Liquid metal stretchable electronics (LMSE) offer remarkable stretchability, softness, and self-healing properties, making them ideal for smart wearables and soft robotics. A key fabrication method involves spray deposition of LM into patterned structures. However, reliance on manual airbrush techniques has hindered understanding of how spray parameters impact LM deposition, limiting scalability and reliability. This work addresses these challenges by using an innovative automated spray coater (ASC), which provides precise control over deposition. For the first time, the ASC enables a systematic investigation of how spray parameters—such as flow rate, pressure, and spraying distance—affect LMSE properties like reliability, uniformity, and hysteresis. We find that rougher coatings improve yield (nearly 100\%), but compromise long-term reliability. Additionally, finer linewidth patterns (0.25 mm) fail earlier in cyclic testing and show reduced self-healing capabilities compared to wider lines ($>0.5 mm$). The ASC's capabilities are demonstrated through the fabrication of LMSE devices, including a 16-LED array and a large wearable strain sensor (70 × 150 mm) for human motion capture. This work provides crucial insights into LM deposition and highlights relevant applications, advancing the development of scalable and reliable stretchable electronics.

Detection of damage serves as the initial phase for autonomous healing or adaptation to damage in resilient robots. While signaling the occurrence of damage proves beneficial, the more critical requirement lies in localizing the damage to enable targeted actions. This article introduces a soft, self- healing damage localization sensor capable of detecting damage at four distinct locations using only a pair of measuring points (electrodes). The sensor comprises four resistive links, forming a resistive circuit, and operates by measuring the equivalent resistance between two fixed terminals. Damage occurring to each link induces a distinct change in the value of the equivalent resistance. The system is initially characterized at the material level and subsequently at the sensor level through multiple damage trials (the sensor can restore functionality through on-demand healing, achieved by applying a temperature of 90 degrees C for 30 min). Finally, the sensor is sandwiched between self-healing layers to form a skin. Out of the 16 damage trials conducted on the sensor, in standalone and embedded configurations, 15 successful localizations were observed. Additionally, reducing the number of electrodes enhances the ease of integration of this technology into various robotic applications, such as the palm of robotic hands.

While stretchable and compressible sensors are commonly used to enhance the proprioception and exteroception of soft robots, their sensitivity and sensing range are constrained by their design and stiffness, often limiting their measurement capabilities. To overcome this limitation, this paper presents a novel approach that integrates stiffness modulation with piezoresistive sensing, specifically Electrical Impedance Tomography (EIT). This approach results in a new type of artificial skin, termed Variable Sensing Range Electrical Impedance Tomography Skin (VEITS), which features an adjustable sensing range. The VEITS comprises two layers: a top layer with variable stiffness achieved through granular jamming and a bottom EIT sensing layer. By applying a vacuum to the granular layer, the sensor stiffness can be increased, enabling it to measure higher contact forces. Specifically, applying a vacuum pressure of -80 kPa extends the contact force range by 37% compared to the unpressurized state. While increased stiffness temporarily reduce sensitivity to small loads, it does not affect the EIT localization accuracy. The VEITS simple structure makes it suitable for large surface sensing on robots, and effectively expanding the sensing range. Though demonstrated with EIT and granular-jamming stiffness modulation, this novel principle is applicable to other strain-based sensors and stiffness modulation techniques.

While most soft pneumatic grippers that operate with a single control parameter (such as pressure or airflow) are limited to a single grasping modality, this article introduces a new method for incorporating multiple grasping modalities into vacuum-driven soft grippers. This is achieved by combining stiffness manipulation with a bistable mechanism. The system features a bistable dome structure with a central suction cup and a set of vacuum bending actuators. Designed and optimized using fluid and structure modeling in finite element analysis, it offers three grasping modes: two reflex mechanisms (force- and contact-triggered) and one with active control. All modes rely on the structural buckling of the bistable dome, but differ in how this snap behavior is activated, by force, contact, or active control. Adjusting the airflow tunes the energy barrier of the bistable mechanism, enabling changes in triggering sensitivity and allowing swift transitions between grasping modes. This results in an exceptional versatile gripper, capable of handling a diverse range of objects with varying sizes, shapes, stiffness, and roughness, controlled by a single parameter, airflow, and its interaction with objects.

Soft robotics has gained increasing interest recently, but challenges persist, including operating at low temperatures, susceptibility to damage, fatigue-induced deterioration, and the need for proprioceptive sensing for autonomous operation and recovery. Addressing these challenges, this paper introduces a novel conductive ionoelastomer with a dendritic microstructure engineered to resist crack propagation and self-repair even at sub-zero temperature, offering opportunities to construct more adaptable soft robotic grippers which can work in complex environments. Silk ionoelastomers, serving as the matrix material, are complemented by dendritic sodium polyacrylate crystalline fibers. This integration significantly enhances strength and Young's modulus, while maintaining low hysteresis (below 24%), high fracture toughness (35.2 kJ m-2), and a fatigue threshold of 754 J m-2. Furthermore, it exhibits exceptional self-healing capabilities at both room temperature and -20 degrees C, enabling its use in flexible sensors with elongation capabilities of up to 500%. Utilizing folding techniques and inherent self-healing properties, this material can be tailored into pneumatic fingers with sensing and damage-detecting functionalities, facilitating the grasping of various objects. Even when exposed to various types of external damage, its pneumatic functionality is fully restored through the self-healing process at room temperature or low temperature, underscoring its resilience and adaptability in practical applications. PSSFIE, featuring a dendritic microstructure, with enhanced strength, modulus, low hysteresis and high fatigue threshold, was developed to addresses soft robotics challenges like low-temperature self-healing, operation, and damage sensing.

More complex 3D structures were manufactured out of reversible covalent polymer networks using multi- material extrusion-based additive manufacturing. The rheological behaviour of reversible polymer networks based on the thermoreversible Diels-Alder reaction was optimized through the addition of different types of nanoclays and carbon black. The extrudability and deposition of the composites improved up to 5 wt% nanoclay, as the viscosity behaviour increased by an order of magnitude in the nozzle and by two orders of magnitude at the print bed temperature, without affecting the reversible gel transition temperature and the healing performance. An electrically conductive composite with 20 wt% carbon black and 1 wt% nanoclay could be printed using nozzle sizes down to 0.3 mm, resulting in higher resolution and accuracy than the pristine polymer networks. The percolating filler network enabled the printing of overhangs and hollow structures without the need for support material with perfect mechanical and electrical isotropy. Multi-material printing combining the electrically conductive and non-conductive composites enabled manufacturing self-healing deformation and force sensors that could recover their sensing performance upon damage healing at 90 degrees C for one hour.

Two dynamic covalent networks based on the Diels-Alder reaction were blended to exploit the properties of the dissimilar polymer backbones. Furan-functionalized polyether amines based on poly(propylene oxide) (PPO) FD4000 and polydimethylsiloxane (PDMS) FS5000 were mixed in a common solvent and reversibly cross-linked with the same bismaleimide DPBM. The morphology of the phase-separated blends is primarily controlled by the concentration of backbones. Increasing the PDMS content of the blends results in a dilute droplet morphology at 25 wt %, with a growing size and concentration of droplets and the formation of two separate PPO- and PDMS-rich layers at 50 wt %. Further increasing the PDMS content to 75 wt % leads to larger droplets and a thicker layer of the secondary phase. The hydrophobic PDMS phase creates a barrier against water, while the more hydrophilic PPO phase enhances the resistance against oxygen diffusion. Lowering the maleimide-to-furan stoichiometric ratio resulted in a decrease in cross-link density and thus more flexible and stretchable encapsulants. Changes in the stoichiometric ratio also affected the phase morphology due to resulting changes in phase separation and network formation kinetics. Lowering the stoichiometric ratio also resulted in enhanced self-healing properties of 96% at room temperature as a consequence of the increased chain mobility in the blended networks. The self-healing blends were used to encapsulate liquid metal circuits to create stretchable strain sensors with a linear electro-mechanical response without much drift or hysteresis, which could be efficiently recovered by 90% after the damage-healing cycles.

The rising popularity of soft grippers in industry is due to their impressive adaptability. Yet, this adaptability requires flexibility, which often sacrifices grip firmness and complicates sensor integration. This article introduces two additional innovations, variable stiffness and pneumatic sensing, into a FinRay adaptive gripper. The approach and design for incorporating these innovations are guided by requirements outlined by Festo. Regarding this, a layer-jamming-based variable-stiffness skin broadens gripper applications, manipulating objects of varying hardness and weight, while a pneumatic sensor skin detects contact and loss of contact. Both functionalities rely on the airtightness of the skins, which is compromised if damaged. To address this, both the skins and the gripper were crafted using self-healing polymers. The sensing capability and modulated mechanical performance of the gripper were evaluated experimentally and through simulations, and the self-healing ability was assessed by recharacterization after a damage healing. This work showcases the promising synergy between robotics and self-healing materials, demonstrating mutual reinforcement to a highly efficient gripping system.

The addition of an organo-modified nanoclay to a carbon black-based electrically conductive self-healing composite showed a synergistic improvement of the electrical conductivity and healing ability. The synergistic effect was studied as a function of carbon black (primary) and nanoclay (secondary) filler loadings in a Diels-Alder-based polymer network. The synergistic effect is the greatest when the carbon black particles are organized around the partially exfoliated nanoclay platelets. Too extensive exfoliation and dispersion of the nanoclay by ultrasonication result in a partial loss of electrical conductivity by around 18% and a substantial loss of the healing ability by around 84%, whereas the hybrid composite shows a very poor recovery of only 12% based on the strain at break. Second, the synergy is governed by the compatibility between the organic modifiers of the nanoclays and the backbone chemistry of the polymer network. The incorporation of Cloisite 15A with a hydrophobic modifier in a network based on poly-(propylene oxide) (PPO) results in a greater synergetic improvement of the electrical conductivity and healing behavior than the incorporation of a more hydrophilic nanoclay or the use of Cloisite 15A in a more hydrophilic poly-(ethylene oxide) (PEO) backbone. Finally, the effect of nanoclay as a secondary filler depends also on the chemistry and architecture of the polymer network. By using no platelets at 10 wt % carbon black, CB-filled composites built on PPO- and PEO-based networks show distinctly different electrical conductivities of 6.18 and 15.97 S m-1, respectively. The synergistic effect increases with a decreasing cross-linking density of the polymer network, especially by the improvement of the healing behavior. For instance, introducing Cloisite 15A enhances the mechanical healing efficiency of the PPO-based composite possessing a lower cross-linking density by 44% while diminishing it for the PEO-based composite by 33%. This study provides deep insights into the structure-property relationships that facilitate the optimization of electrically conductive and self-healing composites for a wide variety of applications, in particular for flexible electronics and soft robotic applications.

Soft fiber-reinforced actuators have demonstrated significant potential across various robotics applications. However, the actuation motion in these actuators is typically limited to a single type of motion behavior, such as bending, extending, and twisting. Additionally, a combination of bending with twisting and extending with twisting can occur in fiber-reinforced actuators. This paper presents two novel hybrid actuators in which shape memory alloy (SMA) wires are used as reinforcement for pneumatic actuation, and upon electrical activation, they create a twisting motion. As a result, the hybrid soft SMA-reinforced actuators can select between twisting and bending, as well as twisting and extending. In pneumatic mode, a bending angle of 40° and a longitudinal strain of 20% were achieved for the bending/twisting and extending/twisting actuators, respectively. When the SMA wires are electrically activated by the Joule effect, the actuators achieved more than 90% of the maximum twisting angle (24°) in almost 2 s. Passive recovery, facilitated by the elastic response of the soft chamber, took approximately 10 s. The double-helical reinforcement by SMA wires not only enables twisting in both directions but also serves as an active recovery mechanism to more rapidly return the finger to the initial position (within 2 s). The resulting pneumatic–electric-driven soft actuators enhance dexterity and versatility, making them suitable for applications in walking robots, in-pipe crawling robots, and in-hand manipulation.

Contribution: This article presents an engineering outreach activity that aims to teach K-12 students how to develop a tendon-based soft robotic finger. The primary objectives of this STEM activity are to introduce students to the fundamentals of soft robotics, its interdisciplinary nature, and to offer them a hands-on and engaging learning experience using the project-based-learning approach.Background: Soft robotics, an interdisciplinary field combining chemistry, materials science, and robotics, has the potential to revolutionize the design and development of robots. However, introducing the fundamental concepts of soft robotics to K-12 students can be challenging since traditional robotic activities often require complex programming, technical expertise and expensive equipment and software.Intended Outcomes: Increasing the students’ understanding of soft robotics principles, materials, and polymer processing. Positively impacting students’ perception of engineering as a potential career path by enhancing their attitudes toward STEM.Application Design: Students could develop manually actuated soft robotic fingers within a 45-min workshop by utilizing 3-D printed molds, rapidly curing elastomeric materials, and the basic mold casting method. The outreach activity is intentionally designed to simplify the technology used by eliminating the need for complex programming, and to focus on utilizing novel materials and basic concepts to construct actuating soft robots, providing an effective and engaging STEM activity for K-12 students.Findings: The success/effectiveness of the activity was evaluated in three ways: 1) through direct inspection on the performance of the student-fabricated soft finger during the workshop; 2) through the pre-and post-tests to evaluate the learning outcomes; and 3) by conducting a STEM outreach survey to gather student feedback on the quality of the outreach activity and their attitudes toward STEM. During the workshop activities, the students were able to effectively follow the instructions, construct a tendon-based soft robotic finger, and manually actuate the finger using the tendon. According to the results of pre-and post-tests, the students increased their understanding regarding the principles of soft robotics, materials and polymer processing. Furthermore, the STEM outreach survey of IEEE powered “TryEngineering Portal” revealed that the developed outreach activity enhanced the achievement of pedagogical and quality outcome goals and measures, as well as program targets and objectives.

Flexible, soft materials are increasingly used for the fabrication of soft robots, as the inherent compliance and shock‐absorbance protect the robot from mechanical impact. Soft universal grippers take full advantage of this adaptability, facilitating effective and safe grasping of various objects. However, due to their predominantly soft material composition, these grippers have limited lifetimes, especially when operating in unstructured and unfamiliar environments. The self‐healing universal gripper (SHUG) is proposed, which can grasp various objects and recover from substantial realistic damages autonomously. It integrates damage detection, heat‐assisted healing, and healing evaluation. Notably, unlike other universal grippers, the entire SHUG can be fully reprocessed and recycled. The gripper's functionality relies on the particle jamming of steel balls enclosed within a self‐healing membrane. Thanks to the thermoreversible covalent Diels–Alder bonds in self‐healing polymer membrane, the gripper is able to recover from macroscopic damages including scratches and punctures. Temperature‐assisted healing is regulated in a closed‐loop manner using an embedded thermocouple and Joule heater. Experimental validation demonstrates the adaptability, resilience, and recyclability of the SHUG.

The maximum number of damage and healing cycles that can be endured by a self-healing (SH) actuator, that is, its repeatable healability, has never been assessed. One reason for this is because healability was always tested manually. Typically, an operator uses a sharp blade to manually damage the actuator and places both damaged edges back together for them to heal. This process is time consuming, which explains why only a limited amount of damage and healing cycles has been reported. Moreover, this leads to an inaccurate estimation of how repeatable the actuator's healability is, since manual damage cannot be always performed at the exact same location. Therefore, we present a method to automatically and autonomously assess the repeatable healability of a soft SH actuator. It uses a robotic system composed of a damage station and a healing station, which the actuator is automatically moved between using a robotic arm. A sensor integrated inside the actuator is used alongside a dedicated characterization algorithm to automatically indicate whether the actuator is properly damaged and healed. We present a typical use case of the system, performing and analyzing 63 damage cycles of a selfhealing Diels–Alder actuator. After 53 cycles, the actuator will never properly heal again, therefore, we consider this cycle to be the maximum repeatable healability of the tested actuator. The healability of the SH Diels–Alder actuator is, therefore, not infinite experimentally.

Self-healing soft robots show enormous potential to recover functional performance after healing the damages. However, healing in these systems is limited by the recontact of the fracture surfaces. This paper presents for the first time a shape memory alloy (SMA) wire-reinforced soft bending actuator made out of a castor oil-based self-healing polymer, with the incorporated ability to recover from large incisions via shape memory assisted healing. The integrated SMA wires serve three major purposes; (i) Large incisions are closed by contraction of the current-activated SMA wires that are integrated into the chamber. These pull the fracture surfaces into contact, enabling the healing. (ii) The heat generated during the activation of the SMA wires is synergistically exploited for accelerating the healing. (iii) Lastly, during pneumatic actuation, the wires constrain radial expansion and one-side longitudinal extension of the soft chamber, effectuating the desired actuator bending motion. This novel approach of healing is studied via mechanical and ultrasound tests on the specimen level, as well as via bending characterization of the pneumatic robot in multiple damage healing cycles. This technology allows soft robots to become more independent in terms of their self-healing capabilities from human intervention.

Soft strain sensors with high sensitivity and the ability to recover from damages are required in the emerging field of self‐healing soft robotics. Herein, printable supercapacitive strain sensors that can heal upon moderate heating (75 °C for 10 min) and exhibit a 30 times higher sensitivity than PDMS‐based sensors are developed. For the sensor's core layer and electrode, a nitrile‐functional polysiloxane that contains an active ionic initiator and can heal by siloxane equilibration at elevated temperatures is used. Supercapacitive strain sensors prepared from the elastomer are highly sensitive at low strains of 0–30%, enabled by the electric double‐layer formation of the ionic initiator. After healing, the sensors exhibit nearly unaltered performance in tensile testing. Due to the thermoreversible nature of the elastomer network, patterned core layers with different microstructures can be printed by direct ink writing. The capacitive sensors based on these microstructured films reach a higher sensitivity and linearity than those based on unstructured films. Finally, the sensor is integrated into a soft robotic finger and the sensor's ability to determine the bending angle is validated by motion capture. This technology can provide new opportunities to equip soft robotic devices with custom‐printed, healable strain sensors.

Soft robotics modeling is a research topic that is evolving fast. Many techniques are present in literature, but most of them require analytical models with a lot of equations that are time consuming, hard to resolve, and not so easy to handle. For this reason, the help of a soft mechanics simulator is essential in this field. This article presents a tutorial on how to build a soft-robot model using an open source finite element analysis (FEA) simulator, called SOFA. This software is able to generate a simulation scene from a code written in Python or XML, so it can be used by people with different fields of competence, like mechanical knowledge, knowledge of material properties, and programming skills. As a case study, a Python simulation of a cable-driven soft actuator that makes contact with a rigid object is considered. The basic working principles of SOFA required to make a scene are explained step by step. In particular, this article shows how to simulate the mechanics and animate the bending behavior of the actuator and the importance of knowledge of the constitutive material properties for good modeling of the mechanical system. Furthermore, we will also show how to retrieve and save data from simulation, demonstrating that SOFA can easily adapt to a multidisciplinary subject, such as research in soft robotics, but can also be useful for teaching simulation and programming language principles to engineering students.

The recent introduction of self-healing soft materials in robotics is a major step towards sustainable next generation robots. By manufacturing soft robots out of these smart materials, we integrate a self-healing ability and increase the physical intelligence of these systems. However, the embodied intelligence in the material level needs to be augmented by incorporating assistive mechanisms in the system level with minimized control, enabling healing of damage in different sizes and in diverse working conditions. These assistive mechanisms can provide damage detection, damage closure, healing stimuli providing, health monitoring, or a combination of the previous. In this paper, we present two different mechanisms for an autonomous healing of damages; (i) Embedding a healable heater in a self-healing soft actuator to increase the temperature required for an efficient healing, while it allows detecting the damage and monitoring the health of the system. (ii) Incorporating shape memory alloy wires in a self-heling soft bending actuator, with simultaneous sealing through contraction and heating abilities. Apart from assisting in the healing action, both mechanisms play a part in the actuation of the bending robots as strain limiting elements. These assistive mechanisms will overcome the limitation on the material level, leading to robots that can self-heal in applications outside of laboratories and factories.

Designing soft robots that have greater toughness and better resistance to damage propagation while at the same time retaining their properties of compliance is fundamentally important for soft robotics applications. This study's main contribution is proposing a framework for nonlinear multimaterial architectural design of soft structures to increase their toughness and delay damage propagation. What are the limits when combining significantly different materials in one structure that will delay crack propagation while significantly maintaining postdamage toughness? Through this study, we observed that there is a very dynamic interplay when combining significantly different materials in one structure; this interplay could weaken or strengthen the multimaterial structure's toughness. In biological evolutionary terms, the Pangolin, Seashell, and Arapaima have found their answer for deflecting the crack and maintaining strength in their bodies. How does nature put these multimaterial structures together? Our research led us to find that the multimaterial toughness limits depend largely on the following parameters: components' relative morphology, architecture, spatial distribution, surface areas, and Young's Modulus. We found that a linear geometry, when it comes to morphology and/or architecture relative to surface area in multimaterial design, significantly reduces total toughness and fails to delay crack propagation. In contrast, incorporating geometric nonlinearities in both morphology and architecture significantly maintains higher total toughness even after damage, and significantly delays crack propagation. We believe that this study can open the door to further research and ultimately to promising and wide applications in soft robotics.

Self-healing polymers render their life cycle more sustainable by recovering their properties upon healing. Intrinsic self-healing polymers can be recycled, which further reduces waste production. Yet, despite these intrinsic benefits, several sustain-abi l i t y issues remain largely neglected, including the use of fossil-derived materials, hazardous chemicals, and material management at the end of its life. Herein, we report a series of castor oil-based self-healing elastomers that account for these challenges and show improved mechanical and self-healing capabilities compared to the other bio-based self-healing materials. Castor oil was functionalized using a simple, one-pot, solventless synthesis from renewable resources and cross-linked by Diels-Alder cycloaddition. They can be reprocessed and recycled or hydrolytically degraded at the end of their service life. The mechanical properties of the material s can be tuned (Young's modulus 0.5-20 MPa), with a fracture strain of up to 487%. A fracture strain of 100% could already be recovered after just 60 s at room temperature and 75% of the mechanical properties after just 24 h. By taking advantage of these properties, a sof t pneumatic gripper has been developed, capable of healing autonomously, which is fully recyclable and degradable. Hence, we provide a sustainable alternative for sof t robotic applications and for self-healing elastomers in general .

The addition of an organomodified nanoclay to a carbon-based electrically conductive self-healing composite showed a synergistic improvement of the electrical conductivity and self-healing ability of the formed hybrid composites. The effect on the electrical, viscoelastic, and self-healing properties was studied for Diels-Alder-based reversible polymer networks with a maleimide-to-furan stoichiometric ratio of 0.6, with different loadings of carbon black Ensaco 360G and nanoclay Cloisite 15A. Hybrid composites were prepared with carbon black contents from 5 to 15 wt % and nanoclay loading up to 1.5 wt %. The percolating network of conductive particles led to decent electrical conductivity of the order of 0.46 S m(-1) at carbon black loadings of 7.5 wt % and higher. The increase in electrical conductivity was most pronounced at the lowest carbon black loadings, while the improvement of the self-healing properties was most pronounced just above the percolation threshold. The resulting structure-property relations enabled optimization of the filler composition to achieve the best combination of electrical and self-healing properties by exploiting the synergistic effect of the secondary filler. Finally, the electromechanical properties of selected hybrid composites with the best combinations of the two fillers were studied for sensor applications. Self-healing strain sensors showed distinct responses depending on the combination of fillers with decent recovery after the damage-healing process. These promising results suggest the use of the studied electrically conductive and self-healing hybrid composites for deformation and damage-sensing applications in flexible electronics and soft robotics.