Biomimetic Smart Materials

Version	Summary	Created by	Modification	Content Size	Created at	Operation
1		Antreas Kantaros	--	5463	2024-01-18 08:14:13	\|
2	references update and layout	Fanny Huang	Meta information modification	5463	2024-01-22 06:36:33	\|

This entry is adapted from the peer-reviewed paper 10.3390/biomimetics9010048

Biomimicry, rooted in emulating nature’s sophisticated solutions, serves as the foundational framework for developing materials endowed with remarkable characteristics, including adaptability, responsiveness, and self-transformation. These advanced engineered biomimetic materials, featuring attributes such as shape memory and self-healing properties, undergo rigorous synthesis and characterization procedures, with the overarching goal of seamless integration into the field of additive manufacturing. The resulting synergy between advanced manufacturing techniques and nature-inspired materials promises to revolutionize the production of objects capable of dynamic responses to environmental stimuli. Extending beyond the confines of laboratory experimentation, these self-transforming objects hold significant potential across diverse industries, showcasing innovative applications with profound implications for object design and fabrication. Through the reduction of waste generation, minimization of energy consumption, and the reduction of environmental footprint, the integration of biomaterials, biopolymers, and additive manufacturing signifies a pivotal step towards fostering ecologically conscious design and manufacturing practices. Within this context, inanimate three-dimensional objects will possess the ability to transcend their static nature and emerge as dynamic entities capable of evolution, self-repair, and adaptive responses in harmony with their surroundings. The confluence of biomimicry and additive manufacturing techniques establishes a seminal precedent for a profound reconfiguration of contemporary approaches to design, manufacturing, and ecological stewardship, thereby decisively shaping a more resilient and innovative global milieu.

biomaterials biomimetic materials smart materials

1. Introduction

The technology of 3D (three-dimensional) printing has revolutionized manufacturing and design, enabling the creation of complex and customized objects with ease. Building upon this transformative technology, the emergence of 4D (four-dimensional) printing has taken additive manufacturing to the next level ^[1]. Unlike traditional static 3D printing, 4D printing introduces the dimension of time, allowing objects to transform their shape, properties, or functionality over time in response to external stimuli. This groundbreaking concept has opened up new frontiers in engineering, materials science, and robotics, offering unprecedented opportunities for innovation and application ^[2].

At its core, 4D printing encompasses the integration of smart materials that can undergo controlled and programmed shape changes or property alterations when triggered by specific stimuli. These stimuli can range from temperature variations, humidity, light, or even mechanical forces. The materials used in 4D printing possess the remarkable ability to respond to these external cues, initiating a transformation process that leads to dynamic and adaptive behavior ^[3]. Temporal integration in 4D printing introduces the dimension of time, enabling dynamic transformations and shape changes in printed objects over time in response to various stimuli like temperature, light, or moisture. This evolution significantly expands production possibilities compared to static 4D printing. Benefits of temporal integration in 4D printing include increased complexity and functionality, adaptive and responsive properties, and enhanced customization as well as functional evolution ^[3]. Static 4D printing involves objects that have predefined, fixed transformations or shape changes. Temporal integration surpasses this by introducing a time-based element, allowing for continuous or triggered changes. While static 4D printing is remarkable, temporal integration elevates its capabilities by offering more dynamic and adaptive structures ^[3].

The fundamental principle behind 4D printing lies in the precise design and fabrication of smart materials with the desired shape-changing properties. Biomimetic smart materials utilized in 4D printing showcase dynamic adaptability, responding to diverse external cues in a way akin to natural responsiveness. These materials react to stimuli like temperature shifts, humidity fluctuations, pH changes, light exposure, or specific chemicals ^[4]. Triggered by these factors, they undergo alterations, resulting in shape modifications, expansions, contractions, or property changes. By emulating biological responsiveness to environmental cues, these biomimetic materials enable adaptable and dynamic behaviors in 4D printing, fostering applications across fields such as medicine, robotics, and architecture.

Smart material selection stands as a critical determinant for achieving desired shape-changing characteristics in 4D printing. The choice of smart materials significantly influences the object’s responsiveness to stimuli and its ability to undergo precise transformations over time. Factors such as the material’s inherent properties, including shape memory, responsiveness to specific triggers (like temperature, light, or moisture), durability, and compatibility with the printing process, profoundly impact the efficacy of shape changes ^[4]. Moreover, considerations regarding the intended application and environmental conditions further shape the selection process, emphasizing the pivotal role of smart material choice in determining the success and functionality of 4D-printed objects.

Notably, the development of shape-shifting materials has revolutionized the landscape of 4D printing. One prominent example is the utilization of smart materials, such as hydrogels and shape memory polymers, which can transform their shapes in response to external stimuli like temperature, light, or moisture ^[4]. Researchers have successfully demonstrated the creation of intricate structures that can self-assemble or morph into predetermined shapes, showcasing the potential for applications in various fields, including biomedical devices, aerospace components, and flexible electronics. Additionally, the integration of multi-material and multi-scale 3D printing techniques has led to the production of complex, functional, and customizable products, from light-weight, high-performance automotive parts to intricate, patient-specific medical implants, underscoring the versatility and potential of additive manufacturing in modern technology ^[5].

These materials can be classified into two main categories: shape memory materials and stimuli-responsive materials ^[6]^[7]. Shape memory materials have the capacity to “remember” a specific shape and return to it when activated by an external stimulus. Stimuli-responsive materials, on the other hand, can undergo reversible changes in their properties or shape when exposed to specific triggers ^[8]^[9]. To realize the full potential of 4D printing, a multidisciplinary approach is essential. Researchers and engineers draw from fields such as materials science, mechanical engineering, computer science, and design to develop innovative strategies for material selection, design optimization, and fabrication techniques ^[10]^[11]. Computer modeling plays a pivotal role in forecasting and optimizing 4D-printed structures by simulating and predicting their dynamic behaviors and shape changes over time ^[12]. Through advanced computational algorithms, these models can simulate the response of materials to different stimuli, allowing for the prediction of structural transformations. By analyzing various parameters such as material properties, environmental conditions, and design intricacies, computer models can optimize the printing process, predict how structures will morph or adapt, and fine-tune designs to regulate and control specific shape changes. This enables a more precise and efficient creation of 4D-printed objects with tailored and regulated dynamic behaviors ^[13].

Biomimetic smart materials epitomize a scientific endeavor rooted in nature’s teachings. These materials, shaped by a profound understanding and emulation of the intricate architectures, adaptive behaviors, and responsive mechanisms inherent in diverse biological systems, span the vast spectrum from macroscopic entities like plants and animals to the microscopic domains of microbes and cellular structures ^[14]. This scientific pursuit involves an exhaustive exploration of nature’s evolutionarily refined strategies developed over billions of years. Scientists aim to decipher and replicate these exceptional material properties and functionalities previously beyond the scope of conventional methodologies. By meticulously studying nature’s design principles, biomimetic smart materials pave the way for innovations that not only imitate but also surpass the efficiency and adaptability ingrained in natural systems ^[15].

One of the fundamental objectives driving the development of biomimetic smart materials lies in emulating nature’s remarkable adaptability and responsiveness to environmental stimuli ^[16]. Across various biological systems, there exists a pervasive capacity to sense and dynamically react to alterations in temperature, humidity, light exposure, pressure, and chemical cues ^[17]. Integrating these inherent responsive capabilities into synthetic materials represents a scientific pursuit aimed at creating innovative materials capable of real-time adaptation, morphing, or self-healing, mirroring the intricate mechanisms observed in living organisms ^[18]. This scientific pursuit involves a multidisciplinary approach, encompassing materials science, bioengineering, and nanotechnology, among other fields, to replicate and harness the dynamic responsiveness witnessed in biological systems. Researchers meticulously study and replicate these natural mechanisms, seeking to imbue synthetic materials with similar adaptive properties for applications from biomedical devices to advanced engineering and beyond.

The potential applications of biomimetic smart materials are vast and diverse. From engineering to medicine, robotics to architecture, these materials hold promise for enhancing the performance, sustainability, and functionality of various systems ^[19]^[20]^[21]^[22]. They offer new opportunities for designing advanced prosthetics, developing self-repairing infrastructure, creating adaptive textiles, fabricating responsive sensors, and revolutionizing energy harvesting and storage ^[23]^[24]^[25]^[26]^[27]. In example, the pioneering work of Voronkina et.al. ^[28] discusses the intricate structure of the Aphrocallistes beatrix spong while looking into the fundamental principles of bioarchitecture, aligning with the biomimetic framework advocated in the broader discourse. The study’s findings, showcasing the positioning of actin filaments within a biosilica-based honeycomb structure, underscore the potential for biomimetic material design ^[28]. This empirical evidence not only supports the synthesis of biomimetic models through advanced manufacturing techniques like 3D printing but also hints at specialized genetic mechanisms guiding silicate biosynthesis in unique marine environments. Incorporating these insights into the discourse surrounding biomaterials and contemporary manufacturing enhances the paradigm shift towards ecologically conscious design and production practices, ultimately fostering innovative applications that echo nature’s solutions and drive sustainability in object design and fabrication.

Several biomimetic smart materials have emerged as crucial building blocks in the fields of biomimicry and 4D printing ^[29]. Shape memory alloys (SMAs) are among the most important materials, known for their ability to “remember” and recover their original shape when subjected to certain stimuli ^[30]^[31]. They find applications in various fields, including aerospace, robotics, and medicine ^[32]^[33]. Another vital material is shape memory polymers (SMPs), which can undergo significant deformation and recover their original shape when triggered by specific stimuli. SMPs are particularly valuable in biomedical applications, such as tissue engineering and drug delivery systems. Additionally, hydrogels, inspired by the water-absorbing properties of biological tissues, have garnered significant attention ^[34]. These soft, water-swollen materials exhibit remarkable responsiveness to external stimuli, making them ideal candidates for applications in soft robotics, biomedical devices, and sensors ^[35]. Moreover, electroactive polymers (EAPs) imitate the electrical signaling properties of biological systems, enabling them to undergo shape changes or actuation when an electric field is applied. EAPs have tremendous potential in fields such as artificial muscles, haptic interfaces, and biomimetic sensors. These biomimetic smart materials stand at the forefront of research and development, driving innovations and shaping the future of adaptive and responsive technologies ^[36]^[37]^[38].

Biomimetic smart materials represent a pivotal advancement in materials science, with sustainability at their core ^[39]. By drawing inspiration from nature’s adaptive and self-regulating mechanisms, these materials offer a promising avenue for sustainable innovation. One of their most compelling sustainability aspects lies in their ability to reduce waste and energy consumption. Through self-transformation and adaptability, these materials can optimize their performance in response to changing conditions, thus minimizing the need for constant replacements or interventions ^[40]. This inherent durability and efficiency align with the principles of a circular economy, where resources are conserved, and environmental impacts are reduced. Additionally, the development of biomimetic smart materials promotes responsible sourcing and production practices, fostering a more environmentally conscious approach to material design. As such, these materials not only offer exciting possibilities for novel applications but also play a significant role in shaping a more sustainable and eco-friendly future ^[41].

Biomimetic smart materials stand at the forefront of technological advancement, embodying a multifaceted approach that harmonizes seamlessly with several of the key Sustainable Development Goals (SDGs) set forth by the United Nations ^[42]. By harnessing nature-inspired design principles and cutting-edge technology, these materials play a pivotal role in driving innovation and sustainability across various domains ^[43]^[44]. More specifically, as far as “SDG 9—Industry, Innovation, and Infrastructure” is concerned, these materials epitomize innovation, offering groundbreaking solutions that revolutionize industry practices and infrastructure development. Their resource-efficient, self-regulating attributes not only enhance productivity but also contribute significantly to the overarching goal of fostering sustainable industry and infrastructure ^[45]^[46]. Also, as far as “SDG 11—Sustainable Cities and Communities” is concerned, in the field of urbanization, biomimetic materials have emerged as transformative agents. They empower the creation of adaptive, resilient urban structures that can thrive amidst evolving environmental challenges. These materials contribute substantially to the sustainable development of cities and the promotion of resilient communities ^[47]^[48]. In addition, in compliance with “SDG 12—Responsible Consumption and Production”, biomimetic materials are a characteristic example of responsible consumption and production. By extending the lifespan of products and minimizing waste, they embody sustainable manufacturing and consumption practices. Their innate resource optimization capabilities align closely with SDG 12’s objectives ^[49]. Regarding “SDG 13—Climate Action”, these materials play a pivotal role in the global endeavor to combat climate change. Through their ability to adapt to changing environmental conditions and reduce energy consumption, they contribute significantly to mitigating climate-related challenges and advancing climate action initiatives ^[50]. Regarding “SDG 14—Life Below Water” and “SDG 15—Life on Land”, biomimetic materials draw inspiration from the intricacies of ecosystems, thus aligning seamlessly with the preservation and sustainable management of terrestrial and aquatic environments. Their design principles are inherently linked to the goals of conserving biodiversity and maintaining ecological balance ^[51]^[52]^[53]. Lastly, bearing in mind “SDG 17—Partnerships for the Goals”, the development and implementation of biomimetic materials necessitate interdisciplinary collaboration and cooperation. Researchers, industries, governments, and stakeholders unite to pioneer these transformative technologies, thereby nurturing global partnerships and facilitating collective efforts aimed at realizing a sustainable future ^[54]. In summary, biomimetic smart materials exemplify a holistic and dynamic approach to addressing several pivotal SDGs. Their innovative character, sustainability-driven ethos, and capacity for responsible resource utilization make them instrumental in advancing the global mission for a more sustainable and equitable future, aligning impeccably with the comprehensive development agenda outlined by the United Nations.

Challenges associated with biomimetic smart materials for 4D printing arise from the complexity of replicating the intricate functionalities found in living organisms. One major challenge lies in achieving precise control over material properties and responses, ensuring that the desired shape changes or property alterations occur predictably and reliably. Developing materials with the necessary mechanical, thermal, and chemical properties while maintaining responsiveness to stimuli requires a deep understanding of material science and biology. Additionally, scalability and compatibility with existing 4D printing techniques pose challenges, as manufacturing processes need to be optimized for large-scale production without compromising the materials’ functionality. Moreover, long-term durability and stability of biomimetic smart materials remain critical concerns in ensuring their practical viability and longevity in real-world applications ^[55].

However, the challenges of biomimetic smart materials also present significant opportunities for innovation and advancement. The ability to replicate and harness the adaptability and self-regulating mechanisms of living organisms opens up new possi-bilities for engineering design. By leveraging biomimicry principles, researchers can develop materials that exhibit enhanced functionality, responsiveness, and resilience. These materials have the potential to revolutionize fields such as healthcare, robotics, architecture, and more. Biomimetic smart materials enable the creation of self-transforming objects with unprecedented capabilities, from shape-changing structures to programmable soft robots. Furthermore, they offer the prospect of sustainable and eco-friendly solutions by drawing inspiration from nature’s efficient and resource-conserving systems. The advancement of biomimetic intelligent materials within the realm of 4D printing not only affords avenues for scientific and technological progress but also aligns with the overarching objective of fostering enhanced adaptability and intelligence in our environment.

2. Biomimetic Smart Materials

2.1. Shape Memory Alloys (SMAs)

Shape memory alloys (SMAs) are a remarkable class of biomimetic smart materials that exhibit the ability to remember and recover their original shape after defor-mation when subjected to specific stimuli. These materials have gained significant attention due to their unique shape memory effect, which is derived from a reversible phase transformation between austenite and martensite crystal structures. This remarkable property allows SMAs to undergo substantial deformations and then revert to their original shape when triggered by changes in temperature, stress, or magnetic fields ^[56].

One of the most notable features of SMAs is their high capacity for recoverable strain, which can exceed several hundred percent. This exceptional property makes them ideal for applications requiring large actuation capabilities, such as in actuators, valves, and micro-robotic systems ^[57]. SMAs can deliver precise and controlled movements, enabling them to mimic the motion and functionality of biological systems. Their excellent me-chanical properties, including high strength, fatigue resistance, and good damping characteristics, contribute to their suitability for various engineering applications ^[58].

SMAs have wide-ranging applications across numerous fields. In the aerospace industry, SMAs are utilized in adaptive wing structures, morphing airfoils, and de-ployable space structures, where their ability to undergo shape changes in response to environmental conditions provides significant advantages in aerodynamic performance and structural optimization ^[59]^[60]^[61]. In the biomedical field, SMAs are used in orthopedic implants, stents, and dental braces due to their biocompatibility, corrosion resistance, and shape memory behavior, enabling minimally invasive procedures and enhanced patient comfort ^[62].

Regarding robotics applications, shape memory alloys (SMAs) emulate natural adaptation and mobility through their distinctive properties. With their remarkable shape memory effect and superelasticity, SMAs replicate the adaptive capabilities observed in nature. These alloys have the ability to revert to their original shape after deformation when triggered by specific stimuli like temperature changes, akin to how living organisms adapt to varying conditions ^[63]. Moreover, their superelastic nature allows SMAs to endure substantial reversible deformations without permanent damage, resembling the elasticity found in biological tissues. This enables robotic components to maintain structural integrity during agile movements, mirroring the resilience and mobility seen in natural organisms. SMAs’ responsiveness to external stimuli, such as temperature or mechanical stress, allows for real-time adjustments in shape and function, contributing to the adaptability and dynamic mobility required in robotics. By leveraging these properties, SMAs serve as efficient actuators, converting thermal or mechanical energy into motion, thereby facilitating the creation of agile and mobile robotic systems that parallel the adaptability and mobility observed in nature ^[64]. Figure 1 depicts the phase transformation process for SMAs.

Figure 1. Phase transformation process for SMAs.

While SMAs offer numerous advantages, challenges exist in their implementation. Designing precise control mechanisms and understanding the intricate thermo-mechanical behavior of these materials requires expertise and thorough characterization. Moreover, issues related to cost, fatigue life, and reliability in long-term applications necessitate ongoing research and development efforts. Nonetheless, with their exceptional shape memory effect and mechanical properties, SMAs continue to captivate researchers and engineers ^[65]. Their ability to emulate the adaptability and motion found in nature holds immense potential for creating advanced biomimetic systems in fields ranging from robotics and aerospace to medicine and beyond. As research progresses and understanding deepens, the applications of SMAs are expected to expand further, contributing to the development of innovative and intelligent technologies.

Among the various shape memory alloys (SMAs), nickel–titanium (Ni-Ti) alloys, commonly known as Nitinol, stand out as the most important and widely studied. Ni-tinol exhibits exceptional shape memory properties, high recoverable strain capacity, and excellent biocompatibility, making it a highly versatile and sought-after material ^[66]. Its unique characteristics, including a wide transformation temperature range, remarkable mechanical properties, and excellent corrosion resistance have led to its extensive use in diverse fields. Nitinol has applications in the aerospace industry for adaptive structures, in the medical field for stents and orthopedic implants, and in robotics for actuators and artificial muscles. The ability of Nitinol to undergo repeated and reversible shape changes with high precision and reliability has positioned it as a key material in the realm of biomimetic smart materials, driving advancements in various technological domains ^[66].

Another significant shape memory alloy (SMA) is copper–aluminum–nickel (Cu-Al-Ni). This ternary alloy exhibits remarkable shape memory behavior and mechanical properties that make it a valuable material for numerous applications. Cu-Al-Ni SMAs possess a two-way shape memory effect, allowing them to recover not only their original shape but also a secondary shape after deformation and subsequent heating or cooling cycles ^[67]. This property enables complex and multi-step shape transformations, expanding the range of potential applications. Cu-Al-Ni SMAs are particularly suited for precise actuation systems, such as in microelectromechanical systems (MEMs), where their excellent shape memory properties, low hysteresis, and high mechanical stability are advantageous. Additionally, the biocompatibility of Cu-Al-Ni SMAs has led to their utilization in medical devices, including orthodontic wires, endodontic instruments, and vascular stents. The unique combination of shape memory behavior, mechanical properties, and biocompatibility make Cu-Al-Ni SMAs an important material with diverse applications in fields ranging from aerospace and robotics to healthcare and beyond ^[67].

Another shape memory alloy (SMA) worth mentioning is iron-based SMAs, also known as ferrous-based SMAs ^[68]. Iron-based SMAs offer distinct advantages due to their low cost, abundance, and excellent mechanical properties. These alloys, typically based on iron, can exhibit shape memory and superelasticity, making them suitable for a wide range of applications. Iron-based SMAs have garnered attention in fields such as automotive engineering, where they are used in active suspension systems and crash energy absorbers. Their exceptional damping properties make them valuable for vibration control applications in civil engineering structures and mechanical systems. Furthermore, iron-based SMAs show promise in the biomedical field, with applications in orthodontic wires, surgical instruments, and implantable devices due to their compatibility with the human body. The availability, affordability, and favorable mechanical properties of iron-based SMAs make them an important material for various industrial sectors, contributing to the advancement of shape memory alloy technology. Table 1 lists the most common SMAs, their applications, and their most prominent features ^[69]. In the following tables (Table 1, Table 2 and Table 3), the specific criteria and metrics used to evaluate attributes such as strength, cost-effectiveness, and biocompatibility vary based on industry standards, research findings, or expert opinions in the fields of materials science or engineering. The assessments are relative rather than absolute, providing a comparative view of these attributes among different materials.

Table 1. Most common SMAs, their applications and their most prominent features.

SMA Type	Applications	Strength	Cost-Effectiveness	Biocompatibility
Nitinol (NiTi) ^[66]	Medical devices, eyeglass frames, robotics	High	Moderate	Good
Cu-Zn-Al ^[59]	Actuators, robotics, aerospace	Moderate	Moderate	Poor
Ni-Ti-Pd ^[60]	Aerospace, medical implants	High	Moderate	Good
Fe-Pt ^[68]^[69]	Actuators, sensors, robotics	High	High	Poor
Cu-Al-Ni ^[67]	Robotics, automotive	Moderate	Moderate	Poor
Ni-Al-Mn ^[65]	Actuators, medical devices	High	Moderate	Good

2.2. Shape Memory Polymers (SMPs)

Shape memory polymers (SMPs) are a class of biomimetic smart materials that possess the ability to “remember” their original shape and recover it after being deformed, triggered by specific stimuli such as temperature, light, or pH changes. SMPs are typically composed of a polymer network with the capability to exhibit two distinct states: a temporary shape that can be easily manipulated, and a permanent shape that the material will revert back to when activated. This unique behavior stems from the reversible transitions between the material’s glassy and rubbery states, allowing for significant deformation and shape recovery ^[70]. One of the remarkable features of SMPs is their tunability, as their mechanical properties and transition temperatures can be tailored by modifying the polymer composition and crosslinking density. This flexibility enables the design and fabrication of SMPs with desired shape memory behaviors suitable for specific applications. SMPs offer advantages such as low weight, low cost, and ease of processing, making them attractive for a number of fields ^[71].

The applications of SMPs span diverse industries. In the biomedical field, SMPs have gained attention for their potential in minimally invasive surgery, tissue engineering scaffolds, drug delivery systems, and shape-adaptive medical devices ^[72]. SMPs can be designed to respond to body temperature or other physiological cues, allowing for precise, controlled, and localized shape changes in response to the surrounding environment ^[73]. In the aerospace sector, SMPs find use in morphing structures, deployable systems, and adaptive components, where their shape memory behavior can enable efficient aerodynamic profiles and structural optimization ^[74]^[75]. Figure 2 depicts the major fields where SMPs apply.

Figure 2. Major application fields for SMPs.

The practical implementation of shape memory polymers (SMPs) encounters various challenges that ongoing research aims to address. Achieving precise control over shape memory transitions remains a primary hurdle, demanding a comprehensive understanding of how these materials respond to diverse stimuli. Enhancing durability and fatigue resistance stands as another critical area necessitating attention and improvement within SMP research. Researchers are dedicated to unraveling these challenges by delving into advanced methodologies, seeking to grasp the intricate mechanisms governing SMP behavior under different conditions. Moreover, there is a concerted effort towards developing SMPs with multifunctional properties, integrating stimulus-responsive shape memory behavior with capabilities such as self-healing or conductivity ^[75]. Despite these challenges, the versatile nature of SMPs continues to inspire researchers and engineers. Their unique ability to endure substantial deformation and recover their original shape offers a multitude of possibilities for innovative applications. Ongoing progress in SMP research holds promise for the development of advanced smart materials capable of adapting and responding to changing environments. Researchers are exploring novel material formulations, advanced processing techniques, and predictive modeling approaches to overcome these challenges, aiming to pave the way for next-generation technologies in diverse fields like medicine, aerospace, robotics, and beyond. Through interdisciplinary collaboration and innovative approaches, research endeavors seek to overcome these hurdles and harness the full potential of shape memory polymers in practical applications.

Among the various shape memory polymers (SMPs), polyurethane-based SMPs hold significant importance and have been extensively studied and utilized in a wide range of applications ^[76]. Polyurethane SMPs offer excellent shape memory behavior, mechanical properties, and processability, making them highly versatile for engineering applications. Their shape memory effect can be triggered via temperature, allowing for reversible shape changes upon heating or cooling. Polyurethane SMPs find extensive use in fields such as biomedical engineering, where they are employed in shape memory sutures, stents, and tissue engineering scaffolds. Additionally, their biocompatibility, flexibility, and customizable properties make them suitable for drug delivery systems and wearable devices. In industries like aerospace and robotics, polyurethane SMPs are utilized in morphing structures, adaptive components, and soft actuators, using SMPs’ shape-changing capabilities for enhanced performance and functionality. The broad range of applications and the favorable combination of properties make polyurethane-based SMPs the most important and widely utilized among the various types of shape memory polymers ^[77].

Another noteworthy class of shape memory polymers (SMPs) is based on poly-caprolactone (PCL), a biodegradable and biocompatible material ^[78]. PCL-based SMPs possess shape memory properties that can be triggered by temperature changes. These SMPs exhibit excellent shape recovery and mechanical properties, making them valuable for various applications. In the biomedical field, PCL-based SMPs find use in tissue engineering scaffolds, drug delivery systems, and wound dressings, as they can adapt to the body’s contours and provide controlled release of therapeutics. The biodegradability of PCL allows for the gradual regeneration of tissue over time. Moreover, PCL-based SMPs have also been explored in areas such as soft robotics and textiles, where their shape memory behavior enables the development of adaptable and responsive systems. The versatility and biocompatibility of PCL-based SMPs position them as a significant material for creating smart shape-changing structures and devices with potential applications in diverse fields ^[79]. By comparing polyurethane-based shape memory polymers (SMPs) and polycaprolactone-based SMPs, distinct features and diverse applications can be found. Polyurethane-based SMPs often exhibit faster shape recovery rates and higher shape fixity compared to polycaprolactone-based ones. They tend to have superior mechanical properties, offering higher strength and toughness, making them suitable for applications requiring robustness, such as in structural components or medical devices. On the other hand, polycaprolactone-based SMPs typically demonstrate greater elongation at their breaking point and are more flexible, enabling them to be utilized in applications demanding higher deformability and shape adaptability, like in soft robotics or biomedical devices where flexibility is paramount ^[79]. These distinctions in mechanical properties and flexibility allow for tailored applications in various industries, highlighting the versatility of shape memory polymers based on their composition and characteristics.

Polyethylene-based shape memory polymers (SMPs) represent a notable class of SMPs with distinct advantages and applications ^[80]. These SMPs are derived from poly-ethylene, a widely used and versatile polymer known for its excellent mechanical properties and chemical resistance. Polyethylene-based SMPs display shape memory be-havior that can be triggered by temperature, enabling reversible shape changes. Their high elasticity and recoverable strain capacity make them suitable for applications requiring large deformations and shape recovery. Polyethylene-based SMPs have found applications in various fields, including automotive engineering, where they are used in automotive components, such as self-repairing bumpers and shape-adaptive panels. Ad-ditionally, in the field of soft robotics, polyethylene-based SMPs are employed in the development of flexible actuators and grippers that can adapt to complex shapes and perform delicate tasks. The robustness, processability, and shape memory properties of polyethylene-based SMPs make them an important material for engineering applications, showcasing their potential to advance technologies in diverse industries. Table 2 depicts the most common SMPs, their applications, and their most prominent features ^[81].

Table 2. Most common SMPs, their applications, and their most prominent features.

SMP Type	Applications	Strength	Cost-Effectiveness	Biocompatibility
Polyurethane-based ^[76]^[77]	Biomedical devices, textiles, actuators	Moderate	Moderate	Varies (depends on formulation)
Polyethylene-based ^[80]^[81]	Textiles, automotive applications, robotics	Low to moderate	Low to moderate	Generally good
Polyvinyl-based ^[70]^[72]	Biomedical devices, textiles	Low to moderate	Moderate	Generally good
Epoxy-based ^[71]	Aerospace applications, robotics, deployable structures	Moderate to high	Moderate to high	Varies (depends on formulation)
Polycaprolactone ^[78]^[79]	Biomedical implants, drug delivery	Low to moderate	Moderate	Generally good
Polyethylene terephthalate (PET) ^[80]^[81]	Textiles, packaging, automotive applications	Moderate to high	Moderate to high	Generally good

2.3. Electroactive Polymers (EAPs)

Electroactive polymers (EAPs) are a class of materials that possess the unique ability to undergo significant shape changes or actuation in response to electrical stimulation. These polymers imitate the electrical signaling properties of biological systems, enabling them to convert electrical energy into mechanical motion. This remarkable characteristic makes EAPs highly attractive for a wide range of applications, including robotics, sensing, artificial muscles, and biomedical devices ^[82].

One of the most well-known types of EAPs is dielectric elastomers (DEs) ^[83]. DEs consist of a flexible elastomeric material sandwiched between two compliant electrodes. When an electric field is applied, the electrodes attract each other, causing the elastomer to compress or expand. This deformation allows DEs to possess large actuation capabilities and have applications in areas such as soft robotics, where they can replicate natural muscle-like movement and dexterity ^[83]. Another significant type of EAP is conducting polymer-based EAP, which includes materials like polypyrrole and polyaniline. These polymers exhibit electrical conductivity that can be modulated via an applied electric field ^[84]. By controlling the doping or de-doping process, the polymer can undergo changes in volume, shape, or mechanical properties. Conducting polymer-based EAPs have been utilized in actuators, sensors, and artificial muscles, offering advantages such as low weight, flexibility, and responsiveness ^[85]. Figure 3 shows such an EAP undergoing shape changes in response to electrical stimulation.

Figure 3. EAP undergoing shape changes in response to electrical stimulation, (a) initial position, (b) open position, (c) closed position.

While EAPs offer great potential, there are challenges that need to be addressed for wider practical implementation. These challenges include improving the materials’ mechanical stability, enhancing the efficiency of energy conversion, and increasing their operational lifespan. Researchers continue to explore new EAP formulations, fabrication techniques, and actuation mechanisms to overcome these obstacles and unlock the full potential of EAPs.

The field of electroactive polymers holds promise for revolutionizing numerous technological domains, ranging from soft robotics and haptic interfaces to biomedical devices and energy harvesting systems. The ability of EAPs to imitate the electrical behavior of biological systems provides opportunities for the development of advanced, adaptive, and intelligent materials that can respond to electrical stimuli. As research progresses and technologies evolve, electroactive polymers are poised to play a pivotal role in shaping the future of engineering, robotics, and human-machine interfaces.

Conductive polymer-based EAPs, such as polypyrrole (PPy), represent another significant class of electroactive polymers with intriguing properties ^[86]. PPy is a conju-gated polymer that exhibits electrical conductivity and can undergo reversible changes in volume and shape in response to electrical stimulation. The doping and de-doping process of PPy, driven by redox reactions, leads to variations in its electrical and me-chanical properties. This makes PPy a promising material for applications such as ac-tuators, sensors, and energy harvesting devices. PPy-based EAPs offer advantages like high responsiveness, mechanical flexibility, and biocompatibility, which make them suitable for emerging fields such as soft robotics, bioelectronics, and biomedical engineering. The tunability of PPy-based EAPs, along with their compatibility with traditional fabrication techniques, holds promise for the development of innovative and efficient electroactive systems that bridge the gap between biological and artificial systems ^[86].

Another noteworthy electroactive polymer (EAP) is the ferroelectric polymer poly(vinylidene fluoride-trifluoroethylene) (PVDF-TrFE) ^[87]. PVDF-TrFE exhibits piezoelectric properties, meaning it can generate electrical charges when subjected to mechanical stress and, conversely, undergo mechanical deformation when an electric field is applied. This unique property enables PVDF-TrFE to be utilized in various applications, including sensors, actuators, energy harvesting devices, and biomedical applications. PVDF-TrFE-based EAPs offer advantages such as high mechanical flexibility, good chemical stability, and biocompatibility, making them suitable for wearable electronics, human–machine interfaces, and implantable devices. The ability of PVDF-TrFE to convert mechanical energy into electrical energy and vice versa holds tremendous potential for advancing technologies in areas ranging from smart sensing systems to self-powered devices. Table 3 depicts the most common EAPs, their applications, and their most prominent features ^[87].

Table 3. Most common EAPs, their applications, and their most prominent features.

EAP Type	Applications	Strength	Cost-Effectiveness	Biocompatibility
Polypyrrole ^[86]	Artificial muscles, sensors, actuators	Low to moderate	Moderate	Limited
Polyaniline ^[84]	Sensors, actuators, electronic textiles	Low to moderate	Moderate	Limited
Ionic Polymer-Metal Composite (IPMC) ^[82]	Soft robotics, sensors	Low	Moderate	Limited
Dielectric Elastomer ^[83]	Soft robotics, haptic feedback, medical devices	Low to moderate	Moderate	Limited
Conductive Elastomer ^[85]	Tactile sensors, wearable electronics	Low to moderate	Moderate	Limited
Ferroelectric Polymers ^[87]	Energy harvesting, sensors	Low to moderate	Moderate	Limited

References

Kantaros, A.; Ganetsos, T.; Piromalis, D. 4D Printing: Technology Overview and Smart Materials Utilized. J. Mechatron. Robot. 2023, 7, 1–14.
Kantaros, A.; Ganetsos, T.; Piromalis, D. 3D and 4D Printing as Integrated Manufacturing Methods of Industry 4.0. Am. J. Eng. Appl. Sci. 2023, 16, 12–22.
Momeni, F.M.; Mehdi Hassani, N.S.; Liu, X.; Ni, J. A Review of 4D Printing. Mater. Des. 2017, 122, 42–79.
Kuang, X.; Roach, D.J.; Wu, J.; Hamel, C.M.; Ding, Z.; Wang, T.; Dunn, M.L.; Qi, H.J. Advances in 4D Printing: Materials and Applications. Adv. Funct. Mater. 2019, 29, 1805290.
Choi, J.; Kwon, O.-C.; Jo, W.; Lee, H.J.; Moon, M.-W. 4D Printing Technology: A Review. 3D Print. Addit. Manuf. 2015, 2, 159–167.
Khoo, Z.X.; Teoh, J.E.M.; Liu, Y.; Chua, C.K.; Yang, S.; An, J.; Leong, K.F.; Yeong, W.Y. 3D Printing of Smart Materials: A Review on Recent Progresses in 4D Printing. Virtual Phys. Prototyp. 2015, 10, 103–122.
Chu, H.; Yang, W.; Sun, L.; Cai, S.; Yang, R.; Liang, W.; Yu, H.; Liu, L. 4D Printing: A Review on Recent Progresses. Micromachines 2020, 11, 796.
Joshi, S.; Rawat, K.; Karunakaran; Rajamohan, V.; Mathew, A.T.; Koziol, K.; Kumar Thakur, V. Balan 4D Printing of Materials for the Future: Opportunities and Challenges. Appl. Mater. Today 2020, 18, 100490.
Tibbits, S. 4D Printing: Multi-Material Shape Change. Archit. Des. 2014, 84, 116–121.
Wu, J.-J.; Huang, L.-M.; Zhao, Q.; Xie, T. 4D Printing: History and Recent Progress. Chin. J. Polym. Sci. 2018, 36, 563–575.
Javaid, M.; Haleem, A. 4D Printing Applications in Medical Field: A Brief Review. Clin. Epidemiol. Glob. Health 2019, 7, 317–321.
Ahmed, A.; Arya, S.; Gupta, V.; Furukawa, H.; Khosla, A. 4D Printing: Fundamentals, Materials, Applications and Challenges. Polymer 2021, 228, 123926.
Zhang, Z.; Demir, K.G.; Gu, G.X. Developments in 4D-Printing: A Review on Current Smart Materials, Technologies, and Applications. Int. J. Smart Nano Mater. 2019, 10, 205–224.
Sun, T.; Qing, G. Biomimetic Smart Interface Materials for Biological Applications. Adv. Mater. 2011, 23, H57–H77.
Shen, A.Q.; Hamlington, B.; Knoblauch, M.; Peters, W.S.; Pickard, W.F. Forisome Based Biomimetic Smart Materials. Available online: https://web.archive.org/web/20170216155956id_/http://faculty.washington.edu/amyshen/smartsystem.pdf (accessed on 7 September 2023).
Suresh Kumar, N.; Padma Suvarna, R.; Chandra Babu Naidu, K.; Banerjee, P.; Ratnamala, A.; Manjunatha, H. A Review on Biological and Biomimetic Materials and Their Applications. Appl. Phys. A Mater. Sci. Process. 2020, 126, 445.
Fratzl, P. Biomimetic Materials Research: What Can We Really Learn from Nature’s Structural Materials? J. R. Soc. Interface 2007, 4, 637–642.
Vincent, J.F.V. Biomimetic Materials. J. Mater. Res. 2008, 23, 3140–3147.
Huang, G.; Li, F.; Zhao, X.; Ma, Y.; Li, Y.; Lin, M.; Jin, G.; Lu, T.J.; Genin, G.M.; Xu, F. Functional and Biomimetic Materials for Engineering of the Three-Dimensional Cell Microenvironment. Chem. Rev. 2017, 117, 12764–12850.
Ma, P.X. Biomimetic Materials for Tissue Engineering. Adv. Drug Deliv. Rev. 2008, 60, 184–198.
Patterson, J.; Martino, M.M.; Hubbell, J.A. Biomimetic Materials in Tissue Engineering. Mater. Today 2010, 13, 14–22.
Zhu, Y.; Joralmon, D.; Shan, W.; Chen, Y.; Rong, J.; Zhao, H.; Xiao, S.; Li, X. 3D Printing Biomimetic Materials and Structures for Biomedical Applications. Biodes. Manuf. 2021, 4, 405–428.
Naik, R.R.; Singamaneni, S. Introduction: Bioinspired and Biomimetic Materials. Chem. Rev. 2017, 117, 12581–12583.
Cho, K.-J.; Koh, J.-S.; Kim, S.; Chu, W.-S.; Hong, Y.; Ahn, S.-H. Review of Manufacturing Processes for Soft Biomimetic Robots. Int. J. Precis. Eng. Manuf. 2009, 10, 171–181.
Gao, Z.; Shi, Q.; Fukuda, T.; Li, C.; Huang, Q. An Overview of Biomimetic Robots with Animal Behaviors. Neurocomputing 2019, 332, 339–350.
Wang, J.; Chen, W.; Xiao, X.; Xu, Y.; Li, C.; Jia, X.; Meng, M.Q.-H. A Survey of the Development of Biomimetic Intelligence and Robotics. Biomim. Intell. Robot. 2021, 1, 100001.
Pampaloni, F.; Florin, E.-L. Microtubule Architecture: Inspiration for Novel Carbon Nanotube-Based Biomimetic Materials. Trends Biotechnol. 2008, 26, 302–310.
Voronkina, A.; Romanczuk-Ruszuk, E.; Przekop, R.E.; Lipowicz, P.; Gabriel, E.; Heimler, K.; Rogoll, A.; Vogt, C.; Frydrych, M.; Wienclaw, P.; et al. Honeycomb Biosilica in Sponges: From Understanding Principles of Unique Hierarchical Organization to Assessing Biomimetic Potential. Biomimetics 2023, 8, 234.
Lu, H.Z.; Yang, C.; Luo, X.; Ma, H.W.; Song, B.; Li, Y.Y.; Zhang, L.C. Ultrahigh-Performance TiNi Shape Memory Alloy by 4D Printing. Mater. Sci. Eng. A Struct. Mater. 2019, 763, 138166.
Kim, D.; Ferretto, I.; Kim, W.; Leinenbach, C.; Lee, W. Effect of Post-Heat Treatment Conditions on Shape Memory Property in 4D Printed Fe–17Mn–5Si–10Cr–4Ni Shape Memory Alloy. Mater. Sci. Eng. A Struct. Mater. 2022, 852, 143689.
Yao, T.; Wang, Y.; Zhu, B.; Wei, D.; Yang, Y.; Han, X. 4D Printing and Collaborative Design of Highly Flexible Shape Memory Alloy Structures: A Case Study for a Metallic Robot Prototype. Smart Mater. Struct. 2021, 30, 015018.
Kim, D.; Ferretto, I.; Leinenbach, C.; Lee, W. 3D and 4D Printing of Complex Structures of Fe–Mn–Si-based Shape Memory Alloy Using Laser Powder Bed Fusion. Adv. Mater. Interfaces 2022, 9, 2200171.
Abolhasani, D.; Han, S.W.; VanTyne, C.J.; Kang, N.; Moon, Y.H. Powder Bed Fusion of Two-Functional Cu-Al-Ni Shape Memory Alloys Utilized for 4D Printing. J. Alloys Compd. 2022, 922, 166228.
Ge, Q.; Sakhaei, A.H.; Lee, H.; Dunn, C.K.; Fang, N.X.; Dunn, M.L. Multimaterial 4D Printing with Tailorable Shape Memory Polymers. Sci. Rep. 2016, 6, 31110.
Bodaghi, M.; Damanpack, A.R.; Liao, W.H. Triple Shape Memory Polymers by 4D Printing. Smart Mater. Struct. 2018, 27, 065010.
Spiegel, C.A.; Hackner, M.; Bothe, V.P.; Spatz, J.P.; Blasco, E. 4D Printing of Shape Memory Polymers: From Macro to Micro. Adv. Funct. Mater. 2022, 32, 2110580.
Luo, B.; Zhu, Z.; Xu, X.; Bian, C. 4D-Printed Low-Voltage Electroactive Polymers Modeling and Fabrication. In Smart Materials in Additive Manufacturing, Volume 1: 4D Printing Principles and Fabrication; Elsevier: Amsterdam, The Netherlands, 2022; pp. 107–150.
Ma, S.; Zhang, Y.; Wang, M.; Liang, Y.; Ren, L.; Ren, L. Recent Progress in 4D Printing of Stimuli-Responsive Polymeric Materials. Sci. China Technol. Sci. 2020, 63, 532–544.
Oguntona, O.A.; Aigbavboa, C.O. Promoting Biomimetic Materials for a Sustainable Construction Industry. Bioinspired Biomim. Nanobiomaterials 2017, 6, 122–130.
Speck, T.; Speck, O. Emergence in Biomimetic Materials Systems. In Emergence and Modularity in Life Sciences; Springer International Publishing: Cham, Switzerland, 2019; pp. 97–115. ISBN 9783030061272.
Pereira, P.M.M.; Monteiro, G.A.; Prazeres, D.M.F. General Aspects of Biomimetic Materials. In Biotechnologies and Biomimetics for Civil Engineering; Springer International Publishing: Cham, Switzerland, 2015; pp. 57–79. ISBN 9783319092867.
The 17 Goals. Available online: https://sdgs.un.org/goals (accessed on 7 September 2023).
Andreotti, M.; Brondi, C.; Micillo, D.; Zevenhoven, R.; Rieger, J.; Jo, A.; Hettinger, A.-L.; Bollen, J.; Malfa, E.; Trevisan, C.; et al. SDGs in the EU Steel Sector: A Critical Review of Sustainability Initiatives and Approaches. Sustainability 2023, 15, 7521.
Mori Junior, R.; Fien, J.; Horne, R. Implementing the UN SDGs in Universities: Challenges, Opportunities, and Lessons Learned. Sustainability 2019, 12, 129–133.
Abbate, E.; Mirpourian, M.; Brondi, C.; Ballarino, A.; Copani, G. Environmental and Economic Assessment of Repairable Carbon-Fiber-Reinforced Polymers in Circular Economy Perspective. Materials 2022, 15, 2986.
Küfeoğlu, S. SDG-9: Industry, Innovation and Infrastructure. In Emerging Technologies; Springer International Publishing: Cham, Switzerland, 2022; pp. 349–369. ISBN 9783031071263.
Koch, F.; Krellenberg, K. How to Contextualize SDG 11? Looking at Indicators for Sustainable Urban Development in Germany. ISPRS Int. J. Geo-Inf. 2018, 7, 464.
Russell, C. Sustainable Cities and Communities from Backyards to Biolinks: Royal Botanic Gardens Victoria’s Role in Urban Greening. BGjournal 2018, 15, 31–33.
Beccarello, M.; Di Foggia, G. Sustainable Development Goals Data-Driven Local Policy: Focus on SDG 11 and SDG 12. Adm. Sci. 2022, 12, 167.
Campbell, B.M.; Hansen, J.; Rioux, J.; Stirling, C.M.; Twomlow, S.; Wollenberg, E. Urgent Action to Combat Climate Change and Its Impacts (SDG 13): Transforming Agriculture and Food Systems. Curr. Opin. Environ. Sustain. 2018, 34, 13–20.
Arana, C.; Franco, I.B.; Joshi, A.; Sedhai, J. SDG 15 Life on Land: A Review of Sustainable Fashion Design Processes: Upcycling Waste Organic Yarns. In Science for Sustainable Societies; Springer: Singapore, 2020; pp. 247–264. ISBN 9789813299269.
Essential Surgical Care WHO Global Initiative for Emergency and Essential Surgical Care. Available online: https://apps.who.int/iris/bitstream/handle/10665/206541/WHO_HIS_SDS_2016.11_eng.pdf (accessed on 7 September 2023).
Agarwal, B. Gender Equality, Food Security and the Sustainable Development Goals. Curr. Opin. Environ. Sustain. 2018, 34, 26–32.
Clark, H.; Wu, H. The Sustainable Development Goals: 17 Goals to Transform Our World. In Furthering the Work of the United Nations; United Nations: New York, NY, USA, 2016; pp. 36–54. ISBN 9789210583237.
Ayushi; Kumar Vates, U.; Mishra, S.; Jee Kanu, N. Biomimetic 4D Printed Materials: A State-of-the-Art Review on Concepts, Opportunities, and Challenges. Mater. Today 2021, 47, 3313–3319.
Shape Memory Alloys: Modeling and Engineering Applications; Springer: Boston, MA, USA, 2008; ISBN 9780387476841.
Kumar, P.K.; Lagoudas, D.C. Introduction to Shape Memory Alloys. In Shape Memory Alloys; Springer: Boston, MA, USA, 2008; pp. 1–51. ISBN 9780387476841.
Otsuka, K.; Kakeshita, T. Science and Technology of Shape-Memory Alloys: New Developments. MRS Bull. 2002, 27, 91–100.
Hartl, D.J.; Lagoudas, D.C. Aerospace Applications of Shape Memory Alloys. Proc. Inst. Mech. Eng. G J. Aerosp. Eng. 2007, 221, 535–552.
Costanza, G.; Tata, M.E. Shape Memory Alloys for Aerospace, Recent Developments, and New Applications: A Short Review. Materials 2020, 13, 1856.
Aversa, R.; Tamburrino, F.; Petrescu, R.V.V.; Petrescu, F.I.T.; Artur, M.; Chen, G.; Apicella, A. Biomechanically Inspired Shape Memory Effect Machines Driven by Muscle like Acting NiTi Alloys. Am. J. Appl. Sci. 2016, 13, 1264–1271.
Raffaella, A.; Petrescu, F.I.T.; Petrescu, R.V.V.; Antonio, A. Biomimetic Finite Element Analysis Bone Modeling for Customized Hybrid Biological Prostheses Development. Am. J. Appl. Sci. 2016, 13, 1060–1067.
Aversa, R.; Petrescu, R.V.V.; Apicella, A.; Petrescu, F.I.T. A Nanodiamond for Structural Biomimetic Scaffolds. Eng. Rev. 2019, 39, 81–89.
Petrescu, N.; Aversa, R.; Apicella, A.; Petrescu, F.I.T. Something about Robots Today. J. Mechatron. Robot. 2018, 2, 85–104.
Ganetsos, T.; Kantaros, A.; Gioldasis, N.; Brachos, K. Applications of 3D Printing and Illustration in Industry. In Proceedings of the 2023 17th International Conference on Engineering of Modern Electric Systems (EMES), Oradea, Romania, 9–10 June 2023; pp. 1–4.
Wen, S.; Gan, J.; Li, F.; Zhou, Y.; Yan, C.; Shi, Y. Research Status and Prospect of Additive Manufactured Nickel-Titanium Shape Memory Alloys. Materials 2021, 14, 4496.
Gohar, G.A.; Manzoor, T.; Ahmad, A.; Raza, H.; Farooq, A.; Karim, I.; Iftikhar, W.; Umar, M.; Asad, F. Synthesis and Investigate the Properties of Cu–Al–Ni Alloys with Ag Addition Using Powder Metallurgy Technique. J. Alloys Compd. 2020, 817, 153281.
Ur Rahman, R.A.; Juhre, D.; Halle, T. Review of Types, Properties, and Importance of Ferrous Based Shape Memory Alloys. Korean J. Mater. Res. 2018, 28, 381–390.
Mohd Jani, J.; Leary, M.; Subic, A.; Gibson, M.A. A Review of Shape Memory Alloy Research, Applications and Opportunities. Mater. Eng. 2014, 56, 1078–1113.
Xia, Y.; He, Y.; Zhang, F.; Liu, Y.; Leng, J. A Review of Shape Memory Polymers and Composites: Mechanisms, Materials, and Applications. Adv. Mater. 2021, 33, 2000713.
Hager, M.D.; Bode, S.; Weber, C.; Schubert, U.S. Shape Memory Polymers: Past, Present and Future Developments. Prog. Polym. Sci. 2015, 49–50, 3–33.
Yakacki, C.M.; Gall, K. Shape-Memory Polymers for Biomedical Applications. In Shape-Memory Polymers; Springer: Berlin/Heidelberg, Germany, 2009; pp. 147–175. ISBN 9783642123580.
Wang, K.; Strandman, S.; Zhu, X.X. A Mini Review: Shape Memory Polymers for Biomedical Applications. Front. Chem. Sci. Eng. 2017, 11, 143–153.
Li, F.; Liu, Y.; Leng, J. Progress of Shape Memory Polymers and Their Composites in Aerospace Applications. Smart Mater. Struct. 2019, 28, 103003.
Liu, Y.; Du, H.; Liu, L.; Leng, J. Shape Memory Polymers and Their Composites in Aerospace Applications: A Review. Smart Mater. Struct. 2014, 23, 023001.
Sabahi, N.; Roohani, I.; Wang, C.H.; Farajzadeh, E.; Li, X. Thermoplastic Polyurethane-Based Shape Memory Polymers with Potential Biomedical Application: The Effect of TPU Soft-Segment on Shape Memory Effect and Cytocompatibility. Polymer 2023, 283, 126189.
Zhou, J.; Li, H.; Liu, W.; Dugnani, R.; Tian, R.; Xue, W.; Chen, Y.; Guo, Y.; Duan, H.; Liu, H. A Facile Method to Fabricate Polyurethane Based Graphene Foams/Epoxy/Carbon Nanotubes Composite for Electro-Active Shape Memory Application. Compos. Part A Appl. Sci. Manuf. 2016, 91, 292–300.
Defize, T.; Riva, R.; Thomassin, J.-M.; Alexandre, M.; Van Herck, N.; Prez, F.D.; Jérôme, C. Reversible TAD Chemistry as a Convenient Tool for the Design of (Re)Processable PCL-Based Shape-Memory Materials. Macromol. Rapid Commun. 2017, 38, 1600517.
Pfau, M.R.; McKinzey, K.G.; Roth, A.A.; Grunlan, M.A. PCL-Based Shape Memory Polymer Semi-IPNs: The Role of Miscibility in Tuning the Degradation Rate. Biomacromolecules 2020, 21, 2493–2501.
Dolog, R.; Weiss, R.A. Shape Memory Behavior of a Polyethylene-Based Carboxylate Ionomer. Macromolecules 2013, 46, 7845–7852.
Scalet, G.; Auricchio, F.; Bonetti, E.; Castellani, L.; Ferri, D.; Pachera, M.; Scavello, F. An Experimental, Theoretical and Numerical Investigation of Shape Memory Polymers. Int. J. Plast. 2015, 67, 127–147.
Bar-Cohen, Y. Electroactive Polymers: Current Capabilities and Challenges. In Proceedings of the SPIE Proceedings, San Diego, CA, USA, 17–21 March 2002; Bar-Cohen, Y., Ed.; SPIE: Bellingham, WA, USA, 2002.
Lu, T.; Ma, C.; Wang, T. Mechanics of Dielectric Elastomer Structures: A Review. Extreme Mech. Lett. 2020, 38, 100752.
Woehling, V.; Nguyen, G.T.M.; Plesse, C.; Petel, Y.; Dobashi, Y.; Madden, J.D.W.; Michal, C.A.; Vidal, F. Study of the Piezoionic Effect and Influence of Electrolyte in Conducting Polymer Based Soft Strain Sensors. Multifunct. Mater. 2019, 2, 045002.
Seurre, L.; Aréna, H.; Ghenna, S.; Soyer, C.; Grondel, S.; Plesse, C.; Nguyen, G.T.M.; Vidal, F.; Cattan, E. Behavior of Conducting Polymer-Based Micro-Actuators under a DC Voltage. Sens. Actuators B Chem. 2023, 380, 133338.
Choudhary, R.B.; Ansari, S.; Purty, B. Robust Electrochemical Performance of Polypyrrole (PPy) and Polyindole (PIn) Based Hybrid Electrode Materials for Supercapacitor Application: A Review. J. Energy Storage 2020, 29, 101302.
Sharma, T.; Je, S.-S.; Gill, B.; Zhang, J.X.J. Patterning Piezoelectric Thin Film PVDF–TrFE Based Pressure Sensor for Catheter Application. Sens. Actuators A Phys. 2012, 177, 87–92.

© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.

Upload a video for this entry

Information

Subjects: Materials Science, Biomaterials

Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :

Antreas Kantaros

Theodore Ganetsos

Florian Ion Tiberiu Petrescu

View Times: 236

Update Date: 22 Jan 2024

Table of Contents

Video Upload Options

Confirm