Search Results (189)

Artificial muscles are an emerging class of actuators designed to mimic the compliant, efficient, and versatile behavior of biological muscles for fields including the following: soft robotics, prosthetics, wearable enhancements, haptic interfaces, and biomedical devices. These systems encompass various actuation mechanisms, including pneumatic, hydraulic, thermal, ionic, electrochemical, and electrostatic. Each with distinct tradeoffs in voltage, strain, output force, bandwidth, efficiency, and manufacturability. Among them, electrostatic actuators have attracted increased attention due to their fast response times, high energy densities, strong compatibility with soft materials, and scalability from microscale devices to large-area and stacked actuators. However, challenges such as dielectric breakdown, material fatigue, and fabrication complexity continue to limit widespread deployment. This review presents a structured classification of various artificial muscle technologies and an in-depth examination of electrostatic actuators including dielectric elastomers, electrostrictive and ferroelectric polymers, liquid crystal elastomers, electrostatic film motors, stacked architectures, and microscale/milliscale devices. In this review the operating principles, materials, architectures, performance characteristics, and failure modes of electrostatic actuators will be discussed. Additionally, a comparison will highlight tradeoffs across actuator families based on metrics such as voltage, force, strain, bandwidth, and manufacturability. Lastly, we outline future research directions in materials, physics-informed modeling, system integration, and scalable fabrication necessary to advance electrostatic artificial muscles toward practical, real-world deployment. Full article

Smart manufacturing, Industry 4.0, and cyber-physical systems (CPSs) require sensing architectures capable of resolving both spatially distributed asset behavior and highly localized process states. This review examines optical fiber sensors (OFSs) and integrated photonic sensors for industrial monitoring through a deployment-oriented, multi-scale perspective. The discussion covers five major application regimes: continuous infrastructure surveillance, structural health monitoring (SHM) of load-bearing composites, dynamic condition monitoring of machinery, in situ observability in advanced manufacturing, and localized chemical or gas sensing. Extended fiber-optic networks, including distributed fiber-optic sensing (DFOS) based on Rayleigh, Raman, and Brillouin scattering, together with multiplexed fiber Bragg grating (FBG) sensors, provide passive, embeddable, and remotely interrogated monitoring for large-scale assets and harsh environments. Photonic integrated circuits (PICs) shift transduction to compact node-level devices for localized thermal, mechanical, refractive-index, absorption, vibration, and inertial measurements, while plasmonic and dielectric nanophotonic sensors extend optical monitoring toward surface-selective and chemically specific detection. Across these platforms, digital signal processing (DSP), machine learning (ML), sensor fusion, and digital-twin (DT) coupling are treated as artificial-intelligence-enabled (AI-enabled) layers for signal recovery, inverse mapping, uncertainty reduction, and predictive maintenance. The review argues that scalable industrial adoption is less limited by sensing physics than by the complete deployment chain: packaging, fiber–chip interfacing, calibration stability, interrogation robustness, and AI-enabled data interpretation. This manuscript is structured as a deployment-oriented narrative review of optical fiber and integrated photonic sensors for industrial monitoring and smart manufacturing. Full article

Atmospheric ice accretion on transmission lines threatens the safe operation of power systems, whereas existing monitoring methods mainly focus on ice thickness, load, or morphology and provide limited material-related information for distinguishing ice types. This study investigates the dielectric response of ice and snow samples to evaluate its feasibility for preliminary ice-type discrimination. Artificial glaze ice and natural snow samples were measured using a self-built temperature-controlled parallel-plate system within 10–100 kHz. The effects of freezing-water conductivity, temperature, surface water film, and snow density were examined, and representative glaze ice, dry snow, and wet snow samples were further compared under the same measurement framework. The results show that the dielectric constant generally decreases with frequency, while conductivity, water film, and density mainly increase the response magnitude and, in some cases, alter the prominence of loss-related features. These trends are consistent with reported dielectric dispersion, conductive loss, and snow density-related mixing behavior. Dielectric loss provides clearer differences between glaze ice and snow-related samples than dielectric constant alone, whereas dry and wet snow require combined consideration of dielectric constant and loss. A preliminary two-step hierarchical framework is therefore proposed for the tested sample set. Further validation over broader frequency ranges and conductor-like geometries is required before practical application. Full article

Flexible electrolyte-gated synaptic field-effect transistors (EGFETs) have emerged as a promising platform for neuromorphic visual systems, owing to their low-voltage operation, diverse synaptic plasticity, and exceptional mechanical flexibility. In particular, gel-based electrolytes, including hydrogels and ion gels, play a pivotal role as functional gate dielectrics, enabling efficient ion transport and strong ion–electron coupling through electric double-layer (EDL) formation. By leveraging these unique properties at the semiconductor/gel interface, EGFETs can effectively emulate essential biological synaptic behaviors, including short-term and long-term plasticity under optical stimulation. The inherent compatibility of EGFETs with a broad range of semiconductor channels, gel electrolytes, and flexible substrates enables the development of wearable and conformable neuromorphic platforms that seamlessly integrate sensing, memory, and signal processing within a single device architecture. Recent advances in gel material engineering, such as polymer network design, ionic modulation, and nanofiller incorporation, have significantly improved ion transport dynamics, interfacial stability, and device performance. Despite remaining challenges related to ion migration stability, multi-physical field coupling, and large-area device uniformity, these developments have substantially advanced the practical potential of gel-based systems. This review provides a comprehensive overview of the operating mechanisms, gel-based material systems, synaptic functionalities, mechanical reliability, and future prospects of flexible electrolyte-gated visual synapses, highlighting their considerable potential for next-generation intelligent perception and artificial vision technologies. Full article

Dielectric elastomer actuators (DEAs) are attracting much attention as candidates for next-generation flexible actuation. Among various DE matrices, polyacrylate rubber (AR) is especially promising owing to their intrinsically high dielectric constant (ε_r) and good mechanical performance. In particular, its mechanical behavior is close to that of porcine bladder tissue, making it a potentially good material for soft biomedical actuators for artificial bladder constructs. To achieve high actuated strain, which requires high ε_r, high breakdown strength, and low elastic modulus, an AR DE composite filled with silane-functionalized TiO₂ was fabricated, exhibiting good electromechanical performance enabled by strengthened interfacial polarization. To improve compatibility between TiO₂ and AR matrix, TiO₂ was preferentially modified with a silane coupling agent (CA) that features a double bond as its functional group, which can be introduced on TiO₂ surface and participate in vulcanization with AR, thereby forming co-crosslinking bridges that strengthen interfacial bonding, improve filler dispersion, and increase interfacial polarizability within the matrix. As a result, at relatively low filler loadings, the composite exhibits a significantly increased ε_r, while maintaining low modulus, low dielectric loss and high elasticity. The 10 CA@TiO₂/AR composite exhibits a maximal actuated strain of 7.9% at 31.9 kV/mm without pre-stretch, which is 1.48 times that of pure AR and 1.32 times that of the 10 TiO₂/AR composite. Full article

This paper presents the design, modeling, and numerical validation of a compact artificial magnetic conductor (AMC)–backed flexible UHF RFID tag antenna intended for on-body biomedical and wearable sensing applications. Human tissue proximity typically causes severe detuning, radiation efficiency degradation, and increased specific absorption rate (SAR) for conventional RFID tag antennas. To address these limitations, a miniaturized AMC metasurface based on a modified Jerusalem-cross geometry with meandered and interdigitated features is developed on a high-permittivity biocompatible substrate using CST Studio Software (2025). Full-wave simulations demonstrate that the proposed design, with an ultra-compact footprint of 0.0246 λ² (32.12 mm × 64.24 mm), functions as an effective shielding element, significantly enhancing the tag antenna gain and reading range by an order of magnitude compared to conventional on-body tags, while simultaneously reducing backward radiation and SAR. The antenna demonstrates robust platform tolerance and excellent isolation from the human body, ensuring high reliability. Fabricated on a thin, flexible, biocompatible, silicon-doped dielectric substrate, this device also functions as an epidermal antenna for on-skin health parameter sampling. This research paves the way for advanced, non-invasive wearable medical devices with superior performance. Full article

High-voltage equipment imposes increasingly stringent demands on polymeric insulating materials, particularly in terms of dielectric strength, space charge suppression, thermo-electrical stability, and interfacial reliability. Conventional polymers are prone to critical failure modes under high electric fields, including electrical treeing, partial discharge, interfacial degradation, and thermo-oxidative aging. This review systematically summarizes recent advances in polymer modification strategies specifically designed for high-voltage applications, covering nanofiller reinforcement, plasma surface engineering, and the development of self-healing insulating polymers. Multi-scale structural control and interface engineering, aligned with the specific requirements of high-voltage environments, have emerged as pivotal approaches to enhance insulation performance. Moreover, the integration of artificial intelligence-driven materials design, digital characterization, and application-oriented modeling holds significant promise for accelerating the development of next-generation high-voltage polymeric systems, thereby offering robust materials solutions for the reliable long-term operation of high-voltage equipment. Full article

Artificial muscles translate the biological principles of motion into soft, adaptive, and multifunctional actuation. This review accordingly highlights research into natural actuation strategies, such as skeletal muscles, muscular hydrostats, spider silk, and plant turgor systems, to reveal the principles underlying energy conversion and deformation control. Building on these insights, polymer-based artificial muscles based on these principles, including pneumatic muscles, dielectric elastomers, and ionic electroactive systems, are described and their capabilities for efficient contraction, bending, and twisting with tunable stiffness and responsiveness are summarized. Furthermore, the abilities of carbon nanotube composites and twisted yarns to amplify nanoscale dimensional changes through hierarchical helical architectures and achieve power and work densities comparable to those of natural muscle are discussed. Finally, the integration of these actuators into soft robotic systems is explored through biomimetic locomotion and manipulation systems ranging from jellyfish-inspired swimmers to octopus-like grippers, gecko-adhesive manipulators, and beetle-inspired flapping wings. Despite rapid progress in the development of artificial muscles, challenges remain in achieving long-term durability, energy efficiency, integrated sensing, and closed-loop control. Therefore, future research should focus on developing intelligent muscular systems that combine actuation, perception, and self-healing to advance progress toward realizing autonomous, lifelike machines that embody the organizational principles of living systems. Full article

Efficient soil moisture monitoring is fundamental to precision agriculture, enabling improved irrigation management, enhanced crop productivity, and sustainable water use. This review comprehensively evaluates soil moisture sensing technologies, classifying them into invasive and non-invasive approaches. The underlying operating principles, strengths, and limitations, as well as documented practical applications, are critically discussed for each technology. Invasive methods, including dielectric sensors, matric potential devices, heat-pulse sensors, and microstructured optical fibres, offer high-resolution data but require careful installation and calibration to account for environmental and soil-specific variables such as texture, salinity, and temperature. Non-invasive technologies—such as microwave remote sensing, electromagnetic induction, and ground-penetrating radar—enable large-scale monitoring without disturbing the soil profile; however, they face challenges in terms of resolution, cost, and data interpretation. Key performance factors across all sensor types include installation methodology, environmental sensitivity, spatial representativeness, and integration with decision-support systems. The review also addresses recent innovations such as biodegradable and Micro–Electro–Mechanical Systems sensors, the incorporation of Internet of Things platforms, and the application of artificial intelligence for enhanced data analytics and sensor calibration. While sensor deployment has demonstrated tangible benefits for irrigation efficiency and yield improvement, widespread adoption remains constrained by technical, economic, and infrastructural barriers, particularly for smallholder farmers. The analysis concludes by identifying research gaps and recommending strategies to facilitate the broader uptake of soil moisture sensors, with a focus on cost reduction, calibration standardisation, and integration into climate-resilient agricultural frameworks. Full article

Electrical insulation systems (EISs) are the principal reliability bottleneck of modern electrical machines (EMs). Among the many stresses acting on insulation, thermal stress is the most pervasive because it accelerates chemical reactions that progressively erode dielectric and mechanical integrity, ultimately dictating service life. As EMs migrate into compact, high-power-density platforms—automotive, aerospace, and industrial drives—designers need lifetime models that are not merely explanatory but actionable, linking operating temperatures and missions to quantified ageing and risk. This review article traces the evolution of thermal-ageing modelling from fundamental chemistry to a practical design tool. The historical empirical lineage of Arrhenius equation, Arrhenius–Dakin model, and Montsinger model is first revisited, clarifying their assumptions, parameter definitions, and the construction of thermal endurance curves. A discussion then follows on extensions that address deviations from first-order kinetics and demonstrate how variable temperature histories can be incorporated through cumulative damage formulations suitable for duty-cycle analysis. Since models are required to be anchored in data, accelerated thermal ageing (ATA) practices on representative specimens are outlined, alongside a description of the Weibull post-processing for deriving percentile lifetimes aligned with design targets. Building upon these foundations, the Physics-of-Failure (PoF) approach is introduced as a reliability-oriented design (ROD) methodology, in which validated lifetime models guide material selection and geometry optimisation while supporting prognostics and health management during operation. The emerging trend towards a hybrid PoF–AI approach is also discussed, which integrates artificial intelligence to identify nonlinear degradation patterns and drifting parameter relationships beyond the reach of empirical models, with physical constraints ensuring that predictions remain consistent with known ageing mechanisms. Such integration enables the learning process to adapt to operational variability and coupled stress effects, thereby improving both the accuracy and physical interpretability of lifetime estimation. The review aims to provide a concise view of models, tests, and workflows that convert thermal-ageing knowledge into robust, design-time decisions. By linking empirical and physics-based insights with modern data-driven learning, these developments support proactive maintenance, sustainable asset management, and extended operational lifetimes for next-generation EMs. Full article

The Ground Penetrating Radar (GPR) method can image dielectric discontinuities in subsurface structures, which cause the reflection of electromagnetic (EM) waves. These discontinuities are imaged as reflectors in GPR sections, often distorted by diffracted energy. To focus the diffracted energy within the GPR sections, migration is commonly used. The migration velocity of GPR data is a low-wavenumber attribute crucial for effective migration. Obtaining a migration velocity model, typically close to a Root Mean Square (RMS) model, from zero-offset (ZO) data requires analysis of the available diffractions, whose density and (x, t) coverage are random. Thus, the accuracy and efficiency of such a velocity model, whether for migration or interval velocity model estimation, are not guaranteed. An alternative is the multipath summation method, which involves the weighted stacking of constant velocity migrated sections. Each stacked section contributes to the final stack, weighted by a scalar value dependent on the constant velocity value used and its relation to its estimated mean velocity of the section. This method effectively focuses the GPR diffractions in the presence of low heterogeneity. However, when the EM velocity varies dramatically, 2D weights are needed. In this study, with the aid of an Artificial Intelligence (AI) algorithm that detects diffractions and uses their kinematic information, we generate a diffraction velocity model. This model is then used to assign 2D weights for the weighted multipath summation, aiming to focus the scattered energy within the GPR section. We describe this methodology and demonstrate its application in enhancing the lateral continuity of reflections. We compare it with the 1D multipath summation using simulated data and present its application on marble assessment GPR data for imaging cracks and discontinuities in the subsurface structure. Full article

This study presents an innovative bimodal approach for laboratory partial discharge (PD) analysis using a YOLOv8-based convolutional neural network (CNN). The main contribution consists, first, in the transformation of a conventional DDX-type electrical detector into a smart and autonomous data source. By training the CNN, a system capable of automatically reading and interpreting the data from the detector display—discharge magnitude and applied voltage—is developed, achieving an average training accuracy of 0.91 and converting a passive instrument into a digitalized and structured data source. Second, and simultaneously, an optical visualization system captures direct images of the PDs with a high-resolution camera, allowing for their morphological characterization and spatial distribution. For electrical voltages of 10, 13, and 16 kV, PDs were detected with a confidence level of up to 0.92. The fusion of quantitative information intelligently extracted from the electrical detector with qualitative characterization from optical analysis offers a more complete and robust automated diagnosis of the origin and severity of PDs. Full article

Amorphous solid dispersions (ASDs) represent a promising formulation strategy for improving the solubility and bioavailability of poorly water-soluble drugs, a major challenge in pharmaceutical development. This review provides a comprehensive analysis of the physicochemical principles underlying ASD stability, with a focus on drug–polymer miscibility, molecular mobility, and thermodynamic properties. The main manufacturing techniques including hot-melt extrusion, spray drying, and KinetiSol^® dispersing are discussed for their impact on formulation homogeneity and scalability. Recent advances in excipient selection, molecular modeling, and in silico predictive approaches have transformed ASD design, reducing dependence on traditional trial-and-error methods. Furthermore, machine learning and artificial intelligence (AI)-based computational platforms are reshaping formulation strategies by enabling accurate predictions of drug–polymer interactions and physical stability. Advanced characterization methods such as solid-state NMR, IR, and dielectric spectroscopy provide valuable insights into phase separation and recrystallization. Despite these technological innovations, ensuring long-term stability and maintaining supersaturation remain significant challenges for ASDs. Integrated formulation design frameworks, including PBPK modeling and accelerated stability testing, offer potential solutions to address these issues. Future research should emphasize interdisciplinary collaboration, leveraging computational advancements together with experimental validation to refine formulation strategies and accelerate clinical translation. The scientists can unlock the full therapeutic potential with emerging technologies and a data-driven approach. Full article

This work presents the development and characterization of a flexible capacitive electrode for non-contact ECG acquisition, fabricated using a simple and cost-effective method from readily available materials. The electrode consists of a multilayer structure with a copper conductor laminated by a polyimide (Kapton^®) dielectric layer on a polyurethane support. The impedance and capacitance of the electrode were evaluated under varying textile moisture levels with artificial sweat, as well as after exposure to common disinfectants including ethyl alcohol and iodine tincture. Electrochemical impedance spectroscopy (EIS) and broadband impedance measurements (10⁻¹–10⁵ Hz) confirmed stable capacitive behavior, moderate sensitivity to moisture, and chemical stability of the Kapton–copper interface under conditions simulating repeated use. A custom front-end readout circuit was implemented to demonstrate through-textile ECG signal acquisition. Simulator tests reproduced characteristic waveform patterns, and preliminary volunteer recordings confirmed the feasibility of through-textile acquisition. These results highlight the promise of the electrode as a low-cost platform for future wearable biosignal monitoring technical research. Full article

This paper proposes wideband circularly polarized (CP) 1-D connected array antennas with slant slot feeders and gradient artificial dielectric layers (ADLs). The slant slot feeder introduces an identical electric field (E-field) along the x- and y-directions. Three slabs consisting of multiple ADLs are stacked above the slot feeder. Due to the different boundary conditions of a 1-D connected array in the $zx$- and $zy$-planes, the guided wave in the slabs exhibits different multipath lengths along the x- and y-directions, leading to a 90° phase difference between the $E_x$ and $E_y$ components. Moreover, the cascaded slabs are designed with gradient effective permittivities for a gradual impedance transition from the guided mode to the radiating mode, allowing for wideband matching and CP performance. To validate the proposed design approach, an 8 × 1 array was fabricated and measured. The antenna shows a 1.96:1 (10.1–20 GHz) impedance bandwidth (VSWR < 2) and a 1.46:1 (12–17.5 GHz) 3 dB axial ratio bandwidth in measurement. The array exhibits an average right-hand CP boresight gain of 12.39 dBic. Moreover, we produced a frequency-invariant beam pattern with an average half-power beamwidth (HPBW) of 24.77° and a standard deviation below 3.63° over 12–18 GHz for the target pattern, with a HPBW of 26°, demonstrating wideband electronic warfare performance using the proposed array. Full article