Smart Materials and Structures

This work explores a demultiplexer that realizes the spatial separation of bending waves through temperature-tunable topological interface modes. Unlike fixed-property configurations that split input signals into predetermined frequency components, our proposed reconfigurable setup allows the adjustment of the frequency components according to the temperature. The model consists of a metallic host structure covered by pairs of piezoelectric elements connected to negative capacitance circuits. Additionally, the metamaterial features periodically distributed mass-spring resonators made of shape memory alloy (SMA). The negative capacitance enables modification of the local stiffness of each unit cell. At the same time, the resonance frequency of each mass-spring attachment is controlled by changing the temperature of the SMA springs. After characterizing the topological properties of the proposed unit cell configuration, we numerically investigate a two-channel topologically protected demultiplexer. The results demonstrate the system's capability to extract signals at temperature-tunable frequencies of topological modes with enhanced energy localization.

This work presents innovations in polymer science through the development of antimicrobial and reprocessable shape-memory vitrimers from biobased vanillin and glycerol acrylates, incorporating pentaerythritol tetrakis(3-mercaptopropionate). The addition of this thiol increased the viscosity of the resin and reduced shrinkage and rigidity, without significantly affecting the polymerization rate. Samples containing 20 wt.% of thiol exhibited self-welding and 40% self-healing efficiency after just 10 min of heating at 180 °C and without additional pressure, significantly improving mechanical properties. The ability of vitrimers to maintain a temporary shape and return to a permanent shape under temperature changes showed shape-memory behavior, making them suitable for medicine, electronics, and robotics. The mechanical properties remained consistent after three reprocessing cycles, highlighting the sustainability of the vitrimers. The antimicrobial activity of these vitrimers showed efficacy up to 100%, suitable for antimicrobial films, coatings, and 3D printed parts. Microimprint lithography enabled micrometer-scale patterns, highlighting broad practical applications of the vitrimers.

In this work, a new experimental shunt tuning procedure is proposed for piezoelectric shunts targeting a single vibration mode of a flexible structure. The procedure is based on measurements of the dynamic capacitance of the piezoelectric transducer, eliminating the need for additional sensors or actuators. It is demonstrated that all required electromechanical parameters for shunt calibration can be determined from two extremum points on the dynamic capacitance curve. Corrections for the influence of residual modes are included to enable the consistent application of existing tuning expressions for various shunt topologies. The hardware of a digital shunt is used for the measurement of the dynamic capacitance allowing for low-cost implementation. The proposed method is validated numerically and experimentally through the calibration of a series RL shunt using both analog and digital components.

In recent years, fiber- and fabric-based strain sensors have demonstrated potential for achieving high sensitivity and large working sensing ranges. However, they are associated with difficulties due to unsatisfactory repeatability and poor wearing comfort. Here, we report a novel design of a flexible and extensible strain sensor with a core–sheath structure, where a conductive blended fiber assembles wrap around a flexible Spandex core filament. Compared to our previous conductive blended yarn, the elastic core-spun conductive yarn in this study enables an increased strain sensing range from 3% to approximately 16%, while guarantees excellent stability and durability during 100 stretching cycles and high linearity (goodness-of-fit >0.97). Due to these enhanced performance attributes, the prepared elastic yarn strain sensor effectively recognizes gesture language when being fixed on a glove and presents remarkable advantages in detecting and monitoring joint activities (finger, elbow, knee, and neck).

Polydimethylsiloxane (PDMS) based ferroelectrets are an attractive material for the fabrication of human-based applications given their soft and compliant mechanical properties. The typical fabrication approach is to exploit specifically engineered void geometries fabricated by moulding and bonding layers together. Charge instability can be addressed by the addition of Polytetrafluoroethylene (PTFE) particles but this prevents the bonding of PDMS films. This paper illustrates a new approach to obtaining PDMS ferroelectret with random voids by promoting and trapping bubbles to create cavities within a PDMS film. A mathematical model is presented to explore the connection between the percentage of trapped bubbles in the PDMS and the equivalent piezoelectric coefficient, d_33e. The process is compatible with the addition of PTFE powder to the PDMS formulation and a ratio of PTFE to PDMS of 1:3 by weight was found to increase performance achieving an initial d_33e of 384 pC/N which reached a steady state value of 186 pC/N measured after 2 months after poling. The energy harvesting potential of the random void PDMS/PTFE ferroelectret was explored by cyclically compressing with a force of 300 N applied at 1 Hz. The output of the ferroelectret was found to charge a 10μF capacitor to 0.26 V after 40 s. The ferroelectret's performance as a pressure sensor from 0 to 300 N was explored, and the optimum formulation achieved a sensitivity of 25.2 mV/N, a nonlinearity of 4%, and hysteresis of 4.2%, demonstrating a considerable improvement over the pure PDMS ferroelectret.

Ultrasonic guided waves (UGWs) can travel long distances within the detected structures, which is of great significance for monitoring large complex engineering systems. However, the multimodal and dispersive properties of the specific research object making this promising whole structure monitoring difficult to interpret the signal mathematically and physically. With the development and maturity of deep learning and big data mining technologies, many scholars have noticed artificial intelligence algorithms such as deep learning can provide a new tool in UGWs signal processing, avoiding the mechanism analysis difficulties in the application of UGWs. But the integrity of structural state data sets has become a new pain point in engineering applications under this new approach, and how to apply the knowledge obtained from the existing data set to different but related fields through knowledge transfer in such cases begin to attract the attention of scholars and engineers. Although several systematic and valuable review articles on data-driven UGWs monitoring methods have been published, they only summarized relevant studies from the perspective of data-driven algorithms, ignoring the knowledge transfer process in practical application scenarios, and the intelligent UGWs monitoring methods based on knowledge transfer of incomplete sets are still lacking a comprehensive review. This paper focuses on the UGWs transfer monitoring technology when the training sample is missing, explores the feature correlation between samples in different domains, improves the transfer ability of the structural monitoring model under different conditions, and analyzes the UGWs intelligent monitoring methods for structural state under different sample missing conditions from three aspects: semi-supervised monitoring, multi-task transfer and cross-structure transfer. It is also expected to provide a new method and approach to solve the condition monitoring problems in other complex scenarios.

The current boom in soft robotics development has spurred extensive research into these flexible, deformable, and adaptive robotic systems. However, the unique characteristics of soft materials, such as non-linearity and hysteresis, present challenges in modeling, calibration, and control, laying the foundation for a compelling exploration based on finite element analysis (FEA), machine learning (ML), and digital twins (DT). Therefore, in this review paper, we present a comprehensive exploration of the evolving field of soft robots, tracing their historical origins and current status. We explore the transformative potential of FEA and ML in the field of soft robotics, covering material selection, structural design, sensing, control, and actuation. In addition, we introduce the concept of DT for soft robots and discuss its technical approaches and integration in remote operation, training, predictive maintenance, and health monitoring. We address the challenges facing the field, map out future directions, and finally conclude the important role that FEA, ML, and DT play in shaping the future of soft robots.

This review presents a detailed survey of Dielectric Elastomer Actuators (DEAs) and their emerging role in medical applications. DEAs are distinguished by their flexibility, low weight, and excellent biocompatibility, making them well-suited for a wide range of medical devices. The review explores the fundamental electro-mechanical principles behind DEA operation, which enable their remarkable ability to replicate natural muscle movements. Key applications discussed include biomedical devices, rehabilitation systems, in-vivo implants, and wearable health monitors, where DEAs offer dynamic, lifelike movements and precise control. Their ability to provide highly flexible and responsive actuation is a major advantage in medical technologies. However, challenges persist, particularly in terms of material durability, the need for high-voltage activation, and the integration of DEAs with existing medical technologies. By synthesizing recent research and highlighting ongoing hurdles, this review emphasizes the transformative potential of DEAs, offering a comprehensive look at their current state and future impact on next-generation medical devices.

The advancement of information and energy technologies has spurred an increased demand for low-power and compact electronic devices with across various fields. Developing energy harvesting technologies to capture ambient and sustainable energy offers a promising solution to complement or replace conventional batteries. The piezoelectric technique provides a solution for energy harvesting from different energy sources, and high-frequency operation in piezoelectric energy harvesting offers several advantages. These include increased power output, as more charge is generated per unit of time, which increases the current. Additionally, better alignment with the natural resonance of piezoelectric elements enhances energy conversion efficiency. Considering the growing interest in efficient energy harvesting, a review of recent advancements in piezoelectric energy harvesting under high-frequency excitations and operations is presented in this paper. A brief introduction to the operating modes of piezoelectric energy harvester (PEH) is first introduced to provide a general understanding of energy conversion from the piezoelectric effect. PEHs under high-frequency operations from different energy sources are then reviewed and classified into three categories: wind, vehicle and train, and water flow. Next, novel ideas and structures to facilitate high-frequency operations for PEHs are summarized and discussed in detail. Subsequently, the working mechanisms for PEHs under high-frequency operations are described in detail and classified into three groups: high-speed rotation, frequency up-conversion, and friction-induced vibration mechanisms. Finally, applying advanced piezoelectric materials in novel structures and fostering application-oriented prototype testing are identified as trends for future development.

This review provides a comprehensive exploration of small-scale energy harvesting (EH) for low-power devices, covering various ambient energy sources such as human activities, solar, thermal, mechanical vibration, radio frequency (RF), magnetism, and temperature differentials. It explains the use of conversion mechanisms like piezoelectric, thermoelectric, pyroelectric, and triboelectric. The focus is on piezoelectric materials, particularly pyroelectric materials, delving into the fundamental principles and equations governing their operation. The mechanisms of piezoelectric and pyroelectric effects under mechanical loadings and temperature changes are also explained. The review addresses material selection for small-scale EH, discussing both inorganic and organic piezoelectric materials. It justifies the preference for lead-free materials like poly(vinylidene fluoride) (PVDF) due to its biocompatibility, mechanical flexibility, ease of thin film production, and cost-effective implementation, replacing toxic lead-based materials. The various polymorphs within PVDF are explained, emphasizing the β-phase as the one responsible for its highest piezoelectric property. Different methods to enhance β-phase content in PVDF are reviewed, with electrospinning highlighted as a one-step process eliminating the need for post-treatment steps. The research effort to fabricate PVDF-based EH devices with various techniques, dimensions, mechanical loadings, and excitations is thoroughly examined. Recent advancements in the Internet of Things and low-power devices have driven interest in device miniaturization and complex circuit module fabrication using microelectromechanical systems (MEMS) technologies. The review explores approaches for fabricating PVDF-based EH devices using MEMS techniques and discusses hybrid systems combining piezoelectric and pyroelectric effects, with PVDF as the conversion medium.

Soft actuators have become remarkably popular among numerous applications in rehabilitation and manipulation. Despite their numerous advantages, these actuators exhibit a significant limitation in grasping applications. Their inherently low stiffness, a characteristic of soft actuators, leads to considerable deformation when interacting with opposing forces. In this study, a jamming actuator has been integrated into the soft actuator to enable variable stiffness. The system's behavior has been modeled in both linear and nonlinear states, utilizing both the strain energy theory of hyperelastic materials and a novel hysteresis identification technique based on the Prandtl-Ishlinskii method. Moreover, the results have been validated with experiments. By adding a layer jamming actuator to the soft actuator, the newly structured robot can increase its stiffness up to nine times when the layer jamming is activated. If the layer jamming is deactivated, the robot behaves like a typical soft actuator. Moreover, as the test results indicate, the strain energy-based method shows a 6.3% deviation from the actual behavior in the linear range, while it was unable to accurately characterize the actuator's behavior in nonlinear states. In contrast, hysteresis modeling displays an 8.5% deviation from experimental data in both linear and nonlinear states. Overall, the combination of the layer jamming and soft bending actuator has resulted in a more versatile manipulator whose behavior could be modeled and anticipated with adequate accuracy considering both modeling techniques.

Magnetorheological fluid, as a novel intelligent composite material, possesses unique controllable properties in the presence of a magnetic field, thereby opening up new possibilities for its engineering applications. In this study, a novel parametric model is proposed to accurately describe the nonlinear hysteresis behavior of a magnetorheological fluid composed of micron-scale carbonyl iron particles. Experimental investigations involving large-amplitude shear tests, employing strain amplitudes of 10% and frequencies of 0.1 Hz and 1 Hz, were conducted at five different current levels (0A, 0.5A, 1A, 1.5A, and 2A) to determine the model parameters. The genetic optimization algorithm was employed to identify the optimal solution for the model parameters. Subsequently, the model parameters were generalized with respect to the applied current, and the relationship between these parameters and current variations was explored. Research findings demonstrate the superiority of the proposed model over existing models such as the Bouc-Wen model and the hyperbolic tangent model in accurately capturing the nonlinear hysteresis behavior of magnetorheological fluid. This study holds significant potential for predicting the nonlinear hysteresis behavior of automotive dampers and provides a solid theoretical foundation for semi-active suspension control.

Traditional epoxy thermosets cannot be reprocessed or recycled due to their permanent covalent cross-linking network. Covalent adaptable networks (CANs) emerge as a solution, endowing epoxy thermosets with recyclability, reprocessability and self-healing ability to tackle the recycling issue. Nevertheless, the existing covalent adaptable epoxy network exhibits low mechanical robustness, glass transition temperature and thermal stability. Herein, we have developed a covalent adaptable epoxy network based on dynamic amine terminated hyperbranched polyamide (AHPA) to fabricate catalyst-free and high-performance epoxy vitrimers. The incorporation of thermoactivated rearrangement of AHPA enables the obtained epoxy vitrimers to possess remarkable reprocessability, along with good thermal stability, high glass transition temperature and excellent creep resistance. The epoxy vitrimers can be easily reprocessed without compromising thermal and mechanical properties even after multiple cycles, presenting a promising design of dynamic hyperbranched polymers for constructing adaptive and recyclable epoxy thermosets for sustainable engineering applications.

This article proposes an elastic shear thickening polishing (ESTP) method that achieves noncontact, flexible polishing using abrasive grains within a shear thickening polishing liquid driven by mechanical tools. To address the challenge of accurately predicting and controlling the contour of material removal in ESTP, this study analyzes the microscopic mechanism of elastic shear thickening material removal. A material removal model for K9 optical glass was developed based on the Princeton equation, finite element simulations, and the convolution of material removal rates. This model comprehensively accounts for the effects of several key parameters: the inclination angle of the polishing tool head, the rotational speed of the polishing head, and the thickness of the elastic shear layer. These factors are integral in shaping the contour of material removal. By incorporating these variables, the model provides a detailed prediction of the material removal profiles under various polishing conditions. Experimental validations are conducted to compare the theoretical predictions against the actual outcomes. The results demonstrate that the proposed material removal profile model achieves an average relative error of 2.67% between the theoretical and experimental profile depths during elastic shear thickening polishing. Furthermore, the surface roughness of the polished workpiece was refined to 0.94 nm.

Advancement in intelligent materials for the past few decades enabled the development of functional morphing structures and robots operating in fluid environments. Fluid-structure interaction problems for functional morphing structures and robots were naturally accentuated. In this paper, the recent advancements in shape-morphing robots and structures in fluid flow across different Reynolds number scales are reviewed and summarized, from microrobots with Reynolds number of much lower than 1 to deformable aircrafts in turbulent flows. With the quest to improve the design and functionality of the morphing structures and robots, we discuss modeling methods, experiments, and materials for the morphing structures over a vast range of Reynolds number. The importance of understanding fluid-structure interaction to design morphing structures and robots are emphasized. Following up with several critical future questions to address, the potential applications of artificial intelligence and machine learning (AI/ML) techniques in improving the design of shape-morphing structures and robots are discussed. These shape-morphing structures are expected to have a significant impact in enhancing sustainable solutions for today's challenges, and exploring unknown of deeper oceans and outer space.

This work presents innovations in polymer science through the development of antimicrobial and reprocessable shape-memory vitrimers from biobased vanillin and glycerol acrylates, incorporating pentaerythritol tetrakis(3-mercaptopropionate). The addition of this thiol increased the viscosity of the resin and reduced shrinkage and rigidity, without significantly affecting the polymerization rate. Samples containing 20 wt.% of thiol exhibited self-welding and 40% self-healing efficiency after just 10 min of heating at 180 °C and without additional pressure, significantly improving mechanical properties. The ability of vitrimers to maintain a temporary shape and return to a permanent shape under temperature changes showed shape-memory behavior, making them suitable for medicine, electronics, and robotics. The mechanical properties remained consistent after three reprocessing cycles, highlighting the sustainability of the vitrimers. The antimicrobial activity of these vitrimers showed efficacy up to 100%, suitable for antimicrobial films, coatings, and 3D printed parts. Microimprint lithography enabled micrometer-scale patterns, highlighting broad practical applications of the vitrimers.

In this work, a new experimental shunt tuning procedure is proposed for piezoelectric shunts targeting a single vibration mode of a flexible structure. The procedure is based on measurements of the dynamic capacitance of the piezoelectric transducer, eliminating the need for additional sensors or actuators. It is demonstrated that all required electromechanical parameters for shunt calibration can be determined from two extremum points on the dynamic capacitance curve. Corrections for the influence of residual modes are included to enable the consistent application of existing tuning expressions for various shunt topologies. The hardware of a digital shunt is used for the measurement of the dynamic capacitance allowing for low-cost implementation. The proposed method is validated numerically and experimentally through the calibration of a series RL shunt using both analog and digital components.

Polydimethylsiloxane (PDMS) based ferroelectrets are an attractive material for the fabrication of human-based applications given their soft and compliant mechanical properties. The typical fabrication approach is to exploit specifically engineered void geometries fabricated by moulding and bonding layers together. Charge instability can be addressed by the addition of Polytetrafluoroethylene (PTFE) particles but this prevents the bonding of PDMS films. This paper illustrates a new approach to obtaining PDMS ferroelectret with random voids by promoting and trapping bubbles to create cavities within a PDMS film. A mathematical model is presented to explore the connection between the percentage of trapped bubbles in the PDMS and the equivalent piezoelectric coefficient, d_33e. The process is compatible with the addition of PTFE powder to the PDMS formulation and a ratio of PTFE to PDMS of 1:3 by weight was found to increase performance achieving an initial d_33e of 384 pC/N which reached a steady state value of 186 pC/N measured after 2 months after poling. The energy harvesting potential of the random void PDMS/PTFE ferroelectret was explored by cyclically compressing with a force of 300 N applied at 1 Hz. The output of the ferroelectret was found to charge a 10μF capacitor to 0.26 V after 40 s. The ferroelectret's performance as a pressure sensor from 0 to 300 N was explored, and the optimum formulation achieved a sensitivity of 25.2 mV/N, a nonlinearity of 4%, and hysteresis of 4.2%, demonstrating a considerable improvement over the pure PDMS ferroelectret.

Soft body armor composites are broadly utilized for individual security due to their light weight and flexible nature. However, they are not viable in halting high-velocity impact, particularly against impact at a near distance. Integrating shear thickening fluids (STFs) into these composites is a promising result of upgrading their impact resistance. This review article highlights the progress of improving the impact resistance of soft body armor composites due to the incorporation of STFs. It discusses the parameters affecting energy absorption, shear thickening fluid properties, rheological properties of STFs, mechanisms of energy dissipation during the impact, fabrication techniques of STF-fabric composites, ballistic test techniques, and challenges of ballistic performance evaluation and wearer consolation. This review paper incorporates previous research work for experimental and numerical simulation results.In general, the integration of STFs into softbodt armor composites showed noteworthy guarantees in theimpact resistance capabilities of soft body armor composites. The most frequent applications of softbody armor composites are security personnel, civilian applications, emergency response teams, private security, body guards, law enforcement, and the military.

Active (intelligent/smart) materials in engineering solutions are generally combined with other materials, and they are embedded in physical environments. In the current work, these kinds of systems are described as Soft-Hard Active-Passive Embedded Structures (SHAPES). The term emphasizes the interacting materials: In the same way as soft–hard is a spectrum of mechanical compliance, active–passive describes a spectrum of multi-field compliance, i.e., the strength of reaction to a non-mechanical stimulus like a temperature change or an applied electric field. SHAPES can be classified according to the interaction of the active and passive materials as having a Case I (the expansion of the active material is mostly constrained by the passive material), Case II (a combined deformation behavior ensues which is influenced by the active and passive materials) or Case III (the active material deforms freely with only negligible influence of the passive material) behavior. Various application concepts for SHAPES as actuators or for other applications – such as morphing, conductivity switching, sensing, connection-breaking, blocking, and material logic – are presented. Furthermore, the most common active materials that can be part of SHAPES are discussed with respect to their stimulus-responsivity. From these, design recommendations for SHAPES-like applications are derived. Two tables that give a comprehensive overview of relevant literature sources are provided. These tables serve as a snapshot of the currently applied materials and the realized concepts. They can serve as a starting point to add new and emerging materials. The unique focus of the present review is the classification of the interacting materials and how authors utilize the properties of the active and passive materials inside their composites. This allows the identification of gaps/shortcomings in the field and opportunities for new SHAPES designs.

This paper describes and evaluates the embedding of sensors and electronic sensor nodes into fiber metal laminate (FML) plates to achieve material-integrated, guided ultrasonic wave based structural health monitoring for hybrid materials. It evaluates how embedded electronics can enhance the process of sensor data acquisition and at the same time critically investigates the drawbacks that accompany the embedding approach regarding the influence on the received signal. A FML specimen with single sensors in one half of the plate and sensors with attached electronic sensor nodes for wireless readout in the other half is manufactured, introducing the detailed embedding process for such systems. Ultrasonic through-thickness scans of the manufactured plate are presented and analyzed to assess the achieved embedding quality. Together with electric sensor signals from both, wireless and wirebound micro-electromechanical system vibrometers and data from a scanning laser Doppler vibrometer (SLDV) the influence of material-integrated components on the wave propagation around the locations of integration is discussed. Further, the signals of wirebound sensors are successfully correlated with measurements performed using the SLDV and directly compared to data provided by wirelessly readout sensor nodes having the same type of sensor attached. This work shows how reflections occurring due to a material integration of components influence the recorded sensor data. At the same time, it is discussed how, for baseline-based damage detection methods, the influence of this is assumed to be a minor problem, and proof for advantages provided by the integration of complete sensor systems directly into the host material is provided.

This study investigates the synthesis, structural characterization, and dielectrophoretic alignment of potassium strontium niobate—KSN (KSr₂Nb₅O₁₅) particles to develop textured 0–3 piezocomposites with enhanced dielectric and piezoelectric properties for tactile sensor applications. KSN particles were synthesized using a molten salt process. Anisometric needle-like morphology of the particles were confirmed by electron microscopy and their single crystalline nature by electron diffraction techniques. Dielectrophoretic alignment under an alternating current (AC) electric field facilitated particle orientation along the [001] c-axis of the tetragonal structure, as confirmed by x-ray diffraction and quantified using the Lotgering factor (f_(00l)). An f_(00l) = 0.83 was achieved for piezocomposite containing 5 vol% KSN particles and cured under an AC field of 2 kV mm⁻¹ at 200 Hz. Electrical characterization revealed a strong correlation between particle alignment and properties. Compared to random piezocomposites prepared without AC field, increase in dielectric constant of up to two-folds, in polarization of up to ten folds, and in piezoelectric charge coefficient of up to 3 folds were observed in textured piezocomposites. A tactile sensor prototype developed using these textured piezocomposites exhibited voltage output proportional to particle orientation, demonstrating the importance of particle alignment in enhancing the functional properties of piezocomposites.

In the rapidly evolving automotive industry, the need for reliable and efficient pneumatic elastomeric components necessitates cutting-edge health monitoring methods, given that the pneumatic components are directly connected to dampening properties, ride comfort, vehicle safety, and stability. A novel approach for pneumatic elastomeric component health monitoring utilizing ionic liquid (IL)-based soft sensor technology has been proposed, which has the promise to enable real-time health monitoring and prognostics of vehicle systems. The proposed polymer sensor leverages the distinctive characteristics of flexibility, stretchability, and high sensitivity. These properties are critical for precisely measuring load, vertical displacement, air pressure, force locations, and load frequency of the air pressure responsive rubber part. The sensors are attached to three key contact positions: two on the metal cover of the elastomeric component and one on the piston of the component to provide critical information about the rolled and unrolled rubber. These sensors continuously measure essential parameters for health monitoring. The collected data can identify potential issues such as leaks, wear, or structural weaknesses. The finding illustrates the effectiveness of the IL-based soft sensors in providing precise and reliable data for health monitoring parameters.

Mechanical counterpressure (MCP) space suits could offer advantages over current gas-pressurized suits in safety, mobility, and decreased suit complexity and volume. However, a passive MCP space suit design poses challenges with donning and doffing as it must be exceedingly tight, requiring 29.6 kPa of MCP. Equipping the suit with wearable active devices, such as an expanding cuff, is a potential solution to this issue. These devices could allow the suit to loosen and tighten to aid in donning and doffing and to conform to changes in body geometry during movement. Dielectric elastomer actuators (DEAs) are a promising candidate for the active device element of an MCP space suit design due to their compliance, high energy density, long lifetime, and high bandwidth. The high voltage required to drive DEAs can be reduced by subdividing the dielectric layer of the DEA to create DEA multilayers (DEAMs). This work presents a DEAM-based MCP space suit cuff, a fundamental component of a full suit concept, that applies passive pressure through prestretch and loosens upon actuation for donning, doffing, and during movement. The cuff is fabricated using a batch-spray and stamp technique, and it consists of 24 active layers, each 200 µm in thickness, giving the cuff a total thickness of 6 mm including inactive encapsulation layers. The final cuff design achieves an MCP of 19.52 kPa, a maximum pressure relief of 5.42 kPa, and a response time of 0.7 s. The proposed design can achieve a counterpressure of 29 kPa with a prestretch factor of 2.42. These results demonstrate the capabilities of DEAM-based wearable devices, introducing novel actuation functionality to wearable technology.

Contact electrification is the primary mechanism dictating electron transfer and surface charge density for triboelectric nanogenerators (TENGs), making intrinsic material and physical surface properties key parameters for the interfacial charge transfer phenomena. Surface properties are governed by the morphological and textural microstructural features, including tribological interactions, topographical profiling, surface roughness, and real contact area. Therefore, understanding surface morphological effects on the triboelectric performance aids development towards adapting and optimizing surface properties. Particularly, in polymer-based composites TENGs, the surface morphology relies on polymer crystallization and interactions with reinforcing additives. This comprehensive study evaluated the effects of isothermal crystallization and the incorporation and dispersibility of raw and few-layer exfoliated muscovite mica fillers, insightfully realizing and tuning polyethylene oxide's intrinsic properties and semi-crystalline microstructure. The full material characterization presented dramatic variations in polymer growth kinetics, chain dynamics, lamellae profiling, surface roughness, and work functions, allowing the development of a constructive triboelectric surface microstructural design guide. The crystallization temperature of 65 °C with raw mica demonstrated the greatest dielectric properties and triboelectric performance resulting in a peak-to-peak voltage, peak-to-peak current density, transferred charge density, and power density of respectively, 488 V, 45.5 mA m⁻², 152 μC m⁻², and 24.0 W m⁻² at a load resistance of 6 MΩ. The TENG device demonstrated stable long-term voltage outputs over the duration of 12 000 contact-separation cycles and successfully self-powered natural resource environmental monitoring sensors.