Electronic skins (e-skins) are flexible, stretchable films that mimic some of the functionalities of human skin, including the ability to sense pressure, temperature, humidity, and motion. They have a wide range of potential applications, from wearable technology and soft robotics to healthcare and prosthetics. While e-skins need to be flexible and stretchable, finding materials that can withstand repeated stretching and flexing without degradation over time is challenging. Moreover, for continuous monitoring, e-skins must operate efficiently with minimal power consumption and developing low-power sensors that can be embedded in e-skins is a significant challenge. Furthermore, producing e-skins on a large scale at a reasonable cost while maintaining high quality and performance is difficult. The manufacturing process must be compatible with the delicate materials and sensors involved. Additionally, integrating e-skins into wearable devices in a way that is comfortable for the user remains also a challenge. The ideal e-skin must be breathable, lightweight, and have minimal impact on the user’s daily activities. Addressing these challenges is essential for their successful integration into practical applications. To this account, a new study published in Chemical Engineering Journal by Dr. Mohammad Zarei, and Professor Seung Goo Lee from the Department of Chemistry at University of Ulsan in South Korea alongside Dr. Jung Hoon Kim and Dr. Joong Tark Han from the Nano Hybrid Research Center at Korea Electrotechnology Research Institute, the researchers fabricated and evaluated the performance of a biodegradable, breathable, flexible, and electrically modulated all-leaf capacitive electronic skin (e-skin) for gesture recognition and human motion monitoring. Their experimental approach encompassed material synthesis, structural and electrical characterization, sensitivity testing, and application demonstration. The team initiated their study by synthesizing oxidized single-walled carbon nanotubes (Ox-SWCNTs) and incorporated them into silver nanowire (AgNW) networks. The utilization of Ox-SWCNTs and AgNWs as the conductive materials, combined with the structural integrity of leaf skeletons as the substrate, marks a significant departure from traditional e-skin materials. This novel approach of employing leaf skeletons enhances the biodegradability and environmental sustainability of the e-skin and also leverages the natural microstructural properties of leaves to improve the sensor’s performance while incorporating Ox-SWCNTs, which are known for their ultra-high thermal conductivity, into the AgNW network will significantly enhance the electrical and thermal properties of the electrodes, thereby improving the e-skin’s sensitivity and stability.

After fabricating the electrodes, the researchers performed a series of characterizations. Scanning Electron Microscopy (SEM) analyses revealed the hierarchical microstructure of the leaf skeletons, including the presence of non-smooth microdomes and protuberances that contributed to the e-skin’s sensitivity. They showed that incorporation of Ox-SWCNTs into the AgNW network to significantly improve thermal stability and electrical conductivity, which was crucial for the AgNW-based electrodes’ performance. They demonstrated the pivotal role of Ox-SWCNTs in tuning the electrical properties of the AgNW network using electrical modulation experiments. Additionally, by adjusting the concentration of Ox-SWCNTs, they were able to optimize sheet resistance and electrical conductivity, and achieved the optimum balance at 4 wt% concentration. This approach allowed for the modulation of sheet resistance and electrical conductivity, thereby enhancing the e-skin’s ability to accurately detect and monitor a wide range of pressures and motions. Indeed, the e-skin demonstrated high sensitivity and excellent linearity across a broad range of pressures, making it suitable for both subtle physiological monitoring and dynamic motion detection.

The core of their experimentation focused on testing the e-skin’s sensitivity and performance in detecting pressure variations and monitoring human motions. According to the authors, the e-skin exhibited high sensitivity (0.86 ± 0.16 kPa⁻¹) across a wide sensing range (0.01–97 kPa), with excellent linearity for low- and high-pressure regimes. This high sensitivity allowed the e-skin to accurately monitor subtle human physiological signals such as exhalation and vocal cord vibrations, as well as detect and monitor human motion with high precision. Moreover, the team conducted several demonstrations to showcase the practical applications of their e-skin. These included attaching the e-skin to various body parts (e.g., joints like elbows and knees, and the throat) to monitor different types of human movements and physiological signals. The e-skin successfully detected joint movements, including flexion and extension, and was able to monitor vocal cord vibrations indicative of speech patterns. Additionally, they tested the e-skin’s performance in a humid environment and its breathability and showed its robustness and comfort in wearable applications. It is noteworthy to mention, the authors’ work highlights the potential of using plant-based materials and nanotechnology in creating advanced wearable electronics. In 2024, drawing insights from this study, the authors published an advanced article in the Chemical Engineering Journal focusing on the development of a biodegradable all-leaf piezoresistive e-skin with enhanced sensitivity and improved breathability. This innovative e-skin, designed for tactile sensing and physiological monitoring, is disposable, biodegradable, and exceptionally breathable, making it well-suited for analyzing subtle human movements and comprehensive physiological signals. Its remarkable breathability, ultra-low limit of detection (LOD ~0.27 Pa), and enhanced sensitivity (19.75 ± 1.50 kPa⁻¹, <3 kPa) are facilitated by its high electrical conductivity, structural porosity, and effective interlayer contacts. The piezoresistive e-skin developed, characterized by the highly porous structure of leaf skeletons, exhibits exceptional breathability and permeability to both vapor and water. This attribute holds significant importance in ensuring comfort when worn. Enhanced sensitivity is achieved through optimized stress concentration, increased contact area, and improved stress distribution across interlocking vein arrays within multiple layers. These features empower the piezoresistive e-skins to effectively monitor a wide range of stimuli, spanning from low to high pressure levels. In conclusion, the application potential of the developed e-skins by Dr. Zarei, Professor Lee, Dr. Kim, and Dr. Han is vast, ranging from healthcare and rehabilitation to robotics and human-machine interfaces.

Multiagent systems (MASs) have become an essential part of modern engineering and technology. These systems, made up of multiple interacting agents, are used in everything from robotics to autonomous vehicles to sensor networks. One of the key ideas in MAS research is formation control which involves ensuring that all the agents stay in a specific geometric pattern while moving toward a shared goal. This ability is important for MASs to work effectively in areas like coordinating drones, exploring underwater environments, or helping in disaster response efforts. But as MASs increasingly rely on networks and digital controls, they face growing risks from cyber-physical threats. One of the biggest challenges MASs face today comes from cybersecurity issues, like denial-of-service (DoS) and deception attacks because DoS attacks interrupt communication channels which make it hard for agents to exchange information while deception attacks are even trickier because they involve altering or corrupting data in ways that often go unnoticed. Scientists have studied these attacks and developed defenses against them, most of these threats aim to disrupt or destabilize the system outright. They typically do not involve the kind of subtle manipulation that can redirect MAS’s behavior while keeping its structure intact. To this note, a team of researchers led by Dr. Junlong Li from North University of China investigated a different kind of threat called induced attacks. Unlike traditional cyberattacks, these attacks do not try to destroy an MAS but Instead, they focus on influencing its behavior in a way that is hard to detect. With the inserting carefully crafted signals into the system, an attacker can guide the MAS along a new trajectory while ensuring that its agents stay in formation. This makes induced attacks especially because they can take advantage of the system’s own robustness to achieve hidden objectives.

In their experiments, the researchers created a simulated environment with six agents, all operating under predefined dynamic rules. These agents were designed to maintain a specific formation pattern while moving along a trajectory. This setup was meant to mimic real-world applications, like coordinating drone fleets or guiding autonomous vehicles. To make the simulation more realistic, the authors gave the agents diverse starting conditions which allowed them to observe how the system would behave in both normal and challenging situations. A key part of the experiment was introducing an induced attack which they did by using a specially designed system called an exosystem, which targeted a few agents in the group. However, unlike typical cyberattacks that often cause chaos or destabilize systems, this attack made sure the overall formation stayed intact. This difference was important because it showed how induced attacks could work quietly in the background. The results were striking—the MAS could be subtly manipulated to follow a specific trajectory chosen by the attacker. This proved the effectiveness of the attack generation exosystem, revealing how easily an attacker could control the entire system without breaking its structure. Such a covert attack poses serious challenges for traditional detection systems. The study also showed that the induced attack successfully guided the MAS along the desired path while keeping all agents in agreement. This precision was made possible by a regulated attack matrix that carefully managed the attack signal’s dynamics. The experiments further highlighted the attack’s robustness, even when the agents started in varied initial states or when formation parameters were adjusted. The MAS consistently followed the imposed trajectory, proving just how precise and powerful the approach was. Interestingly, the attack made use of the system’s own control mechanisms, making it even harder to detect. We believe one important discovery the authors made was how the MAS dynamics could be separated into two components: one governing the overall formation and the other managing individual interactions. This separation allowed the attack to influence the group as a whole without disturbing the agents’ local behavior. The researchers also confirmed that the robust H∞ framework played a vital role in making the system stable and predictable under attack. Even more concerning, their experiments showed that attackers could customize the MAS’s trajectory to meet their specific goals, making the implications of this study even more far-reaching.

To wrap things up, the research led by Dr. Junlong Li and his team has brought to light a critical vulnerability in MASs that has far-reaching implications for robotics, cybersecurity, and beyond. The study showcased how induced attacks can subtly manipulate the paths of MASs while keeping their structural formations intact. This makes induced attacks particularly dangerous in high-stakes scenarios like search-and-rescue missions, military operations, or autonomous vehicle fleets where undetected manipulation could have serious consequences. The findings push us to rethink how MAS control systems are designed. While current control frameworks do a good job of defending against standard cyberattacks, Dr. Junlong Li et al. shows that even robust systems can be quietly exploited. This realization calls for a shift in focus toward creating systems that can detect and counteract subtle changes in their behavior. It also highlights how important it is to factor in security right from the start when designing these systems, rather than trying to bolt on fixes later.

On a broader scale, this study sheds new light on how adversaries might approach cyber threats. It demonstrates that induced attacks are not just theoretical—they can be executed with precision if attackers have enough knowledge about a system’s structure and control mechanisms. This highlights the need to secure critical information like control parameters and system topology to reduce potential risks. The research also makes it clear that we need better detection strategies that can catch slight deviations in system behavior, even when everything seems to be running smoothly. We believe the practical implications of this work are vast. Industries relying on MASs for essential operations need to start thinking about how these systems might be manipulated in ways that are nearly invisible. For instance, in logistics, an induced attack could reroute a fleet of autonomous vehicles without breaking formation, causing significant disruption. Similarly, in defense, a covert adjustment to a drone formation’s trajectory could compromise a mission without setting off any alarms.

Electrorheological (ER) fluids exhibit dramatic changes in their mechanical and rheological properties when exposed to an electric field. These smart fluids, often consisting of a suspension of polarizable particles in a non-conducting fluid, can swiftly transition from a liquid-like state to a solid-like state upon the application of an electric field. This unique property makes ER fluids highly valuable in a variety of applications where controlled motion and force are required.  However, challenges remain in the widespread adoption of ER fluids. The stability of the suspension, particle sedimentation, and the high electric fields required to achieve significant changes in viscosity are some of the issues that need addressing.  To this account, a new study published in the ACS Applied Materials and Interfaces and conducted by Miss. Sai Chen, Professor Yuchuan Cheng, Mr. Zihui Zhao, Dr. Ke Zhang, Dr. Tingting Hao, Professor Yi Sui, Professor Wen Wang, Professor Jiupeng Zhao, and Professor Yao Li from the Harbin Institute of Technology, Chinese Academy of Sciences, Queen Mary University of London, researchers investigated the significant impact of nanocarbon-based materials on ER fluids. They synthesized and analyzed the ER behavior of barium titanate@nanocarbon shell (BTO@NCs) composites, incorporating carbonized polydopamine (C-PDA) into the shell.

The team synthesized BTO@NCs composites using a two-step process. First, BTO nanoparticles were coated with PDA by stirring BTO nanoparticles in a Tris-HCl buffer solution, to which dopamine hydrochloride was added. The mixture’s pH was adjusted to 8.5 using sodium hydroxide, and the reaction was allowed to proceed for varying durations (12, 24, and 48 hours) to control the thickness of the PDA coating. These PDA-coated nanoparticles were then carbonized at 500 °C under an inert atmosphere to form the nanocarbon shell, resulting in BTO@NCs composites with different shell characteristics. Afterward, the synthesized BTO@NCs nanoparticles were dispersed in silicone oil to create ER fluids. The concentration of nanoparticles and the preparation method (mechanical stirring and ultrasonication) were carefully controlled to ensure a homogeneous dispersion of nanoparticles within the silicone oil, forming the basis for subsequent ER behavior testing.

The authors performed comprehensive material characterization to analyze the morphology, structure, and composition of the synthesized BTO@NCs nanoparticles and the ER fluids and used transmission electron microscopy and high-resolution TEM to visualize the morphology and nanostructure of the BTO@NCs, revealing the core-shell structure and the variation in shell thickness. They also used x-ray diffraction to confirm the crystalline structure of BTO within the composites, which indicated that the carbonization process did not alter the BTO’s crystal structure, X-ray photoelectron spectroscopy to provide information on the surface chemistry, which showed variations in the sp²/sp³ carbon ratio and the presence of surface functional groups, which are important for the ER response and they used Fourier transform infrared spectroscopy to identify the specific functional groups present on the nanocomposite’s surface, which provided additional confirmation for the presence of polar groups that facilitate ER behavior and Raman Spectroscopy was employed to analyze the degree of graphitization, with changes in the I_D/I_G ratio indicating how the polymerization time influenced the carbon structure.

The research team also evaluated the ER performance of the fluids, where they measured the relationship between shear stress and shear rate under varying electric fields, revealing how the ER fluids transitioned from a liquid to a solid-like state using flow curve analysis. Dielectric Measurements assessed the dielectric properties of the ER fluids, important for understanding the polarization behavior under an electric field and rheological measurements were used to quantify the yield stress and viscosity of the ER fluids, indicating the strength of the ER effect under different conditions. The authors found that the optimal polymerization time to be 24 hours, producing BTO@NCs with the best combination of shell thickness, sp²/sp³ carbon ratio, and surface functional groups, leading to the highest ER response. Moreover, the ER fluid containing BTO@NCs synthesized with 24 hours of polymerization exhibited a maximum yield stress of 2.5 kPa at an electric field strength of 4 kV/mm, significantly higher than that of BTO@NCs synthesized with shorter or longer polymerization times. Furthermore, the increased content of sp³ C-OH and oxygen-containing functional groups within the shell was attributed to the enhanced ER response, indicating that these groups play a crucial role in achieving rapid polarization and strong ER effects. Additionally, comparative analysis with SiO₂@NCs and TiO₂@NCs ER fluids also prepared showed enhanced ER behavior, confirming the effectiveness of the approach for high-performance ER fluids based on nanocarbon composites.

The novelty of this study lies in its approach to manipulating the carbon structure of C-PDA to optimize ER response, highlighting the importance of sp²/sp³ -hybridized carbon structures and the role of surface polar functional groups in achieving rapid polarization. The control over polymerization time allowed for the fine-tuning of shell thickness and the functional group density on the nanocarbon shell, which in turn influences the ER fluid’s properties. This level of control is important for the development of smart materials that can respond predictably to external stimuli. In conclusion, the work of Harbin Institute of Technology scientists exemplifies the cutting-edge research in nanomaterials and their applications in creating functional and adaptive materials.  The study conducted on the development of BTO@NCs composites for use in ER fluids carries substantial significance across multiple domains of materials science, engineering, and applied physics.   ER fluids with enhanced properties as demonstrated in this study can be used in a wide range of applications, from automotive systems (such as adaptive suspensions and clutches) to haptic feedback devices in virtual reality systems and soft robotics. Improved ER fluids could lead to more efficient, responsive, and durable systems. There is potential for these advanced ER fluids to be used in the development of novel medical devices, such as artificial muscles or other devices that require precise control of movement and stiffness. This could lead to innovations in prosthetics, orthotics, and other assistive technologies. Moreover, improved ER fluids can lead to more energy-efficient systems in automotive and industrial applications, potentially reducing energy consumption and environmental impact.

Metamaterials, structures with properties distinct from their constituent materials, have garnered significant attention for their potential to revolutionize various fields, from mechanical engineering to material science. In a recent publication in the Journal ACM Transactions on Graphics by Liane Makatura, Bohan Wang, Yi-Lu Chen, Bolei Deng, Chris Wojtan, Bernd Bickel, and led by Professor Wojciech Matusik, an innovative approach to metamaterial design has been introduced. The study introduces a novel procedural graph representation for cellular metamaterials, enabling the design of a plethora of structures with diverse material properties and architectures.

Metamaterials, by their very nature, offer properties that defy conventional materials, exhibiting behaviors not found in nature. These materials hold tremendous potential for engineering applications, including tunable compliance, non-reciprocal behaviors, and impressive strength-to-weight ratios. The key to harnessing these properties lies in the architectural arrangement of the constituent elements, known as cellular architectures. However, exploring the myriad possibilities of cellular architectures poses significant challenges due to the diversity of architectural elements and the absence of a unified representation.

Existing approaches to representing cellular architectures each have their limitations. Generic voxel grids offer versatility but lack efficiency when editing or representing complex structures. On the other hand, architecture-specific approaches provide compact and editable descriptions but are often incompatible with each other. For instance, trusses and beams are described by graphs, solid bulks by constructive solid geometry (CSG) operations, and shellular structures using surface meshes or implicit functions. Such incompatibilities hinder holistic exploration across architectural classes.

Even within a given class, challenges abound. The design of shellular metamaterials, particularly those based on triply periodic minimal surfaces (TPMS), poses difficulties. Existing methods involve intricate mathematical functions or approximations, limiting the accessibility of these structures to engineers and researchers. To address these challenges, a novel procedural graph representation is proposed.

The heart of the proposed approach is a compact and intuitive procedural graph that encapsulates the construction process of various cellular metamaterial structures. This representation employs a skeleton annotated with spatially varying thickness to capture the diverse elements present in metamaterials. For instance, straight and curved beams are captured through lines with smoothness annotations, while shells are represented by surface skeletons accompanied by thickness profiles.

A notable contribution of this study is the automated version of the conjugate surface construction method (CSCM) for TPMS. Traditionally, constructing TPMS involved intricate human interventions and expertise. The proposed automated CSCM pipeline democratizes TPMS design, making it accessible to a wider audience. This innovation is poised to open avenues for the use of TPMS-based metamaterials in various applications, from bone scaffolding to thermal energy management.

The potential of the introduced procedural graph representation is evident in its ability to span various architectural classes and material properties. Through a user study, the ease of use and intuitiveness of the representation are affirmed, promising a more efficient and dynamic design process for engineers and researchers. The representation’s versatility enables the generation of novel structures that can be tailored to specific needs.

The study also hints at exciting future prospects. Guided search strategies and validation of physical properties are areas ripe for exploration. The potential to create structures with tailored material properties, using simple random exploration schemes, showcases the representation’s power.

In a nutshell, the new study by Professor Wojciech Matusik and colleagues presents an innovative approach to metamaterial design. The procedural graph representation, with its compactness, intuitive nature, and automated TPMS construction, is poised to reshape the field of metamaterial engineering. Its ability to span diverse architectural classes and create structures with varied material properties opens new doors for innovation and exploration. As the study unveils the potential of this representation, it also highlights the uncharted territories awaiting future researchers and engineers.

The Robotic Flying Fish is a bio-inspired machine designed to emulate the remarkable locomotion of its biological counterpart, the flying fish. This creation stands as a testament to the power of biomimicry in robotics, where the principles of nature are harnessed to engineer robots capable of transcending the boundaries between water and sky. One of the most captivating features of the Robotic Flying Fish is its propulsion system. Powerful oscillations, akin to the movements of a real fish, are used to achieve high-speed swimming. The incorporation of these oscillations enables the robot to navigate the underwater domain with exceptional efficiency and agility. This design choice emphasizes the importance of imitating nature’s fluid dynamics to excel in aquatic environments. The swimming motion testing phase of this project is of paramount importance, as it serves as the foundation for the robot’s aquatic performance. The experiments conducted within a water tank, measuring 5 meters in length, 4 meters in width, and 1.5 meters in depth, have unveiled the impressive capabilities of the Robotic Flying Fish.

In a new study published in the peer-reviewed journal Bioinspiration & Biomimetics, scientists from Peking University, in collaboration with Taiyuan University of Technology, have reported an exciting development in the field of aquatic-aerial robotics. Led by Dr. Di Chen, Dr. Zhengxing Wu, Dr. Yan Meng, and Professor Junzhi Yu, along with Dr. Huijie Dong, their work focused on designing a robotic flying fish with the capability to seamlessly transition between water and air, a feat inspired by the remarkable locomotion of natural flying fish. To achieve high-speed swimming, the research team designed a propulsion system that mimics the undulating motion of a fish’s body. This design choice, combined with bilateral symmetry and slight positive buoyancy, imparts remarkable stability during high-speed swimming. Notably, the robot maintains partial buoyancy, allowing it to rely primarily on self-propulsion rather than buoyancy for acceleration. This innovation aligns with the overarching goal of achieving dynamic and agile underwater locomotion.

The results of the swimming motion tests are truly remarkable. As the actuation frequency increases, the swimming speed of the robot proportionally rises. The peak swimming speed reached an impressive 1.43 meters per second, equivalent to 5.42 body lengths per second. This achievement underscores the significance of designing a propulsion system that faithfully emulates nature’s mechanisms, as it is through this mimicry that such high-performance swimming is made attainable. Comparatively, it is important to note that the swimming speed, although impressive, has decreased slightly when compared to previous work due to the implementation of morphing pectoral fins. These fins were integrated to address the challenges posed by cross-domain locomotion. While this modification impacted the swimming speed, it also opened doors to exciting possibilities in other areas.

The pinnacle of this project’s success lies in its ability to perform cross-domain motion seamlessly. The transition from water to air, a feat rarely achieved in the realm of robotics, is the true litmus test for an aquatic-aerial robot like the Robotic Flying Fish. To validate the cross-domain capability of this robotic marvel, the authors conducted experiments with a specific focus on the intricate phases involved in transitioning from the underwater realm to the aerial domain. A robotic arm, equipped with two degrees of freedom, was ingeniously employed to assist in this challenging endeavor. The gripping mechanism of the arm, coupled with precise adjustments in pitch angle, enabled controlled and strategic movements. The experiment involved several phases, including underwater acceleration, water-air interface crossing, and the deployment of wing-like pectoral fins. The entire process was meticulously documented using high-speed cameras. What transpired was a mesmerizing display of technology and nature seamlessly merging into one.

The Robotic Flying Fish achieved a vertical leaping motion and wing spreading, precisely imitating the biological flying fish’s behavior. The robot’s ability to accomplish this cross-domain feat in a controlled and efficient manner heralds a significant advancement in aquatic-aerial robotics. It is the first of its kind to genuinely possess the capability of ‘fish leaping and wing spreading’ cross-domain locomotion. While the primary focus of this robotic creation is its aquatic prowess, the engineers behind the Robotic Flying Fish were also curious about its gliding capabilities. In nature, flying fish utilize a combination of swimming and gliding to cover significant distances above the water’s surface. Replicating this behavior in a robotic system poses considerable challenges.

During the gliding motion, the robot’s pitch angle increased gradually due to the wing’s pitch moment. This unique design choice aimed to mimic the characteristics of real flying fish. However, due to certain design limitations, such as the absence of an extra pair of pelvic fins for better longitudinal stability, the gliding distance was relatively short. It is essential to recognize that this exploration of gliding motion represents the first step in understanding the robot’s potential beyond aquatic environments.

Recognizing the need for further improvement in gliding performance, the engineers turned to simulation analysis. The goal was to optimize the gliding distance by dynamically adjusting the sweepback angle of the pectoral fins, a critical aspect of the robot’s design. The simulation conducted by the authors involved the use of a double Deep Q-Network (DQN) control strategy, with a reward function designed to reflect improvements in gliding distance. The training of the network was rigorous, comprising 30,000 episodes. The results of this simulation were promising. It was demonstrated that the gliding distance could be improved by dynamically adjusting the sweepback angle. The optimization control strategy effectively enhanced the gliding performance, offering a 7.2% increase in the maximum gliding distance. Their findings are of significant importance as they highlight the potential for further enhancements in gliding capabilities through meticulous control strategies and design optimizations.

The journey of creating the Robotic Flying Fish by Professor Junzhi Yu and colleagues is a testament to the fusion of engineering ingenuity and nature’s designs. It has yielded a robot that showcases remarkable aquatic speed, precise cross-domain motion, and initial gliding capabilities. However, this journey is far from over. One cannot overlook the considerable challenges that come with imitating the complex locomotion of flying fish. While the robotic flying fish achieved a notable swimming speed of 1.43 meters per second and executed cross-domain locomotion with finesse, the gliding performance still lags behind its biological counterpart. In conclusion, the Robotic Flying Fish is a remarkable engineering achievement that exemplifies the power of biomimicry in robotics. It serves as a bridge between the aquatic and aerial worlds, offering a glimpse into the future of autonomous cross-domain locomotion.

For cybersecurity, vulnerabilities continue to evolve, and one such vulnerability that has gained significant attention is Rowhammer. Rowhammer is a hardware reliability concern that arises when an attacker repeatedly accesses specific DRAM (Dynamic Random-Access Memory) rows to cause unauthorized changes in physically adjacent memory rows. This vulnerability become a critical concern in the field of computer security due to its potential to enable various attacks, including privilege escalation, sandbox escapes, and breaking cloud isolation. The severity of the Rowhammer problem lies in its exploitation of fundamental DRAM circuit behavior, where bit flips occur when a specific DRAM row is repeatedly activated and precharged, resulting in electromagnetic interference between the aggressor row and its neighboring rows, causing cell capacitors in victim rows to leak faster than normal operation and eventually leading to bit flips. Rowhammer is not confined to specific DRAM generations; it affects both DDR3 and DDR4 DRAM memories making it a widespread and persistent concern across various hardware platforms. Over the years, numerous Rowhammer mitigation techniques have been proposed, both at the hardware and software levels. However, existing mitigation schemes have proven to be either ineffective against emerging Rowhammer attacks or have incurred substantial implementation overhead.

The research study recently published in the peer-reviewed Journal IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, led by Dr. Tyler K. Bletsch from Duke University, Dr. Biresh Kumar Joardar, and Professor Krishnendu Chakrabarty from Arizona State University, marks a significant advancement in the ongoing battle against Rowhammer. The authors developed a machine learning (ML)-based technique designed to provide fast and efficient detection of various Rowhammer attacks while maintaining low hardware overhead. They proposed  a solution which leverages machine learning models to analyze memory access patterns and distinguish between benign memory accesses and malicious Rowhammer attacks. By using this ML approach, the authors have successfully developed a Rowhammer mitigation solution that offers several notable advantages. First, the ML model can accurately detect various Rowhammer attacks, including the recently proposed TRRespass, Half-double, and Blacksmith attacks, which have posed challenges to existing mitigation techniques. Secondly, the proposed solution is implemented entirely on-chip, which means it does not require significant external hardware resources, making it a practical and cost-effective choice for mitigating Rowhammer attacks. Moreover, the experimental analysis demonstrated that the ML method introduces significantly lower power and area overhead compared to existing mitigation techniques like Graphene and Blockhammer, with 5% and 19% lower power overhead, respectively.

The researchers’ experimental evaluation of the ML-based Rowhammer mitigation technique provided compelling evidence of its effectiveness. The researchers conducted their experiments using the Gem5 simulator, which emulates a four-core system with various DRAM configurations. They employed a diverse set of applications, including benign applications and Rowhammer attacks, to evaluate the proposed solution’s performance and accuracy.  The experiments revealed that Rowhammer attacks were successful on commercial DDR4 DRAMs, even with Targeted Row Refresh (TRR) enabled. This highlights the urgent need for more effective mitigation techniques.

A critical aspect of the study was the creation of a diverse dataset that included both benign memory access patterns and Rowhammer attacks. Memory-access traces were collected using cycle-accurate simulations on the Gem5 platform. The ML model demonstrated high accuracy in detecting Rowhammer attacks while introducing minimal performance overhead. It outperformed existing techniques in terms of area and power overhead, making it a practical choice for implementation. Moreover, the ML solution exhibited robust performance even when HCfirst (the number of hammers required to cause a bit flip) was as low as 10K. This suggests that the model can adapt to future reductions in HCfirst, which is expected as DRAM technology advances.

The authors’ findings represent a significant step forward in addressing the persistent threat posed by Rowhammer attacks. By harnessing the power of machine learning, the researchers have developed a practical and efficient mitigation technique that not only detects various Rowhammer attacks but also minimizes power and area overhead. The scalability of this solution ensures its relevance in future hardware development.

Construction robots have become indispensable in various construction tasks, such as heavy component hoisting, laying, and installation. These robots are classified into two main categories: underactuated and full-actuated systems, each presenting unique challenges and requiring specialized control strategies. In this editorial, we delve into the complexities of underactuated robots, with a focus on their nonholonomic constraints and associated control methods. Additionally, we explore the benefits of full-actuated control systems and their applications within the construction industry. Underactuated construction robots, such as hoisting cranes, are characterized by nonholonomic constraints, making direct control of certain state variables challenging. This constraint, coupled with dynamic coupling complexities and uncertain configurations in state space, poses significant challenges for researchers and engineers. Tower cranes, gantry cranes, and bridge cranes are prime examples of underactuated systems commonly used in construction. To tackle these challenges, researchers have developed various control strategies for underactuated construction robots: firstly, the energy (Passive) Control Method where energy shaping control addresses actuator saturation, enabling precise positioning of lifting cranes while minimizing load swing. Coupling signals and antisway controllers, designed based on passivity analysis, enhance the relationships between system variables and improve robustness. Enhanced coupling control methods have been derived for 3D underactuated bridge cranes, significantly enhancing control performance. Secondly, motion planning method which involves designing controllers based on predefined reference trajectories. Offline trajectory planning and input-shaping methods are commonly employed to address complex coupling between crane motions. Real-time modified trajectory planning methods improve control performance and robustness.

In a recently published paper in the peer-reviewed  Journal of Field Robotics  titled “A review for control theory and condition monitoring on construction robots,” led by Huaitao Shi, Xiaotian Bai, Yixing Zhang, Linggang Min, Dong Wang, Xinyu Lu, Yang Yan from Shenyang Jianzhu University in collaboration with Ranran Li from Northeastern University and Yaguo Lei from Xi’an Jiaotong University, the authors discussed the control theory and condition monitoring of engineering robots. They emphasized the importance of control methods based on driving models and state monitoring in fault detection, intelligent maintenance, and fault tolerance control. This comprehensive review provided fresh perspectives and insights for future studies in Construction Robots (CR)  control models and state monitoring, contributing to the body of knowledge in construction industrialization and construction robots.

The authors discussed the Optimal Control Method and mentioned in their paper that linear models are often employed in motion planning, with optimal control algorithms introduced to handle disturbances and enhance system robustness. Pseudospectral time-optimal motion planning methods consider load swing suppression and transportation efficiency. Optimal control methods effectively restrain load swing during transmission, resulting in improved control effectiveness. On the other hand Intelligent Control Method, in their expert opinion fuzzy control, neural networks, and genetic algorithms are utilized to develop intelligent control strategies. These methods eliminate the need for precise system models, enhancing speed response and synchronization control. Challenges include theoretical stability proof and simulation-based validation, especially in the face of changing system parameters. Combining multiple control methods is a common approach to meet the increasing complexity of underactuated systems and their control requirements.

The researchers also discussed the Full-Actuated Control Systems in Construction Robots and they are distinguished by their high-order, nonlinear, and mechanically complex systems with multiple degrees of freedom. These systems offer precise control and find applications in various construction tasks, such as wall-laying robots, power-assisted robots for gypsum board installation, and floor tile laying robots.

According to the authors, control methods commonly used in full-actuated construction robots include PID-Based Control where Proportional-Integral-Derivative (PID) control ensures high-speed and stable control, guaranteeing robustness and reliability. Secondly Machine Vision (MV)-Based Control where MV systems aid in defect identification, object detection, and sorting, contributing to automation in construction. Both position-based and image-based visual servo systems control robot movements. Thirdly, Multisensor Information Fusion which combines cameras, sensors, and computing power to interpret images and make informed decisions. It is crucial for tasks involving complex and uncertain working environments.

Control strategies for construction robots vary depending on their actuation type. Underactuated robots contend with nonholonomic constraints, intricate dynamics, and uncertainties, necessitating energy-based, motion planning, optimal, and intelligent control methods for effective operation. In contrast, full-actuated systems offer precise control and find applications in various construction tasks, commonly employing PID-based control, machine vision, and multisensor information fusion. As construction robots continue to evolve and tackle more complex tasks, the integration of these control strategies and the development of new ones will play a pivotal role in enhancing their performance, reliability, and efficiency within the construction industry.

The construction industry has witnessed a gradual shift from traditional on-site construction to industrialization and off-site construction methods over the past decade. The authors believe advancements in construction industrialization technology have led to increased productivity and environmental sustainability, particularly in complex construction sites. This trend is expected to persist, despite challenges related to skill variations and collaboration issues across different construction processes. In this context, CR have emerged as a transformative solution in the high-tech sector, addressing skill disparities and cost challenges. CR has revolutionized construction by improving production efficiency, reducing costs, and enhancing worker safety.

In summary, the authors offered an excellent comprehensive overview of CR technology, its development trajectory, and the challenges it faces. It underscores the ongoing refinement of CR’s actuator control, position compensation, and state monitoring methods as crucial for advancing its applicability in construction industrialization, ultimately enhancing its benefits for the industry’s evolution.

Personalized health monitoring is an exciting and rapidly expanding field that has the potential to revolutionize the way we manage our health. The primary objective of personalized health monitoring is to provide individuals with real-time information about their health status, empowering them to make informed decisions regarding their care. However, one of the key challenges in this field is the development of wearable sensors that are comfortable, affordable, and easy to use. Traditional wearable sensors are often bulky, uncomfortable, and expensive to manufacture. Thankfully, 3D printing technology holds great promise for addressing these challenges. By enabling the rapid and cost-effective fabrication of complex structures with high precision, 3D printing offers a potential solution for creating wearable sensors that are comfortable, affordable, and user-friendly. Carbon nanotubes (CNTs) have emerged as a promising material for the development of wearable sensors. CNTs possess exceptional properties such as high conductivity and flexibility, which make them ideal for integration into 3D printed structures. This capability allows for the fabrication of wearable sensors capable of monitoring various physiological parameters like heart rate, blood pressure, and temperature.

In a recent study published in the journal Advanced Functional Materials, researchers Andy Shar, Phillip Glass, and Professor Daeha Joung from Virginia Commonwealth University, in collaboration with Dr. Sung Park from the Korea Institute of Industrial Technology, set out to develop and thoroughly characterize a one-part, highly conductive, flexible, and stretchable carbon nanotube (CNT)-silicone composite that could be 3D printed. The research team successfully developed an ink that met all the desired criteria, demonstrating high conductivity, flexibility, stretchability, and biocompatibility suitable for health monitoring applications.

The researchers achieved this by dispersing carbon nanotubes in a silicone precursor and employing sonication to create a one-part CNT-silicone ink. Notably, this ink exhibited remarkable conductivity and a low percolation threshold of carbon nanotubes. It could be directly printed onto diverse substrates like glass, paper, fabric, and even skin without requiring any surface modification or adhesion promoter. Additionally, the ink allowed for the creation of self-supporting structures with intricate geometries, such as helices, coils, and pyramids, and high aspect ratios. After curing under ambient conditions within 24 hours, the ink formed a flexible and stretchable composite that exhibited heightened sensitivity to both tensile and compressive stimuli. The composite also demonstrated high thermal conductivity and a low thermal expansion coefficient.

Furthermore, the composite acted as a heating element capable of generating heat up to 80 °C with a 5V voltage, maintaining a stable temperature for over 10 minutes. It also proved valuable for water distillation, as it could evaporate water and produce pure water. Additionally, the composite functioned as a dual temperature sensor and Joule heating element, accurately measuring temperature changes of external heat sources and regulating its own temperature by employing different voltages. The composite’s applications extended to fabricating wearable electronics for health monitoring, including motion detection, cardiac monitoring, and respiratory monitoring. Impressively, it demonstrated good biocompatibility with human skin cells, causing no irritation or inflammation upon attachment. Moreover, the composite showcased exceptional durability during repeated bending, stretching, washing, and wearing tests, making it a promising candidate for personalized health tracking and bionic skin applications. This capability allows for the printing of patient-specific patterns and shapes directly onto the skin.

However, it is important to note that while increasing the carbon nanotube loading significantly enhances the electrical conductivity of the CNT-silicone ink, it simultaneously decreases its mechanical properties. Consequently, optimizing the carbon nanotube loading is crucial to achieve the desired balance between electrical conductivity and mechanical performance. To ensure its suitability for health monitoring applications, the researchers conducted biocompatibility tests in vitro, confirming that the CNT-silicone ink poses no harm to patients. They also employed the ink in 3D printing a wide range of sensors and devices, such as ECG and EEG electrodes, pressure sensors, temperature sensors, and a wearable device capable of simultaneously monitoring multiple physiological parameters. These outcomes highlight the potential of 3D printing conductive elastomers for personalized health monitoring applications.

The developed one-part CNT-silicone ink is a versatile material with extensive applications, offering the ability to 3D print a wide array of sensors and devices. Furthermore, its biocompatibility makes it suitable for use in implantable devices. The research team is actively working on refining the CNT-silicone ink for health monitoring applications, with a focus on improving its electrical and mechanical properties. They are also exploring new methods for 3D printing conductive elastomers and investigating the ink’s potential in other areas such as wearable electronics and robotics.

In conclusion, this groundbreaking research has successfully developed a one-part CNT-silicone ink that is highly conductive, flexible, stretchable, and 3D printable. The ink can be printed on various substrates and used to create self-supporting structures with high aspect ratios and intricate geometries. The resulting composite exhibits high sensitivity to mechanical and thermal stimuli, and it can function as a heating element, water distiller, and dual temperature sensor. Moreover, the composite is biocompatible, durable, and suitable for fabricating wearable electronics for health monitoring and bionic skin applications. The ongoing efforts of the research team continue to refine and optimize the CNT-silicone ink for personalized health monitoring applications, highlighting its potential to transform the field of healthcare.

A kinetic responsive facade (KRF) is a type of building envelope that can move or change shape in response to environmental conditions such as wind, sun exposure, or temperature. These facades are designed to be dynamic and can adjust their configuration to optimize energy performance, occupant comfort, or aesthetic appeal. The importance of kinetic responsive facades in engineering lies in their potential to improve the energy efficiency and sustainability of buildings. By adjusting to changing environmental conditions, these facades can help regulate temperature and reduce the need for mechanical heating or cooling. Additionally, they can allow for more natural light and ventilation, improving indoor air quality and reducing reliance on artificial lighting. Kinetic facades also have aesthetic benefits, providing a dynamic and visually engaging element to the building exterior. They can be designed in a variety of shapes and sizes, creating a unique and customizable appearance for each building.

KRF mechanism allows architects to design a building with dynamic styles and morphological acclimation to improve indoor comfort and building energy efficiency. Despite the growing popularity of KRF design and simulation, its application in architectural design remains relatively low because it is built on rigid mechanical systems requiring considerably higher manufacturing and repair costs. KRF systems with large and heavy structural movements can induce more noise and vibration, causing occupant discomfort. Another critical challenge in the design and construction of KRF is the increasing complexity of buildings.

Soft robotic technology continues to attract research attention for architectural applications. Several studies have explored using this technology to address the inherent challenges in designing KRF. Generally, soft robots are tailored to mimic the complex functioning of living organisms. Unlike traditional rigid-body robots, soft robots have numerous advantages, including reduced complexity, improved human safety, reduced risks, and improved mechanical performance. They are also suitable for performing specific tasks requiring scalable and adaptive motion in unstructured surroundings.

With the growing evidence of the potential benefits of smart ad smart materials in constructing adaptive self-shaping KRFs, attention has shifted to finding suitable materials for improving performative responsibility of building and active engagement of façade morphology in climate-adaptive operation. However, despite the remarkable progress, the functional inadequacies of soft materials like weak shape retention, degradation of elastic resilience and system vulnerabilities are often ignored. This is of great significance in advancing the architectural KRF application. Therefore, it is desirable to bridge the existing research gap between advances in applied design and soft robotic fabrication technologies.

To address these challenges, Professor Hwang Yi and his team, Professor Je-sung Koh, Mi-jin Kim, and Baek-gyeom Kim from Ajou University investigated the feasibility of a lightweight, gearless and flexible flexural biomimetic climate-adaptive shading façade module. This module was compatible with nonlinear surfaces and was obtained by hybridizing smart SMA and gripper-shaped pneumatic elastomer actuators. Design procedures were presented to facilitate the design of building scale FEA and SMA actuators for stable soft KRF control. The design feasibility was validated via finite element analysis. Their work is currently published in the peer-reviewed research journal, Automation in Construction.

The authors showed that the resulting system exhibited improved shape-changing capability and structural stability for robust control of soft KRF. The compliant inflation mechanism, together with the thermomechanical response of the bias SMA actuation, achieved morphological flexibility in the design scheme as well as a maximum opening area ratio of ~20%. Through hybridization strategy, pleated FEA obtained adequate to overcome stress relaxation of the SMA actuator. When pleated FEA was deactivation, the tensional force induced by the heated SMA actuator was responsible for the panel membrane recovery. Furthermore, pneumatic control enhanced the flexibility of elastomer actuators in geometric deformation. Inflated elastomers produced greater force than SMA actuators, which is significant in building scalable applications.

In summary, the study addressed questions about the application of KRF in free-form buildings. The results revealed that a combination of two or more soft actuator systems could complement each function to reduce system complexity and improve operational performance. Stable mobility, design feasibility, and structural endurance were some of the performance benefits f the proposed approach. In a statement to Advances in Engineering, Professor Hwang Yi noted that the study findings would provide a better understanding of the bio-inspired design and construction of climate-responsive buildings.

The performance of engineering systems is often susceptible to the effects of disturbances. For example,  the additional force added to the nominal control inputs due to collisions or actuator faults is a common disturbance for robotic systems. These disturbances cause perturbations to the dynamics of the systems and may lead to  degradation to its desired performance. Numerous control methods, such as adaptive, fault-tolerant, and disturbance observer-based controls, are available for suppressing the detrimental effects of these disturbances. There are also some extrinsic force sensors available to directly measure the additional forces. Since these sensors are typically very expensive, it is imperative to develop accurate disturbance estimation methods to replace these sensors currently in use.

Conventional approaches to disturbance estimation rely on observers to reconstruct the unknown system states and inputs using measurable outputs like perturbation observers. Although observer-based methods can recognize the disturbance and formulate extended state observation problems, they experience several drawbacks limiting their practical applications. Lately, sliding mode observers (SMO) have emerged as a promising solution for solving the inadequacies of observer-based methods. SMO uses high-frequency switching to promote the balance between the high gains and error dynamics stability to obtain robust disturbance estimation. However, it is challenging to achieve a balance between the chattering attenuation and the robustness.

Significant research efforts have been devoted to addressing these inherent challenges hindering the development of robust and highly effective. For example, the integral sliding mode observer (ISMO) is an improved version of SMO, which has been used in various robotic systems. However, the assumption considered in its development does not hold for generic robotics. Similarly, the boundary layer method commonly used to avoid the chattering phenomenon suffers from small gains and low estimation accuracy. Thus, a detailed analysis of the effects of the boundary layer on the estimation method is needed.

Herein, Postdoctoral fellow Dr. Zengjie Zhang from The University of British Columbia, Professor Dirk Wollherr from Technical University of Munich and Professor Homayoun Najjaran from the University of Victoria developed a novel force-sensor-less method for robust disturbance estimation for robotic systems. This new method is based on ISMO, which was used as a second-order differentiator for system position measurement. The passivity property was substituted with a general assumption to address the nonlinearity and discontinuity problems and extend the ISMO to general second-order robotic systems. Furthermore, the boundary-layer method was adopted to attenuate the chattering phenomena and analyze its influence on the estimation accuracy via Lyapunov method. Their research work is currently published in the peer-reviewed, International Journal of Robust Nonlinear Control.

The research team reported the estimation of the system states and disturbances without using the velocity and force measurements explicitly. Unlike the conventional SMO methods, the boundary layer analysis of ISMO is of great significance owing to the effects of the cascaded sliding mode structure. The influence of the boundary-layer method on the system states and the disturbance estimation accuracy was established and presented in analytical forms as a reference for parameter selection.

The authors validated the feasibility of the proposed continuous ISMO through a simulation case involving a robot manipulator. It exhibited several advantages, including improved estimation accuracy and responsiveness, compared to conventional methods. Additionally, it could be extended to a wide range of robotic systems, expanding its application scope. The results indicated the potential application of continuous ISMO to specific force-perception tasks for second-order and force-sensor-less robotic systems.

In summary, the researchers reported the generalization of the ISMO method based on proposed general assumptions and its extension to more general systems to overcome the limitations of observer-based methods. The obtained results are important in determining feasible parameters for the designed parameters to meet the prerequisites of practical tasks. In a statement to Advances in Engineering, corresponding author, Professor Homayoun Najjaran said that the study provided new perspectives for accomplishing advanced robotic tasks using force-sensor-less methods.

Due to their exceptional qualities, such as high energy density, voltage, and extended lifespan, lithium-ion batteries (LIBs) have emerged as the preferred option as electric vehicles have gained popularity. To ensure safety, optimal performance, and longevity, LIBs must operate within a specific temperature range because they are sensitive to temperature variations. For LIBs, the ideal operating temperature is typically 20°C to 30°C. The battery’s life and capacity may decline if it is operated outside of this range, posing risks of thermal runaway that endanger human safety. Therefore, keeping the battery temperature within the proper range requires the implementation of efficient thermal management systems (TMS).

Traditional TMS strategies, such as air and liquid cooling, have their respective advantages and drawbacks. Air cooling, while simple and energy-efficient, is limited in its cooling capacity and temperature control accuracy, resulting in temperature fluctuations. On the other hand, liquid cooling provides better heat transfer efficiency and temperature control accuracy but requires complex systems with high maintenance costs. In a new study published in the peer -reviewed Journal, Applied Thermal Engineering, Professor Xiangbo Cui from Hunan University of Technology and PhD candidate Tete Hu from Central South University proposed a novel thermal management system based on thermoelectric cooling (TEC) that overcomes the limitations of existing approaches, providing rapid cooling, high control accuracy, and enhanced energy optimization.

The research team proposed thermal management system based on the principles of thermoelectric cooling, which utilizes the Peltier effect to create refrigeration. This system involves forming a circuit of two P-N semiconductors with different thermoelectric effects, applying DC power, and achieving efficient cooling. The first step in the development of the new system is the construction of a heat transfer model based on thermal resistance, which takes into account the influence of heat sinks and fans. This model is crucial for accurately predicting and optimizing the cooling performance. Subsequently, a distributed battery thermal model is developed using the finite difference method, considering the heat generation features of the battery tabs and their impact on temperature distribution. This refined modeling approach ensures improved accuracy in predicting temperature profiles.

Next, the authors created a thermal management model by integrating the heat transfer model and the distributed battery thermal model. To achieve robust and precise temperature control, Professor Xiangbo Cui and Tete Hu proposed a robust nonlinear model predictive control (NMPC) strategy based on neural networks (NN). Unlike traditional control methods, this strategy accounts for system uncertainty arising from parameter diversities in battery packs due to manufacturing inconsistencies, as well as external factors like electromagnetic noise, measurement errors, weather conditions, traffic congestion, and driving style. To validate the performance of the proposed thermal management system, a series of experiments is conducted under different operating conditions. The test results demonstrate the accuracy and effectiveness of the system, with temperature errors remaining below 0.5°C compared to the desired reference value during all test cycles.

The proposed robust predictive BTM strategy offers several key benefits, firstly, enhanced Safety: By precisely regulating the battery temperature within the optimal range, the risk of thermal runaway and associated safety hazards is significantly reduced. Secondly, improved Efficiency: The use of TEC in the thermal management system allows for rapid cooling and accurate temperature control, leading to increased energy optimization and overall efficiency. Thirdly: stability and Robustness: The incorporation of robust NMPC with neural networks ensures system stability and compensates for uncertainties, providing a reliable and robust control strategy. Lastly: better Model Accuracy: The distributed battery thermal model, considering the heat generation features of battery tabs, results in improved model accuracy, further enhancing temperature prediction and control.

The new study presented the significance of thermal management in the context of electric vehicle battery safety and efficiency. The proposed robust predictive BTM strategy, based on thermoelectric cooling, offers superior cooling capabilities, precise temperature control, and energy optimization. The integration of a distributed battery thermal model and robust NMPC using neural networks enhances system accuracy and stability, while experimental verification confirms the system’s effectiveness. Implementing this advanced thermal management system in electric vehicles will undoubtedly contribute to safer and more efficient battery operation, further propelling the adoption and widespread use of electric vehicles in the future.

Hydrogels are three-dimensional networks of hydrophilic polymers that can absorb and retain large amounts of water. Hydrogels have attracted considerable attention for various applications in materials science and biomedical engineering, such as tissue scaffolds, actuators, drug delivery systems, and wearable devices. However, conventional hydrogels often suffer from low mechanical strength and toughness, which limit their practical use. To overcome this limitation, double-network hydrogels have been developed, which are a class of hydrogels with a unique structure consisting of two networks: a primary network that provides mechanical strength and a secondary network that imparts toughness to the hydrogel. The primary network is typically composed of a rigid, cross-linked polymer network, such as polyacrylamide, while the secondary network is composed of a flexible, linear polymer, such as sodium alginate, that is physically interpenetrated within the primary network. The utility of double-network hydrogels lies in their unique combination of mechanical properties, which make them well-suited for a variety of applications. The primary network provides stiffness and mechanical strength, while the secondary network provides toughness and resistance to fracture. As a result, double-network hydrogels can be stretched to high strains without breaking and can absorb large amounts of energy without undergoing catastrophic failure. Double-network hydrogels have been used in a variety of applications, including tissue engineering, drug delivery, and soft robotics. In tissue engineering, double-network hydrogels have been used as scaffolds to support the growth of cells and tissues due to their mechanical properties and biocompatibility. In drug delivery, double-network hydrogels have been used to encapsulate and release drugs in a controlled manner. In soft robotics, double-network hydrogels have been used to create soft, flexible robots that can adapt to changing environments. The double-network hydrogels composed of biocompatible sodium alginate (SA)/polyacrylamide (PAM) are a promising class, which are typically cross-linked with calcium ions (Ca²⁺). However, the use of calcium salts as cross-linkers typically induces structural inhomogeneity and reduces the mechanical properties of the resultant hydrogels. Therefore, there is a need for a novel strategy to fabricate homogeneous and high-performance SA/PAM double-network hydrogels for flexible devices.

In a new study published in the peer-reviewed Journal of Materials Chemistry A, by Ms Xiaohui Zhang, Dr. Huimin Geng, Ms Xunhui Zhang, Prof. Yaqing Liu, Prof.  Jingcheng Hao and Prof. Jiwei Cui from Shandong University, it demonstrated that by pre-seeding calcium carbonate (CaCO₃) microparticles into SA/PAM double-network hydrogels, and subsequently inducing the release of calcium ions from the microparticles in an acidic solution, it is possible to improve the mechanical characteristics and strain sensitivity of the hydrogels for use in the development of highly sensitive wearable sensors. Engineering of ultrasensitive wearable sensors involves the design, development, and integration of advanced materials, electronics, and signal processing techniques to create wearable devices that can detect and measure physiological or environmental signals with high sensitivity and accuracy. Ultrasensitive wearable sensors have a wide range of potential applications, including monitoring of health and wellness, early detection of disease, and environmental monitoring. They can provide continuous, non-invasive monitoring of physiological and environmental signals, enabling early detection of changes and interventions before they become more serious.

The key innovation in Professor Jiwei Cui’s study is that the researchers pre-seeded CaCO₃ microparticles into the hydrogel, which could release calcium ions when exposed to acidic solution. The calcium ions subsequently cross-linked the SA chains to form sacrificial ionic bonds that enhanced the mechanical strength and toughness of the hydrogel. The mechanical properties of the hydrogel could also be tuned by changing the amount of CaCO₃ microparticles and the trigger time of the acid solution. The hydrogel showed excellent self-healing and fatigue resistance due to the reversible nature of the ionic bonds. Moreover, the hydrogel had high electrical conductivity due to the presence of mobile ions in the network, which enabled it to function as a strain sensor. The hydrogel strain sensor had high sensitivity, wide detection range, fast response time, and good durability under various environmental conditions. The sensor could monitor human motions such as finger bending, wrist rotation, facial expression, and speech recognition with high accuracy and reliability. The sensor could also be integrated with other electronic components to form flexible circuits that could be attached to various substrates such as paper, cloth, or skin. Furthermore, the sensor could be applied to monitor the pain signal induced by an in-situ cascade reaction at a wound site in a diabetic rat model. The pain signal was detected by measuring the change in resistance of the sensor before and after injecting glucose oxidase and azithromycin into the wound, which triggered a chemical reaction that produced reactive oxygen species that caused inflammation and pain in the wound tissue. The inflammation and pain induced a contraction of the wound tissue, which increased the strain and resistance of the sensor, indicating a pain signal that could be quantified by measuring the resistance change. The authors indeed provided a controllable strategy to engineer stretchable and tough double-network hydrogels for potential applications in flexible devices.

In summary, Professor Jiwei Cui and co-workers developed a new type of hydrogel that can be tuned by pre-seeding CaCO₃ microparticles and acid-triggered cross-linking to achieve high mechanical and electrical properties. The hydrogel can be used as a strain sensor for wearable devices that can monitor human motions and pain signals.

Soft robotic actuators have played a fundamental role in the current shifts in task and workplace priorities. Unlike traditional hard robotic systems, soft robotic systems comprise complex control schemes and sensory mechanisms needed to provide the necessary task flexibility, compliance and safety. Producing intrinsically soft devices has emerged as a promising approach for integrating safety and compliance measures into established systems. Among them, soft pneumatic actuators are an attractive new technology featuring rapid response, high force-to-mass ratio and a versatile range of motions while offering intrinsic compliance.

Newer soft pneumatic actuators have been engineered to overcome the deficiencies of the earlier designs, like pneumatic artificial muscles, by increasing the degrees of freedom and achievable structural deformation. Pneu-Flex actuators and pneumatic networks, mostly developed by exploring the compliant nature of elastomers, are some of the common topologies of the new designs. Nevertheless, most of the existing soft pneumatic actuators are mainly suitable for creating bending motions, with very few used to either produce linear movements or strains in multiple directions.

Although linear soft pneumatic actuators function like biological muscles and are suitable replacements for rigid and heavy piston mechanisms, the linear operation of these actuators requires very high pressure to produce desirable strokes. 3D printing, specifically the digital light projection (DLP) method, is a promising technique for producing complex elastomeric parts commonly used in producing soft actuators. However, the lack of suitable materials is currently the biggest limitation to developing soft actuators through DLP.

Recently, a newly developed soft elastomeric resin, known as ElastAMBER, has proved effective for DLP printing of soft and flexible parts owing to its low stiffness, high elastic strains and low hysteresis properties. ElastAMBER was successfully used to produce Linear Soft Multi-Mode Actuators (LSOMMAs), capable of producing bidirectional actuation and achieving remarkable extensile and contractile forces under positive and negative pressures, respectively. It exhibited significant advantages over other soft actuators.

Inspired by the recent developments, Australian researchers: Dr. Ryan Drury, Dr. Vitor Sencadas and led by Professor Gursel Alici from University of Wollongong produced a novel 3D printed pneumatic device named LSOMMA. In their approach, the new elastomeric resin was designed to be used on DLP 3D printers. The characteristics and functional capabilities of LSOMMA were evaluated, including contraction and extension under differential pressures. Finally, the applications of these actuators in soft robotics were demonstrated. Their research work is currently published in the journal, Soft Matter.

The research team demonstrated the scalability of the presented LSOMMA as well as its ability to provide stable responses over 410,000 cycles. Such soft pneumatic actuators require low operating pressure to achieve meaningful strains at pressures way below that of most pneumatic devices. Thus, in correspondence to the actuator strains of up to 37% and -50%, LSOMMA was successfully operated at low pressures to realize full expansion and contraction at gauge pressures of 75 kPa and -25 kPa, respectively. Further, all actuators examined recorded a rise time of less than 250 ms.

Two mobile robots were constructed and analyzed to demonstrate the applicability and benefits of the multi-mode LSOMMA actuators in soft robotics. The peristaltic crawler robot could rapidly traverse vertical, horizontal and bent pipe sections while adapting to the pipe diameter changes. The ground locomotion robot could turn 361°C min^-1 and move up to 652 mm min^-1. An untethered version of the ground robot could transverse multiple surfaces materials like carpet. Furthermore, the lower operating pressures of LSOMMA allowed the utilization of lighter and smaller pumps and other control components, perming more opportunities for creating mobile devices.

In summary, soft pneumatic actuators based on 3D printed elastomeric resin, and their application in soft robotics were reported. Achieving multiple mode operations is beneficial in producing pulling and pushing forces using only a single actuator. All the demonstrated robotic applications performed significantly better in many categories than most reported in the literature. In a statement to Advances in Engineering, senior Professor Gursel Alici who is also the executive Dean of Faculty of Engineering and Information Sciences said ” We continuously aim to reduce the footprint of robotic technologies such that the mechanical structure, for example, seamlessly houses actuation and sensing elements and other important elements such as energy source (e.g., battery pack) to bring robotic systems one step closer to the operation and functions of biological systems. This work is a significant step towards this goal, pushing the limits for minimum foot-print robotic systems to widen their application prospects in the field of soft robotics”.

Flexible sensors find a wide range of applications in different fields, including human-machine interfaces, electronic devices and robotics. The design and manufacturing of these sensors often closely follow the trends in miniaturization, functionality and compatibility fundamental to the design of integrated sensing materials. To date, a wide range of nano- and micro-scale flexible sensors have been developed using various materials that possess the necessary transducing principles. Among them, piezoelectric materials occupy a vital position in the design of sensing systems owing to their improved mechanical properties and energy conversion efficiency at the nanoscale level.

Particularly, piezoelectric polymers like polyvinylidene fluoride (PVDF) and constituent co-polymers have attracted research attention as promising sensing materials. However, most PVDF and related co-polymers are assembled via a multistep and tedious process, leading to costly and bulk sensing devices that cannot meet the current miniaturization and industrialization requirements. To overcome this challenge, there have been considerable attempts to improve the interfacial interactions of functional and electrode elements by exploring different piezoelectric sensor-based flexible electrodes. Compared with traditional metal electrodes, flexible conductive electrodes produce superior energy conversion efficiency and hold potential applications in next-generation flexible sensors.

Nevertheless, flexible electrodes require multiple machining, which increases the size, cost and complexity of manufacturing the sensors. In addition, most piezoelectric sensing materials rely on the bottom and top planar electrodes to facilitate the assembly of piezoelectric sensors. This is characterized by flexibility limitations, high cost and time-consuming manufacturing processes that limit their practical applications. Electrospinning technique that has gained popularity for fabricating nanofiber materials with higher energy conversion efficiency and larger piezoelectric coefficient has proved insufficient for fabricating flexible nanofibers.

To address these challenges, Wenbo Jiang (Master degree), Kongsen Hu (PhD candidate) and Dr. Nan Lv from Northeast Electric Power University in collaboration with Dr. Zhiwei Lyu from Shenzhen Wenirune Electronic Technologies Company Limited, proposed a feasible strategy for constructing nanoresistance networks and piezoelectric elements through fabricating single PAN/PANI/PVP piezoelectric-conductive nanofibers by electrospinning technology. Piezoelectric polymer PAN served as functional elements, while PANI and matrix PVP served as nanoresistance networks. The tiny force-sensing capability of the PAN/PANI/PVP integrated nanofibers membrane (INFM) was systematically evaluated and discussed. Their work is currently published in the journal, Sensors and Actuators A: Physical.

The researchers demonstrated the ability of the PAN/PANI/PVP piezoelectric-conductive nanofibers to timely collect and output voltage via the nanoresistance networks based on the principle of Wheatstone bridge. The integration nanoresistance networks and piezoelectric elements was beneficial in improving polarization and collection of the induced charges, contributing to enhanced piezoelectric conversion and output performance. This enabled the INFM to accurately perceive pressure, especially tiny force, with high-precision sensitivity of approximately 667 mVN^-1 and an extremely low detection limit of about 0.05 while maintaining the linear relationship.

In a nutshell, a novel PAN/PANI/PVP piezoelectric-conductive nanofibers capable of constructing nanoresistance networks and piezoelectric elements simultaneously via electrospinning technique was reported. The presented strategy eliminated the constraints of the sandwich structure associated with traditional devices to achieve single nanofibers, thus paving the way for efficient construction of nanoscale, lightweight, low cost and highly sensitive integrated sensing materials. Regarding their outstanding performance, the authors, in a statement to Advances in Engineering, observed that the PAN/PANI/PVP INFM could pioneer novel breakthroughs in the design and fabrication of higher-performance flexible micro/nanoscale sensors for different practical applications.