Journals
2026 EN
Guo Feng · Ren Hongda · Zhang Yang
+1 more
Neuromorphic devices, inspired by the human brain's efficiency and adaptability, hold great potential for artificial intelligence (AI) hardware to overcome the limitations of traditional von Neumann architecture. As a subclass, multimodal and multifunctional neuromorphic devices have recently gained a lot of attention due to their advantages in in‐sensor computing and sophisticated behaviors. In this review, recent advances in materials, device structures, and applications in this field are systematically presented. It includes optical, electrical, mechanical, and chemical sensing in multimodal neuromorphic device, which enable in‐sensor computing to minimize energy consumption and enhance real‐time decision‐making. The materials applied in this field such as phase‐change, 2D materials, and ferroelectrics are summarized for their roles in achieving synaptic plasticity, nonvolatile memory for multifunctional neuromorphic devices. Structural innovations, including reconfigurable, multi‐terminal, and 3D‐integrated designs, further optimize parallel processing and multifunctional integration. Besides, application scenarios of multimodal and multifunctional neuromorphic devices and their advantages for improving the efficiency of AI are reviewed. Finally, challenges in material stability and commercialization are discussed, it emphasizes the need for interdisciplinary efforts to bridge the gap. This review provides critical insights and future directions for developing brain‐inspired, energy‐efficient AI hardware.
Journals
2026 EN
Zhao Fangjiao · Xu Ruixue · Zhao Wujun
+3 more
Robotic needle steering plays a critical role in improving the precision and safety of percutaneous interventions across various clinical applications. However, manual needle steering remains challenged by operator‐dependent variability, physiological tremor, and limited adaptability to dynamic tissue deformation. To address these limitations, this review examines recent advances in robotic needle steering, structured around three core components: 1) sensing for closed‐loop needle steering, 2) modeling of soft tissue deformation and needle deflection, and 3) trajectory planning and closed‐loop control strategies. Furthermore, emerging trends are discussed in artificial intelligence‐driven autonomy and advanced biocompatible materials, highlighting their potential to enhance steering accuracy and real‐time adaptability in future robot‐assisted percutaneous procedures.
Journals
2026 EN
MarinLlobet Arnau · SánchezManso Sergio · Manasanch Arnau
+7 more
This study investigates the application of Riemannian geometry‐based methods for brain decoding using invasive electrophysiological recordings. While Riemannian geometry has been successfully applied in noninvasive settings, its utility for invasive datasets, which are typically smaller and scarcer, remains less explored. Herein, a minimum distance to mean (MDM) classifier is proposed using a Riemannian geometry approach based on covariance matrices extracted from intracortical local field potential (LFP) recordings across various regions during different brain state dynamics. For benchmarking, the performance of the approach is evaluated against convolutional neural networks (CNNs) and Euclidean MDM classifiers. The results indicate that the Riemannian geometry‐based classification not only achieves a superior mean F1 macro‐averaged score across different channel configurations but also requires up to two orders of magnitude less computational training time. Additionally, the geometric framework reveals distinct spatial contributions of brain regions across varying brain states, suggesting a state‐dependent organization that traditional time series‐based methods often fail to capture. The findings align with previous studies supporting the efficacy of geometry‐based methods and extend their application to invasive brain recordings, highlighting their potential for broader clinical use, such as brain‐computer interface applications.
Journals
2026 EN
Pavone Antonio · Rifino Rosanna · Pricci Alessio
+2 more
Electromagnetic (EM) soft actuators exhibit exceptional responsiveness to the critical demands of soft robotics, offering advantages such as high actuation speed, low weight, energy efficiency, and electrical controllability. However, conventional designs require external permanent magnets positioned outside the soft structure, constraining practical applications. This study addresses this limitation by developing a novel class of soft EM actuators composed of a silicone matrix with fully embedded copper coils and small magnets, eliminating the need for external magnets. This fully integrated architecture, enabled by additive manufacturing, ensures operation in unstructured environments, as functional elements are monolithically integrated within the actuator. The actuators function in two modes: attraction, inducing 41.7% compression, and repulsion, enabling 47.6% expansion. These actuators demonstrate key performance such as stretchability (continuous operability after 94% strain), scalability (up to 300%), multimodal operation, wearable compatibility (blocking force of 0.15 N), fast response (>1 Hz), low power consumption (2.8 W), lightweight design (18 g), multifrequency capability, and bio‐inspired actuation, wherein compression is achieved through electrical current. These silicone EM actuators demonstrate versatility across several applications, such as flow‐regulating soft valves, fluid mixing devices, high‐speed soft robots (exceeding the 24 BL/s relative speed of cheetahs), and complex 3D structures for controlled contraction and expansion.
Journals
2026 EN
Kong Lingchen · Zhao Yaoyao Fiona
Conventional systems based on traditional design strategies typically excel at single‐task performance but lack adaptability when operating conditions change. Reconfiguration offers a promising alternative, enabling systems to adopt multiple configurations tailored to varying requirements. Natural biological organisms regularly modify their morphology to overcome environmental challenges, inspiring engineering applications that seek similar adaptability. However, the real potential of reconfiguration in engineering is often bounded by traditional design strategies and rigid materials. In this case, shape‐changing structures can provide new insights. This review focuses on the structural foundations of reconfigurable design, emphasizing key principles across origami, bistable structures, and laminate structures, and examines how these shape‐changing structures can enhance the multifunctionality in soft robotics, soft manipulators, and metamaterials. Finally, the review discusses the primary challenges faced by achieving the multifunctionality in practical applications. In conclusion, combining advanced materials with innovative structural designs enables systems to achieve diverse working modes and adaptive properties, paving the way for more versatile and resilient applications across various fields.
Journals
2026 EN
Niazi Muhammad Umer Khan · Mehmood Usman · Choi Jaesoon
+2 more
To advance the application of continuum robots in the medical field, this study proposes the integration of compliant bi‐stability in continuum robots to enable the ability to form custom shapes. The process includes designing a compliant bistable mechanism (CBM), deconstructing a ball‐joint‐based continuum robot segment, and then integrating the two. This allows the resultant continuum segment to become bistable. Two versions of this CBM‐integrated continuum segment arose from the design process: an “Extending” version that improves the effective length and reachability of the segments, and a “Locking” version that locks certain regions and enhances stiffness. When used together, these two segment types enable the CBM‐based continuum robot to demonstrate mechanical intelligence by achieving custom shapes in different configurations through passive mechanical design. The paper also presents an analytical model to characterize the CBM. Simulation and experimental results for the segments establish a 50% increase in workspace by using the extending segment. Using the locking segment results in an 80% to 100% increase in stiffness. A shape formation test for a liver biopsy procedure shows a decrease of 61% in the tip‐to‐target deviation, demonstrating a potential application for minimally invasive surgeries.
Journals
2026 EN
Liu Juejing · Li Xiaoxu · Feng Yifu
+4 more
Understanding rare‐earth element (REE) mineralization mechanisms is essential for developing efficient separation strategies. Although the geochemical pathways that generate REE deposits are qualitatively known, quantitative links between specific conditions and mineralization outcomes remain limited. Herein, the repurpose laboratory REE hydrothermal synthesis data—originally collected for functional‐materials fabrication—as a surrogate for studying mineralization with data‐driven methods. The compiled 1,200+ hydrothermal reaction records and trained three machine‐learning models—K‐nearest neighbors (KNN), random forest (RF), and extreme gradient boosting (XGB)—to predict product elements and phases from precursors, additives, reaction conditions, and engineered features. Validation shows XGB achieves the highest accuracy. Feature importance indicates thermodynamic properties of cations and anions dominate model decisions. Correlations reveal positive relationships among precursor concentration, reaction time, pH, and temperature, consistent with classical crystallization behavior. XGB‐based regressors are built to predict crystallization temperature and pH from precursor/product attributes. Performance is strongest when similar training examples exist, while accuracy declines for underrepresented reactions, notably REE carbonates and heavy‐REE systems. Overall, the study shows that functional‐materials datasets can illuminate REE mineralization and provide priors for exploration and processing. Expanding datasets with less‐studied chemistries and conditions will improve generality and support deposit discovery and more efficient REE recovery.
Journals
2026 EN
Tang Zhichuan · Shao Jiahui · Yang Yizhou
+2 more
Existing continuum robots with nonstretchable skeletons are monofunctional, providing only bending degrees of freedom (DOF). Additionally, their nonembedded solid structures limit application potential. In this study, inspired by origami structures, a cable‐driven continuum robot featuring dual bending‐contraction DOF is developed. Then, kinematic analysis to predict deformation at different cable contraction and joint parameters is conducted. Based on the kinematic characteristics of the robot, a continuum robot with five joint modules is fabricated to demonstrate its contraction and bending capabilities under no‐load and loaded states. This continuum robot can reach a maximum contraction of 33.87% and a bending angle of 95.71°. By increasing the number of joints in the continuum robot, grippers with different numbers of fingers can be made for grasping objects of various shapes. Additionally, based on the embedded characteristics of the robot's cable‐supported structure, a wrist rehabilitation orthosis with two joint modules is designed. This orthosis supports personalized customization, and has two rehabilitation movement modes, that is, flexion‐extension (76.45° curvature) and radial‐ulnar deviation (53.66° curvature). The performance and application experiments demonstrate the robot's structural conformality and potential for application in different interaction scenarios, and provide the practical guidance for cable‐driven continuum robotic applications.
Journals
2026 EN
Wu Changchun · Liu Hao · Lin Senyuan
+4 more
Mimicking invertebrates, soft robots tend to control each of their body joints to achieve the desired shape changes and movements to accomplish different tasks. Shape‐memory alloy (SMA) is a common actuation material but needs to be designed into specific shapes to disperse local strains. In this article, a dedicated configuration of SMA strips is introduced for soft robotic body joint manipulation and morphing. Inspired by the Möbius strip, the proposed SMA torsion strip (STS) can meet the requirements of both large deformation and large force output desirable for robotic applications. Compared to conventional morphing methods, the STSs can supply considerable torque output over a wide range of bending angles to freely chosen body joints. Therefore, both pattern‐to‐pattern extreme shape morphing, such as tendrils curling, and programmable shape morphing can be achieved. A mathematical model is established to describe how geometry and temperature affect the STS properties to optimize performance. Due to the extensibility of the STS, it can be used for a large variety of robotic applications that are partially illustrated in this research as artificial muscles, grippers, jumping robots, and soft proportional valves.
Journals
2026 EN
Beoletto Paolo H. · Milano Gianluca · Ricciardi Carlo
+2 more
Traditional speech recognition methods rely on software‐based feature extraction that introduces latency and high energy costs, making them unsuitable for low‐power devices. A proof‐of‐concept demonstration is provided of a bioinspired tonotopic sensor for speech recognition that mimics the human cochlea, using a spiral‐shaped elastic metamaterial. The measured modal response of the structure at different frequencies generates a spatially distributed signal, providing a spatiotemporal map of the input named “tonogram”. The device acts as an in‐sensor physical reservoir computing system, working simultaneously as a sensor and as a computing unit, capable of extracting features of spoken words relevant to speech recognition. Results indicate that this can serve as a valid alternative to traditional software‐based digital preprocessing, ensuring high accuracy in terms of classification, while reducing computational requirements. This work demonstrates the potential of bioinspired metamaterials for energy‐efficient auditory sensing and, beyond speech recognition, for applications such as IoT devices and edge computing artificial intelligence systems.