Journals
2026 EN
Li Yug · Zhu Bin · Yue Wenchao
+3 more
The development of centimeter‐scale reconfigurable parallel robots is critical for applications requiring precise, adaptive, and space‐constrained operations across bench‐top automation and minimally invasive systems. However, existing platforms struggle to balance miniaturization, multidegree‐of‐freedom flexibility, and rapid in situ reconfiguration for different tasks. To address these challenges, Grassmann Line‐Guided Magneto‐Operative Robotics is proposed, a systematic design framework that synergizes Grassmann line geometry with modular magnetic spherical joint connections to enable rapid in situ topology changes. A synthesis‐based method is employed to guide the assembly strategy design flow for a specific magneto‐connected parallel robot. The magneto‐coupled modules enable in situ minute‐level (average 48 s) reconfiguration across multiple types of centimeter‐scale magneto‐reconfigurable parallel robots, all sharing a common driving system and featuring a base diameter of 3.2 cm. As a case study, a widely used 3‐P(4S) robot system is fully analyzed. The regular workspace, considering the influence of magnetic connections and the global transmission index, is evaluated to assess the performance of different design parameters. Then, the payload experiment is conducted. Bench‐top positioning, bench‐top manipulation, and laparoscopic manipulation potentials are demonstrated. Overall, the proposed library and design methodology, utilizing magneto‐connected parts, enable the rapid prototyping of reconfigurable, modular parallel robots.
Journals
2026 EN
Chung Chongyoung · Mun Heeju · Atashzar Seyed Farokh
+1 more
This article presents a novel, versatile wearable force myography (FMG) system based on optical fiber technology, designed for high sensitivity and mechanical robustness. Unlike conventional FMG systems, which are susceptible to environmental interference, the proposed system utilizes light loss through controlled fiber–polymer contact to achieve stable and noise‐free signal transmission. Its compact and flexible form factor allows seamless integration into wearable devices, facilitating muscle‐activity monitoring under diverse real‐world conditions, including biologically challenging scenarios such as sweating. Experimental evaluations highlight the system's ability to detect even micronewton‐scale forces and accurately recognize multiple gestures. Furthermore, the system can estimate joint angles, including those of individual fingers, which underscores its potential for precise motion capturing and continuous tracking. Overall, the proposed FMG system represents a promising solution for a wide range of practical human–robot interaction applications.
Journals
2026 EN
Woo Jae Seung · Choi Kyoung Min · Park Geun Tae
+2 more
For the first time, a fully complementary metal–oxide–semiconductor process‐compatible novel three‐transistor (3‐T) embedded NOR flash is demonstrated on a 28 nm fully depleted silicon‐on‐insulator platform. By introducing the band‐band hot hole (BBHH) and channel hot electron (CHE) injection enabled by NMOS/PMOS pair coupling transistors, the proposed memory achieves record‐fast 28‐ns long‐term potentiation (LTP) and depression (LTD), offering high‐speed and highly reliable synaptic behavior for online training in neuromorphic hardware applications. The proposed 3‐T embedded NOR flash demonstrates highly reliable operations: only 1.36% memory window degradation and 4.7% subthreshold swing degradation after 10 7 program/erase (P/E) cycles. Moreover, its near‐zero linearity LTP/LTD operations deliver a high classification accuracy of 92% on the Modified National Institute of Standards and Technology (MNIST) datasets. Finally, the proposed device shows an exceptionally short training latency of 2.68 s for 1M MNIST images, which confirms its suitability for high‐performance training accelerators.
Journals
2026 EN
Liu Yuchen · Tian Bocheng · Yuan Feiyang
+5 more
Perching robots offer an effective solution to the energy limitations of small robots during long‐duration missions. However, without integrated sensing capabilities, they are prone to failure in complex environments. This study presents a remora‐inspired sensing suction cup to enhance adhesion reliability for robots in complex aerial–aquatic conditions. Mimicking remora fish, the design incorporates liquid metal microchannel sensors arranged at 90° intervals to monitor lip deformation, enabling real‐time assessment of adhesion state and force distribution. The optimized suction cup morphology improves deformation sensitivity, while the integrated sensor system operates effectively in both aquatic and aerial environments. Performance tests demonstrate that the sensors exhibit nonlinear but repeatable responses, with 2° bending resolution and stable operation over 1,000 cycles despite minor hysteresis. Experimental results confirm that the four‐directional sensor array can reflect adhesion status and horizontal force detection, validating the design's feasibility. When deployed on an aerial–aquatic robot, the system successfully enables real‐time leakage detection, lateral disturbance detection, and environmental tactile sensing. This bioinspired approach enhances the environmental adaptability and operational reliability of robots, offering a robust solution for maintaining attachment in complex conditions and significantly enhances the applicability of such systems in robotic applications.
Journals
2026 EN
Sapuppo Francesca · Di Pasquale Giovanna · Graziani Salvatore
+5 more
Bacterial cellulose (BC) is an emerging smart material, synthesized through microbial fermentation of environmentally friendly substrates, including organic waste. When functionalized with ionic liquids (ILs) and coated with conductive polymers, BC forms soft, sustainable, and electroactive composites, making it suitable for sensors in soft robotics, wearable, biomedical, and environmental monitoring applications. However, modeling frameworks for BC–IL sensors are still lacking, hindering their integration into real‐world applications. To bridge this gap and support smart material design, we propose a novel first‐principle white‐box modeling framework is proposed that couples a 2D finite element method (FEM) for mechanical deformation with 1D FEM sub‐models for ion transport and voltage generation. Specifically, this work introduces the first dual‐carrier multiphysics model for mechanoelectric transduction in BC–IL sensors. The model, experimentally calibrated and validated, resolves the spatio‐temporal dynamics of mechanical deformation and dual‐ion transport, including diffusion, electromigration, and advection. By explicitly incorporating the transport and interaction of both cations and anions, previously neglected in smart‐sensors modeling, the proposed strategy provides a foundational simulation framework for the scalable, rapid, and intelligent design of next‐generation biodegradable and multifunctional smart sensors, advancing the integration of green materials into intelligent systems.
Journals
2026 EN
Lee Jin Wook · Kim Changha · Park Geun Tae
+3 more
A novel ternary content‐addressable memory architecture using one capacitor and one nanoelectromechanical (NEM) memory switch [1C‐1N ternary content‐addressable memory (TCAM)] is proposed for energy‐efficient and high‐reliability computations in large‐scale arrays. The proposed 1C‐1N TCAM integrates a capacitor and a nonvolatile NEM memory switch monolithically on the CMOS interconnect layers, leveraging the unique high‐impedance state of NEM memory switches to enable ternary operation within a single cell. The proposed fully back‐end‐of‐line integrated TCAM cells achieve high capacitance ratios (138–1695) owing to exceptionally low air gap capacitance, facilitating robust sensing margin while minimizing dynamic energy consumption. Experimental demonstration of TCAM operation and array‐level simulations based on the measurement results confirm the feasibility and efficiency of the proposed architecture. Moreover, the proposed 1C‐1N TCAM exhibits excellent tolerance against cycle‐to‐cycle and device‐to‐device variations due to the inherent mechanical characteristics of NEM memory switches, showing significant potential for large‐scale implementation in various in‐memory computation tasks and underscoring its suitability for data‐intensive applications.
Journals
2026 EN
Tasnim Nishat · Tunur Mirza M. A. · Arefin Fahad
+7 more
AI‐powered monitoring platforms can significantly enhance the accessibility and responsiveness of water quality assessment in decentralized and resource‐limited settings. Conventional methods for detecting heavy metal ions, such as atomic absorption spectroscopy (AAS), offer high accuracy but require expensive instrumentation, trained personnel, and laboratory infrastructure, limiting their use in field applications. Here, SmartDetectAI, a low‐cost, portable, AI‐powered web application designed for rapid, on‐site colorimetric detection of heavy metal ions in water is presented. The system integrates silver nanoparticles (AgNPs) prepared from plant extract with a custom‐built imaging chamber and a web‐based application (web app) for automated and remote analysis. Supported by a computer vision model (YOLOv8n) for region detection and a machine learning algorithm (XGBoost) for concentration estimation, SmartDetectAI enables automated, real‐time quantification of mercury‐ and cadmium‐based species, which are the predominant aqueous forms under near‐neutral pH conditions. Users capture sensor images with a smart device and receive result outputs through an intuitive graphical interface hosted on a Flask‐based server. Field validation using pond water samples spiked with 1 and 10 μM Cd 2+ shows strong agreement with standard AAS measurements, achieving an average predictive accuracy of ≈84%.
Journals
2026 EN
Gallucci Giulio · Hunt Andres
Poly(vinylidene fluoride) (PVDF) and its derivatives are ferroelectric polymers (FPs) that combine high electric‐field‐induced strains with mechanical flexibility, light weight, and processability, making them attractive materials for actuator applications. This work reviews the state‐of‐the‐art in PVDF‐based electromechanical transduction, covering both reported materials and actuators. Materials are compared by maximum strains, energy densities, and coupling efficiencies and categorized as: 1) vinylidene fluoride (VDF) polymers, including PVDF and its co‐, ter‐, and tetrapolymers; 2) PVDF‐based composites with ceramic, conductive, metal‐organic, and organosilicate fillers; and 3) polymer blends with plasticizers or other electroactive polymers. The highest strains and energy densities have been respectively reported for P(VDF‐DB) (13.4%) and TiO2/PVDF (11.3 J cm −3 ) and highest coupling efficiencies for P(VDF‐TrFE‐CFE‐FA), SWCNTs/P(VDF‐TrFE), and TiO2/PVDF (0.88). Actuators are compared in terms of maximum displacements and categorized as unimorph and bimorph bending cantilevers, dilating diaphragms, plates, stacks, and tubular structures. Bending cantilevers are the most frequently reported actuators. The highest length‐normalized displacements ( δ/L ) in quasi‐static and resonant operation were reported for PVDF bimorphs (0.35 and 0.45 respectively), which can be significantly improved by optimizing the transducer design and employing more efficient materials. The findings further indicate several unexplored transducer material candidates that are anticipated to exhibit high transduction response.
Journals
2026 EN
Hong Taehwa · Lee Jungjae · Song ByungHyun
+1 more
Soft robotics holds immense promise for applications requiring adaptability and compliant interactions. However, the lack of sufficiently fast and accurate simulation environments for soft robots has hindered progress, particularly in linking with reinforcement learning (RL) applications. Traditional finite element method (FEM) models provide precise insights into soft robot dynamics but are computationally intensive and impractical for accelerated simulation. This work introduces a novel framework that integrates high‐fidelity FEM simulations with computationally efficient physics‐based simulations through a surrogate model tailored for RL. The surrogate model, trained on real‐world and FEM‐generated datasets, captures complex dynamics while maintaining efficiency. Sim2real experiments validate the framework, implementing the trajectory tracking and the force control tasks with high accuracy. These results demonstrate the framework's ability to bridge the simulation gap, enabling its application to advanced tasks, such as manipulation and interaction in unstructured environments.
Journals
2026 EN
Yu Yang · Zhang Jinyao · Wang Dengchen
+12 more
Kirigami, or “jianzhi” in Chinese, is an art in paper‐cutting. Using simple tools like scissors, artisans transform paper into intricate designs featuring flowers, animals, or characters (e.g., “囍”). Nowadays, kirigami has emerged as a particularly promising design strategy in engineering. This method involves creating systematic cut patterns on thin, planar sheets, which enables complex mechanical responses by changing dimensions, thereby offering innovative solutions for the development of metamaterials, soft actuators, and robotic systems. The concept of the integration of ancient art and modern science and technology has injected vitality into the development of many disciplines and become the forefront of interdisciplinary research. This review provides a systematic review of recent progress on the design of kirigami and applications in diverse robotic prototypes. The kirigami begins by classifying into two categories from a compliant mechanism perspective, and then it examines the distinctive mechanical properties that altered by cut patterns, followed by reviewing the design of the two types of kirigami. Next, the kirigami‐inspired kinematic metamaterials is examined. Finally, applications in soft actuators and robotic systems is demonstrated. The integration of design methods, fabrication techniques, materials research, mechanics modeling, and control systems will further advance this emerging field.