PANI) blends with different weight ratios were prepared by Fused deposition molding First, six groups of PLA/TPU (U9A1/U8A2/U7A3/U6A4/U5A5/U4A6) and three groups of PLA/TPU/PANI (821/823/825) with different ratios were fabricated by melt blending. Then, the effects of different filament forming and printing process parameters on print resolution and quality were investigated. Next, printed samples were characterized by Fourier transform infrared (FTIR), Thermogravimetric analysis (TGA), Scanning electron microscopy (SEM) and mechanical experiments. The results of FTIR and TGA showed no chemical reaction between different components, and uniform distribution of the material was observed in the SEM. The tensile and compressive curves of the samples showed an inverted U-shape. Finally, the shape-memory property was evaluated by differential scanning calorimetry. For PLA/TPU blends, U8A2 had the best shape memory capability ([Formula: see text] = 80.8% and [Formula: see text] = 100%). Based on the excellent shape memory performance of PLA/TPU, the addition of PANI can introduce a light-actuated mechanism to form a binary-driven shape memory material. The composite materials prepared in this work can be applied to tissue engineering scaffolds, medical devices, soft robots and so on.