Search Results (708)

Biomolecule-driven smart materials represent a paradigm shift in pharmacology, transitioning drug delivery from a passive process to an active, programmable, and highly specific intervention. These systems, constructed from or functionalized with biological macromolecules such as nucleic acids, peptides, proteins, and polysaccharides, are engineered to sense and respond to specific pathophysiological cues or external triggers. This review provides a comprehensive analysis of this rapidly evolving field. We first delineate the fundamental principles of stimuli-responsive actuation, categorizing systems based on their response to endogenous (pH, redox, enzymes, ROS) and exogenous (temperature, light, magnetic fields) triggers. We then conduct an in-depth survey of the primary biomolecular architectures, examining the unique design space offered by DNA nanotechnology, the functional versatility of peptides and proteins, and the biocompatibility of polysaccharides. Key therapeutic applications in oncology, inflammatory diseases, and gene therapy are discussed, highlighting how these intelligent systems are being designed to overcome critical biological barriers and enhance therapeutic efficacy. Finally, we address the formidable challenges—spanning biocompatibility, manufacturing scalability, and regulatory navigation—that constitute the “bench-to-bedside” chasm. We conclude by exploring future perspectives, including the development of multi-stimuli responsive, logic-gated systems and the transformative potential of artificial intelligence in designing the next generation of personalized nanomedicines. Full article

The accurate estimation of parameters in dynamical systems stands for an open key research issue in modeling, control, and fault diagnosis. The presence of noise in input and output signals poses a serious challenge for accurate real-time dynamical system parameter estimation. This paper proposes a new robust algebraic parameter estimation methodology for integer-order dynamical systems that explicitly incorporates the signal filtering dynamics within the estimator structure and enhances noise attenuation through fractional differentiation in frequency domain. The introduced estimation methodology is valid for Liouville-type fractional derivatives and can be applied to estimate online the parameters of differentially flat, oscillating or vibrating systems of multiple degrees of freedom. The parametric estimation can be thus implemented for a wide class of oscillating or vibrating, nth-order dynamical systems under noise influence in measurement and control signals. Positive values are considered for the inertia, stiffness, and viscous damping parameters of vibrating systems. Parameter identification can be also used for development of actuators and control technology. In this sense, validation of the algebraic parameter estimation is performed to identify parameters of a differentially flat, permanent-magnet direct-current motor actuator. Parameter estimation for both open-loop and closed-loop control scenarios using experimental data is examined. Experimental results demonstrate that the new parameter estimation methodology combining signal filtering dynamics and fractional calculus outperforms other conventional methods under presence of significant noise in measurements. Full article

Biopolymers have emerged as a transformative class of materials that reconcile high-performance functionality with environmental stewardship. Their inherent capacity for controlled degradation and biocompatibility has driven rapid advancements across electronics, sensing, actuation, and healthcare. In flexible electronics, these polymers serve as substrates, dielectrics, and conductive composites that enable transient devices, reducing electronic waste without compromising electrical performance. Within sensing and actuation, biodegradable polymer matrices facilitate the development of fully resorbable biosensors and soft actuators. These systems harness tailored degradation kinetics to achieve temporal control over signal transduction and mechanical response, unlocking applications in in vivo monitoring and on-demand drug delivery. In healthcare, biodegradable polymers underpin novel approaches in tissue engineering, wound healing, and bioresorbable implants. Their tunable chemical architectures and processing versatility allow for precise regulation of mechanical properties, degradation rates, and therapeutic payloads, fostering seamless integration with biological environments. The convergence of these emerging applications underscores the pivotal role of biodegradable polymers in advancing sustainable technology and personalized medicine. Continued interdisciplinary research into polymer design, processing strategies, and integration techniques will accelerate commercialization and broaden the impact of these lower eCO₂ value materials across diverse sectors. This perspective article comments on the innovation in these sectors that go beyond the applications of biodegradable materials in packaging applications. Full article

A novel glucose biosensor is developed based on a tilted fiber Bragg grating (TFBG) functionalized with a pH-responsive polyelectrolyte multilayer membrane, onto which glucose oxidase (GOD) is immobilized. The sensing film is constructed via layer-by-layer self-assembly of poly(ethylenimine) (PEI) and poly(acrylic acid) (PAA), which undergoes reversible swelling and refractive index (RI) changes in response to local pH variations. These changes are transduced into measurable shifts in the resonance wavelengths of TFBG cladding modes. The catalytic action of GOD oxidizes glucose to gluconic acid, thereby modulating the interfacial pH and actuating the polyelectrolyte membrane. With an optimized (PEI/PAA)₄(PEI/GOD)₁ structure, the biosensor achieves highly sensitive glucose detection, featuring a wide measurement range (10⁻⁸ to 10⁻² M), a low detection limit of 27.7 nM, and a fast response time of ~60 s. It also demonstrates excellent specificity and robust performance in complex biological matrices such as rabbit serum and artificial urine, with recovery rates of 93–102%, highlighting its strong potential for point-of-care testing applications. This platform offers significant advantages in stability, temperature insensitivity, and miniaturization, making it well-suited for clinical glucose monitoring and disease management. Full article

The convergence of carbon nanomaterials and functional polymers has led to the emergence of smart carbon–polymer nanocomposites (CPNCs), which possess exceptional potential for next-generation sensing and actuation systems. These hybrid materials exhibit unique combinations of electrical, thermal, and mechanical properties, along with tunable responsiveness to external stimuli such as strain, pressure, temperature, light, and chemical environments. This review provides a comprehensive overview of recent advances in the design and synthesis of CPNCs, focusing on their application in multifunctional sensors and actuator technologies. Key carbon nanomaterials including graphene, carbon nanotubes (CNTs), and MXenes were examined in the context of their integration into polymer matrices to enhance performance parameters such as sensitivity, flexibility, response time, and durability. The review also highlights novel fabrication techniques, such as 3D printing, self-assembly, and in situ polymerization, that are driving innovation in device architectures. Applications in wearable electronics, soft robotics, biomedical diagnostics, and environmental monitoring are discussed to illustrate the transformative impact of CPNCs. Finally, this review addresses current challenges and outlines future research directions toward scalable manufacturing, environmental stability, and multifunctional integration for the real-world deployment of smart sensing and actuation systems. Full article

Intelligent robotic systems frequently operate under stringent energy limitations, especially in complex and dynamic environments. To enhance both adaptability and reliability, this study introduces a semantic planning framework that integrates ontology-driven reasoning with energy awareness. The framework estimates energy consumption based on the platform-specific behavior of sensing, actuation, and computational modules while continuously updating place-level semantic representations using real-time execution data. These representations encode not only spatial and contextual semantics but also energy characteristics acquired from prior operational history. By embedding historical energy usage profiles into hierarchical semantic maps, this framework enables more efficient route planning and context-aware task assignment. A shared semantic layer facilitates coordinated planning for both single-robot and multi-robot systems, with the decisions informed by energy-centric knowledge. This approach remains hardware-independent and can be applied across diverse platforms, such as indoor service robots and ground-based autonomous vehicles. Experimental validation using a differential-drive mobile platform in a structured indoor setting demonstrates improvements in energy efficiency, the robustness of planning, and the quality of the task distribution. This framework effectively connects high-level symbolic reasoning with low-level energy behavior, providing a unified mechanism for energy-informed semantic decision-making. Full article

Micro-accelerometers are in high demand across many due to their compact size, low energy consumption, and excellent precision. Since gravity causes a large movement when the device is positioned vertically, measuring low gravitational acceleration is challenging. This study examines the intrinsic relationship between applied voltage levels and displacement in micro-accelerometers. The study introduces a novel design that integrates hybrid flexures, comprising both linear and angular configurations, with an out-of-plane overlap varying (OPOV) electrostatic actuation mechanism. This design aims to measure the micro-accelerometer’s movement and low frequency response. The proposed device with silicon material is designed and simulated using the IntelliSuite^® software, considering its small dimensions and 25 µm thickness. The norm value of 28.0916 μN from gravity’s reaction forces on the body, a resonant frequency of 179.668 Hz at the first desired mode, and a maximum stress of 24.7 MPa were obtained through the electro-mechanical analysis. A comparison of the proposed design was conducted with other configurations, measuring a frequency of 179.668 Hz at a minimum downward displacement of 7.69916 µm under the influence of gravity without electrostatic mechanisms. Following this, an electrostatic actuation mechanism was introduced to minimize displacement by applying different voltage levels, including 1 V, 1.5 V, and 3 V. At 3 V, a significant improvement in displacement reduction was observed compared to the other applied voltages. Additionally, dynamic and sensitivity analyses were carried out to validate the performance of the proposed design further. Full article

Autonomous surgery involves surgical tasks performed by a robot with minimal or no human involvement. Thanks to its precise automation, surgical robotics offers significant benefits in enhancing the consistency, safety, and quality of procedures, driving its growing popularity. However, ensuring the safety of autonomous surgical robotic systems remains a significant challenge. To address this, we propose a simulation-based validation method to detect potential safety issues in the software of surgical robotic systems, complemented by a digital twin to estimate the gap between simulation and reality. The validation framework consists of a test case generator and a monitor for validating properties and evaluating the performance of the robotic system during test execution. Using a robotic arm for needle insertion as a case study, we present a systematic test case generation method that ensures effective coverage measurement for a three-dimensional, irregular model. Since no simulation can perfectly replicate reality due to differences in sensing and actuation, the digital twin bridges the gap between simulation and the physical robotic arm. This integration enables us to assess the discrepancy between virtual simulations and real-world operations by verifying whether the data from the simulation accurately predicts real-world outcomes. Through extensive experimentation, we identified several flaws in the robotic software. Co-simulation within the digital twin framework has highlighted these discrepancies that should be considered. Full article

Wind turbine blades feature complex geometries and operate under harsh conditions, including high curvature gradients, nonlinear deformations, elevated humidity, and particulate contamination. This study presents the design and kinematic analysis of a novel climbing robot based on a 10R folding metamorphic mechanism. The robot employs a hybrid wheel-leg drive and adaptively reconfigures between rectangular and hexagonal topologies to ensure precise adhesion and efficient locomotion along blade leading edges and windward surfaces. A high-order kinematic model, derived from a modified Grubler–Kutzbach criterion augmented by rotor theory, captures the mechanism’s intricate motion characteristics. We analyze the degrees of freedom (DOF) and motion branch transitions for three representative singular configurations, elucidating their evolution and constraint conditions. A scaled-down prototype, integrating servo actuators, vacuum adhesion, and multi-modal sensing on an MDOF control platform, was fabricated and tested. Experimental results demonstrate a configuration switching time of 6.3 s, a single joint response time of 0.4 s, and a maximum crawling speed of 125 mm/s, thereby validating stable adhesion and surface tracking performance. This work provides both theoretical insights and practical validation for the intelligent maintenance of wind turbine blades. Full article

In recent years, the flexoelectric effect has demonstrated significant potential for applications in sensing, actuation, energy acquisition and other related fields. As the primary structure of flexural output, the flexoelectric beam structure also exhibits substantial potential for development and application. However, flexoelectric output is unable to function effectively at the macroscale, and the impact of the uncertainty of the parameters of flexoelectric material on the flexural output remains unclear. To address the issue of parameter uncertainty, this paper employs the analysis method based on variance-driven coupled with moment-free measure to study the impact caused by structural parameters on the uncertainty of the output voltage of the flexural electron beam in the case of an open circuit, the influence on the output charge uncertainty under short-circuit conditions, and the influence on the effective piezoelectric coefficient uncertainty. This study of parameter uncertainty offers a valuable reference for the reliability assessment and structural optimization design of flexural electric beam and provides theoretical support for the macroscale application of the flexoelectric effect. Full article

Engineered living materials (ELMs) integrate living cells—such as bacteria, yeast, or mammalian cells—with synthetic matrices to create responsive, adaptive systems for sensing and actuation. Among ELMs, those endowed with sensing capabilities are gaining increasing attention for applications in environmental monitoring, biomedicine, and smart infrastructure. Central to these sensing functions are synthetic gene circuits, which enable cells to detect and respond to specific signals. This mini-review focuses on recent advances in sensing ELMs empowered by synthetic gene circuits. Here, we highlight how rationally designed genetic circuits enable living materials to sense and respond to diverse inputs—including environmental chemicals, light, heat, and mechanical loadings—via programmable signal transduction and tailored output behaviors. Input signals are classified by their source and physicochemical properties, including synthetic inducers, environmental chemicals, light, thermal, mechanical, and electrical signals. Particular emphasis is placed on the integration of genetically engineered microbial cells with hydrogels and other functional scaffolds to construct robust and tunable sensing platforms. Finally, we discuss the current challenges and future opportunities in this rapidly evolving field, providing insights to guide the rational design of next-generation sensing ELMs. Full article

This paper presents a fully electronic, CMOS-compatible ultrasonic sensing system integrated into a 3D beamforming architecture for advanced automotive applications. The proposed system eliminates mechanical scanning by implementing a dual-path beamforming structure comprising programmable transmit (TX) and receive (RX) paths. The TX beamformer introduces per-element time delays derived from steering angles to control the direction of ultrasonic wave propagation, while the RX beamformer aligns echo signals for spatial focusing. Electrostatic actuation governs the CMOS-compatible ultrasonic transmission mechanism, whereas dynamic modulation under acoustic pressure forms the reception mechanism. The system architecture supports full horizontal and vertical angular coverage, leveraging delay-and-sum processing to achieve electronically steerable beams. The system enables low-power, compact, and high-resolution sensing modules by integrating signal generation, beam control, and delay logic within a CMOS framework. Theoretical modeling demonstrates its capability to support fine spatial resolution and fast response, making it suitable for integration into autonomous vehicle platforms and driver-assistance systems. Full article

The integration of emerging eXtended Reality (XR) technologies, digital twins (DTs), smart environments, and advanced mobile and wireless networks is set to enable novel forms of immersive interaction and communication. This paper proposes a high-level conceptual framework for shared presence via XR-based communication and interaction within a virtual reality (VR) representation of the digital twin of a smart space. The digital twin is continuously updated and synchronized—both spatially and temporally—with a physical smart space equipped with sensors and actuators. This architecture enables interactive experiences and fosters a sense of co-presence between a local user in the smart physical environment utilizing augmented reality (AR) and a remote VR user engaging through the digital counterpart. We present our lab deployment architecture used as a basis for ongoing experimental work related to testing and integrating functionalities defined in the conceptual framework. Finally, key technology requirements and research challenges are outlined, aiming to provide a foundation for future research efforts in immersive, interconnected XR systems. Full article

Biomimetic robotics and sensor technologies are reshaping the landscape of healthcare and rehabilitation. Despite significant progress across various domains, many areas within healthcare still demand further bio-inspired innovations. To advance this field effectively, it is essential to synthesize existing research, identify persistent knowledge gaps, and establish clear frameworks to guide future developments. This systematic review addresses these needs by analyzing 89 peer-reviewed sources retrieved from the Scopus database, focusing on the application of biomimetic robotics and sensing technologies in healthcare and rehabilitation contexts. The findings indicate a predominant focus on enhancing human mobility and support, with rehabilitative and assistive technologies comprising 61.8% of the reviewed literature. Additionally, 12.36% of the studies incorporate intelligent control systems and Artificial Intelligence (AI), reflecting a growing trend toward adaptive and autonomous solutions. Further technological advancements are demonstrated by research in bioengineering applications (13.48%) and innovations in soft robotics with smart actuation mechanisms (11.24%). The development of medical robots (7.87%) and wearable robotics, including exosuits (10.11%), underscores specific progress in clinical and patient-centered care. Moreover, the emergence of transdisciplinary approaches, present in 6.74% of the studies, highlights the increasing convergence of diverse fields in tackling complex healthcare challenges. By consolidating current research efforts, this review aims to provide a comprehensive overview of the state of the art, serving as a foundation for future investigations aimed at improving healthcare outcomes and enhancing quality of life. Full article

Technology roadmapping is conducted by systematic mapping of technological evolution through patent analytics to inform innovation strategies. This study proposes an integrated framework combining hierarchical Latent Dirichlet Allocation (LDA) modeling with multiphase technology lifecycle theory, analyzing 113,449 Derwent patent abstracts (2008–2022) across three dimensions: technological novelty, functional applications, and competitive advantages. By segmenting innovation stages via logistic growth curve modeling and optimizing topic extraction through perplexity validation, we constructed dynamic technology roadmaps to decode latent evolutionary patterns in AI-powered programmable manipulators (B25J classification) within an innovation trajectory. Key findings revealed: (1) a progressive transition from electromechanical actuation to sensor-integrated architectures, evidenced by 58% compound annual growth in embedded sensing patents; (2) application expansion from industrial automation (72% early stage patents) to precision medical operations, with surgical robotics growing 34% annually since 2018; and (3) continuous advancements in adaptive control algorithms, showing 2.7× growth in reinforcement learning implementations. The methodology integrates quantitative topic modeling (via pyLDAvis visualization and cosine similarity analysis) with qualitative lifecycle theory, addressing the limitations of conventional technology analysis methods by reconciling semantic granularity with temporal dynamics. The results identify core innovation trajectories—precision control, intelligent detection, and medical robotics—while highlighting emerging opportunities in autonomous navigation and human–robot collaboration. This framework provides empirically grounded strategic intelligence for R&D prioritization, cross-industry investment, and policy formulation in Industry 4.0. Full article