Search Results (75)

Structural colors generated by interference, diffraction, or light scattering offer vivid visual effects without dyes or electronic components, making them promising for flexible optical sensing. This work reports a simple stretch–plasma–release (S-P-R) strategy to fabricate wrinkled photonic elastomers (WPEs). The flexible periodic structures exhibit mechanically responsive structural colors, as tensile strain alters the grating period, generating optical signals that can be visualized and quantified by spectroscopy. The wrinkle period is tunable in the range of 0.4–3.42 μm by adjusting plasma power, exposure time, pre-stretch ratio, and film thickness. A dumbbell-shaped substrate design reduces edge-induced stress concentration. It shows improved wrinkle uniformity, with the coefficient of variation reduced from 6.64% to 2.74%, and experimental colors agreeing well with modified Bragg condition predictions. The reflection peak shows a significant shift from 356 nm to 658 nm with varying viewing angles. Patterned plasma treatment enables the selective generation of wrinkled structures, producing bright color patterns. The structural color can be fully erased at a critical strain of 20% and recovered upon release, remaining stable over multiple loading–unloading cycles. With excellent mechanical compliance and optical tunability, these materials are well-suited for integration with hydrogel-based systems and show promise for wearable devices, security marking, and anti-counterfeiting applications. Full article

Background/Objectives: Proton therapy has emerged as an advanced radiotherapy modality due to its unique physical dose distribution and its distinct radiobiological properties. The finite range of protons in tissue enables highly conformal dose delivery with minimal exit dose, significantly reducing irradiation of surrounding normal tissues compared to photon-based radiotherapy. Beyond these physical advantages, proton beams exhibit a spatially varying linear energy transfer that increases toward the distal edge of the spread-out Bragg peak, leading to clustered and complex DNA damage that is more difficult for cancer cells to repair. Methods: This review integrates experimental, computational, and clinical evidence to examine how proton-induced DNA damage, relative biological effectiveness, oxygen effects, and non-targeted responses contribute to tumor control and normal tissue sparing. Results: Comparative analyses with photon intensity-modulated radiotherapy demonstrate consistent reductions in acute and late toxicities across multiple tumor sites, particularly in pediatric patients and in tumors located near critical organs. The review also discusses emerging technologies, including pencil beam scanning, image-guided and adaptive proton therapy, compact accelerator systems, and ultra-high dose rate FLASH proton therapy, which collectively aim to enhance treatment precision, biological effectiveness, and accessibility. Conclusions: Together, these developments support proton therapy as a rapidly evolving modality with significant potential to improve therapeutic outcomes in modern oncology. Full article

The evolution of next-generation urban infrastructure necessitates the deployment of intelligent pavement systems capable of real-time data acquisition, adaptive response, and predictive analytics. This article presents the design, implementation, and performance evaluation of the smart pavement system incorporating multimodal embedded sensors for traffic density analysis, structural health monitoring, and environmental surveillance. SPS integrates piezoelectric transducers, micro-electro-mechanical system accelerometers, inductive loop coils, fiber Bragg grating (FBG) sensors, and capacitive moisture and temperature sensors within the asphalt and sub-base layers, forming a distributed sensor network that interfaces with an edge-AI-enabled data acquisition and control module. Each sensor node performs localized pre-processing using low-power microcontrollers and transmits spatiotemporal data to a centralized IoT gateway over an adaptive mesh topology via long-range wide-area network or 5G-Vehicle-to-Everything protocols. Data fusion algorithms employing Kalman filters, sensor drift compensation models, and deep convolutional recurrent neural networks enable accurate classification of vehicular loads, traffic, and anomaly detection. Additionally, the system supports real-time air pollutant detection (e.g., NO₂, CO, and PM2.5) using embedded electrochemical and optical gas sensors linked to mobile roadside units. Field deployments on a 1.2 km highway testbed demonstrate the system’s capability to achieve 95.7% classification accuracy for vehicle type recognition, ±1.5 mm resolution in rut depth measurement, and ±0.2 °C thermal sensitivity across dynamic weather conditions. Predictive analytics driven by long short-term memory networks yield a 21.4% improvement in maintenance planning accuracy, significantly reducing unplanned downtimes and repair costs. The architecture also supports vehicle-to-infrastructure feedback loops for adaptive traffic signal control and incident response. The proposed SPS architecture demonstrates a scalable and resilient framework for cyber-physical infrastructure, paving the way for smart cities that are responsive, efficient, and sustainable. Full article

This article presents a comparison of empirical and simulation studies and the parameters declared by the membrane manufacturer. The analysis concludes that these values differ at each stage. Therefore, a numerical and simulation analysis of an optimal flat membrane was undertaken, which will successfully perform measurement functions across the full pressure range without causing inelastic deformations based on a membrane made of 316 L stainless steel with the following mechanical parameters: Young’s modulus $\mathrm{E}=2\times {10}^{11} \mathrm{P}\mathrm{a}$, Poisson’s ratio $\mathsf{\nu }=0.28$, density $\mathsf{\rho }=7980 \mathrm{k}\mathrm{g}/{\mathrm{m}}^3$, and yield strength 2.8 × 10⁸ Pa. A diaphragm with an outer diameter of 25.4 mm, an inner diameter of $2.22\times {10}^{-4} \mathrm{m}$, and a thickness of t = $5.08\times {10}^{-5} \mathrm{m}$ was designed for a pressure sensor in vacuum extinguishing chambers of medium-voltage devices, with a pressure difference $\Delta \mathrm{p}$ from 7 × 10⁻⁴ Pa to 1.013 × 10⁵ Pa. Finite element method (FEM) simulations in the COMSOL Multiphysics environment showed maximum von Mises reduced stresses 1.96 × 10⁸ Pa below the yield strength, confirming operation in the linear-elastic range. The central deflection, described analytically by the equation $\mathrm{y}={\displaystyle \frac{3(1-{\mathsf{\nu }}^2)\mathrm{P}{\mathrm{r}}^4}{16\mathrm{E}{\mathrm{t}}^3}}$, increased fivefold with an increase in diameter to $3.81\times {10}^{-2} \mathrm{m}$ (active area A = 1.14 × 10⁻³ m² compared to 5.07 × 10⁻⁴ m²), achieving a metrological sensitivity of 9.1 × 10⁻¹⁰ m/Pa. Experimental studies integrated with Bragg FBG and epoxy adhesive (E = 5 × 10⁹ Pa, tensile strength $4.2\times {10}^7 \mathrm{P}\mathrm{a}$) revealed a significant deviation from the manufacturer’s catalog data (e.g., deflection of $2.0\times {10}^{-5} \mathrm{m}$ at $6.89\times {10}^2 \mathrm{P}\mathrm{a}$), resulting from uneven bonding and a lack of coaxiality. Corrugated membranes with t = $2.0\times {10}^{-5} \mathrm{m}$ exceeded plasticity, while the optimized configuration of a smooth membrane with rounded adhesive edges ($\mathrm{R}=1\times {10}^{-4} \mathrm{m}$) enabled precise pressure monitoring below ${10}^{-1} \mathrm{P}\mathrm{a}$, despite technological restrictions on assembly and miniaturization. Full article

Japanese swords have a history of more than one thousand years and are recognized as metallic art objects. The sword-making process is not clearly understood, especially for old swords made before about 1600 A.D. Knowledge of structural information such as crystallite sizes and anisotropy is important to understand the sword characteristics and the sword-making process. Bragg-edge transmission imaging is a useful noninvasive method that can extract this structural information continuously over a wide area of the sword. Neutron CT is powerful enough to detect quenched areas, voids, and precipitates. Using both methods, we measured more than 10 swords and obtained information on the two-dimensional crystallite size distribution, anisotropy parameter, lattice plane spacing, and quenched regions. Comparison of the results indicated the following features: the crystallite size distributions showed two patterns: an almost uniform distribution of small-sized crystallites, and mixed distributions of large- and small-sized crystallites. The patterns were observed in different eras and places. The preferred orientation showed different patterns, and strain areas due to quenching were observed in many swords. The quenched area showed a trend that the quenching was weaker for old swords than newer ones. CT images showed the boundaries of the quenched regions and a void in the layered structure for one sword, for which a layered structure was confirmed. Full article

With the increasing demand for safety monitoring of offshore engineering structures, traditional single-modality sensing and centralized data processing models face challenges such as insufficient real-time performance and weak anti-interference abilities in complex marine environments. This research proposes an intelligent monitoring system based on multimodal sensor fusion and edge computing, aiming to achieve high-precision real-time diagnosis of offshore structure damage. The research plans to construct multimodal sensors through sensors such as stress change sensors, vibration sensors, ultrasonic sensors, and fiber Bragg grating sensors. A distributed wireless sensor network will be adopted to realize the transmission of sensor data, reduce the complexity of wiring, and meet the requirements of high humidity and strong corrosion in the marine environment. At the edge computing layer, lightweight deep learning models (such as multi-branch Transformer) and D-S evidence theory fusion algorithms will be deployed to achieve real-time feature extraction of multi-source data and damage feature fusion, supporting the intelligent identification of typical damages such as cracks, corrosion, and deformation. Experiments will simulate the coupled working conditions of wave impact, seismic load, and corrosion to verify the real-time performance and accuracy of the system. The expected results can provide a low-latency and highly robust edge-intelligent solution for the health monitoring of offshore engineering structures and promote the deep integration of sensor networks and artificial intelligence in Industry 4.0 scenarios. Full article

Composite engine fan blades are critical aircraft engine components, and their failure can compromise the safe and reliable operation of the entire aircraft. To enhance aircraft availability and safety within a condition-based maintenance framework, effective methods are needed to identify damage and monitor the blades’ condition throughout manufacturing and operation. This paper presents a unique experimental framework for real-time monitoring of composite engine blades utilizing state-of-the-art structural health monitoring (SHM) technologies, discussing the associated benefits and challenges. A case study is conducted on a representative Foreign Object Damage (FOD) panel, a substructure of a LEAP (Leading Edge Aviation Propulsion) engine fan blade, which is a curved, 3D-woven Carbon Fiber Reinforced Polymer (CFRP) panel with a secondary bonded steel leading edge. The loading scheme involves incrementally increasing, cyclic 4-point bending (loading–unloading) to induce controlled damage growth, simulating in-operation conditions and allowing evaluation of flexural properties before and after degradation. External damage, simulating foreign object impact common during flight, is introduced using a drop tower apparatus either before or during testing. The panel’s condition is monitored in-situ and in real time by two types of SHM sensors: screen-printed piezoelectric sensors for guided ultrasonic wave propagation studies and surface-bonded Fiber Bragg Grating (FBG) strain sensors. Experiments are conducted until panel collapse, and degradation is quantified by the reduction in initial stiffness, derived from the experimental load-displacement curves. This paper aims to demonstrate this unique experimental setup and the resulting SHM data, highlighting both the potential and challenges of this SHM framework for monitoring complex composite structures, while an attempt is made at correlating SHM data with structural degradation. Full article

Narrow-linewidth broadband tunable external cavity diode lasers (NBTECDLs), with their broadband tuning range, narrow linewidth, high side-mode suppression ratio (SMSR), and high output power, have become important laser sources in many fields such as optical communication, spectral analysis, wavelength division multiplexing systems, coherent detection, and ultra-high-speed optical interconnection. This paper briefly describes the basic theory of NBTECDLs, introduces NBTECDLs with diffraction grating type, fiber Bragg grating (FBG) type, and waveguide type, and conducts an in-depth analysis on the working principles and performance characteristics of NBTECDLs based on different NBTECDL types. Then, it reviews the latest research progress on Littrow-type, Littman-type, FBG-type, and waveguide-type NBTECDLs in detail and compares and summarizes the characteristics of Littrow-type NBTECDLs, Littman-type NBTECDLs, FBG-type NBTECDLs, and waveguide-type NBTECDLs. Finally, it looks at the structural features, key technologies, optical performance, and application fields of the most cutting-edge research in recent years and summarizes the challenges and future development directions of NBTECDLs. Full article

Carbon fiber reinforced polymer (CFRP) composites are prone to developing localized material loss defects during long-term service, which can severely degrade their mechanical properties and structural reliability. To address this issue, this study proposes a multi-sensor synchronous monitoring method combining embedded fiber Bragg grating (FBG) sensors and surface-mounted electrical resistance strain gauges. First, finite element simulations based on the three-dimensional Hashin damage criterion were performed to simulate the damage initiation and propagation processes in CFRP laminates, revealing the complete damage evolution mechanism from initial defect formation to progressive failure. The simulations were also used to determine the optimal sensor placement strategy. Subsequently, tensile test specimens with prefabricated defects were prepared in accordance with ASTM D3039, and multi-sensor monitoring techniques were employed to capture multi-parameter, dynamic data throughout the damage evolution process. The experimental results indicate that embedded FBG sensors and surface-mounted strain gauges can effectively monitor localized material loss defects within composite laminate structures. Strain gauge measurements showed uniform strain distribution at all measuring points in intact specimens (with deviations less than 5%). In contrast, in defective specimens, strain values at measurement points near the notch edge were significantly higher than those in regions farther from the notch, indicating that the prefabricated defect disrupted fiber continuity and induced stress redistribution. The combined use of surface-mounted strain gauges and embedded FBG sensors was demonstrated to accurately and reliably track the damage evolution behavior of defective CFRP laminates. Full article

Shape-sensing optical fibers have become increasingly important in applications requiring flexible navigation, spatial awareness, and deformation monitoring. Fiber Bragg Grating (FBG) sensors inscribed in multi-core optical fibers have been democratized over the years and nowadays offer a compact and robust platform for shape reconstruction. In this work, we propose a novel, computationally efficient method for determining the 3D tip position of a bent multi-core FBG-based optical fiber using a second-order polynomial approximation of the fiber’s shape. The method begins with a calibration procedure, where polynomial coefficients are fitted for known bend configurations and subsequently modeled as a function of curvature using exponential decay functions. This allows for real-time estimation of the fiber tip position from curvature measurements alone, with no need for iterative numerical solutions or high processing power. The method was validated using miniaturized test structures and achieved sub-millimeter accuracy (<0.1 mm) over a 4.5 mm displacement range. Its simplicity and accuracy make it suitable for embedded or edge-computing applications in confined navigation, structural inspection, and medical robotics. Full article

Structural health monitoring (SHM) plays a vital role in ensuring the safety, durability, and performance of civil infrastructure. This review delves into the significant advancements in optical fiber sensor (OFS) technologies such as Fiber Bragg Gratings, Distributed Temperature Sensing, and Brillouin-based systems, which have emerged as powerful tools for enhancing SHM capabilities. Offering high sensitivity, resistance to electromagnetic interference, and real-time distributed monitoring, these sensors present a superior alternative to conventional methods. This paper also explores the integration of OFSs with Artificial Intelligence (AI), which enables automated damage detection, intelligent data analysis, and predictive maintenance. Through case studies across key infrastructure domains, including bridges, tunnels, high-rise buildings, pipelines, and offshore structures, the review demonstrates the adaptability and scalability of these sensor systems. Moreover, the role of SHM is examined within the broader context of civil and urban infrastructure, where IoT connectivity, AI-driven analytics, and big data platforms converge to create intelligent and responsive infrastructure. While challenges remain, such as installation complexity, calibration issues, and cost, ongoing innovation in hybrid sensor networks, low-power systems, and edge computing points to a promising future. This paper offers a comprehensive amalgamation of current progress and future directions, outlining a strategic path for next-generation SHM in resilient urban environments. Full article

Accurate real-time temperature measurement under extreme thermal-pressure conditions remains challenging in aerospace. Sapphire fiber Bragg gratings (FBGs), exhibiting temperature measurement capabilities up to 1900 °C, demonstrate suitability for such extreme environments. However, the development of a high-performance demodulation system capable of processing sapphire FBG signals over wide spectral ranges at elevated speeds remains a technical challenge. This study presents a real-time FBG signal demodulation system that incorporates an all-dielectric subwavelength grating edge filter. The designed grating, comprising a TiO₂/Si₃N₄ subwavelength unit array, modulates Mie-type electric and magnetic multipole resonances to achieve precisely tailored transmission and reflection spectra. Simulation results indicate that the grating exhibits low ohmic loss, excellent linearity, complementary transmission/reflection characteristics, a wide linear range, and angular-dependent tunability. The designed edge-filter-based demodulation system incorporates dual single-point detectors to simultaneously monitor the transmitted and reflected signals. Leveraging the functional relationship between the center wavelength of the FBG and the detected signals, this system enables high-speed, wide-range interrogation of the center wavelength, thus facilitating real-time demodulation for wide-range temperature sensing. The proposed method and system are validated through theoretical modeling, offering an innovative approach for sapphire FBG signal demodulation under extreme thermal-pressure conditions. Full article

Implementation of edge-filter detection for interrogating optical interferometric ultrasonic sensors is often hindered by the lack of cost-effective laser sources with agile wavelength tunability and good noise performance. The detected signal can also be affected by optical power variations and locking-point drift, negatively affecting the sensor accuracy. Here, we report the use of laser single-sideband generation with a dual-parallel Mach–Zehnder interferometer (DP-MZI) for laser wavelength tuning and locking in edge-filter detection of fiber-optic ultrasonic sensors. We also demonstrate real-time in situ calibration of the sensor response to ultrasound-induced wavelength shift tuning. The DP-MZI is employed to generate a known wavelength modulation of the laser, whose response is used to gauge the sensor response to the ultrasound-induced wavelength shifts in real time and in situ. Experiments were performed on a fiber-optic ultrasonic sensor based on a high-finesse Fabry–Perot interferometer formed by two fiber Bragg gratings. The results demonstrated the effectiveness of the laser locking against laser wavelength drift and temperature variations and the effectiveness of the calibration method against optical power variations and locking-point drift. These techniques can enhance the operational robustness and increase the measurement accuracy of optical ultrasonic sensors. Full article

In multi-source fusion deformation-monitoring methods that utilize fiber Bragg grating (FBG) data and other data types, the lack of FBG constraint points in edge regions often results in inaccuracies in fusion results, thereby impacting the overall deformation-monitoring accuracy. This study proposes a multi-source fusion deformation-monitoring calibration method and develops a calibration model that integrates vision and FBG multi-source fusion data. The core of this model is a normal distribution transform (NDT)–convolutional neural network (CNN)–self-attention (SA) calibration network. This network enhances continuity between points in point clouds using the NDT module, thereby reducing outliers at the edges of the fusion results. Experimental validation shows that this method reduces the absolute error to below 0.2 mm between multi-source fusion calibration results and high-precision measured point clouds, with a confidence interval of 99%. The NDT-CNN-SA network offers significant advantages, with a performance improvement of 36.57% over the CNN network, 14.39% over the CNN–gated recurrent unit (GRU)–convolutional block attention module (CBAM) network, and 9.54% over the CNN–long short term memory (LSTM)–SA network, thereby demonstrating its superior generalization, accuracy, and robustness. This calibration method provides smoother and accurate structural deformation data, supports real-time deformation monitoring, and reduces the impact of assembly deviation on product quality and performance. Full article

Guided waves (GW) allow fast inspection of a large area and hence have received great interest from the structural health monitoring (SHM) community. Fiber Bragg grating (FBG) sensors offer several advantages but their use has been limited for the GW sensing due to its limited sensitivity. FBG sensors in the edge-filtering configuration have overcome this issue with sensitivity and there is a renewed interest in their use. Unfortunately, the FBG sensors and the equipment needed for interrogation is quite expensive, and hence their number is restricted. In the previous work by the authors, the number and location of the actuators was optimized for developing a SHM system with a single sensor and multiple actuators. But through the use of the phenomenon of acoustic coupling, multiple locations on the structure may be interrogated with a single FBG sensor. As a result, a sensor network with multiple sensing locations and a few actuators is feasible and cost effective. This paper develops a two-step methodology for the optimization of an actuator–sensor network harnessing the acoustic coupling ability of FBG sensors. In the first stage, the actuator–sensor network is optimized based on the application demands (coverage with at least three actuator–sensor pairs) and the cost of the instrumentation. In the second stage, an acoustic coupler network is designed to ensure high-fidelity measurements with minimal interference from other bond locations (overlap of measurements) as well as interference from features in the acoustically coupled circuit (fiber end, coupler, etc.). The non-sorting genetic algorithm (NSGA-II) is implemented for finding the optimal solution for both problems. The analytical implementation of the cost function is validated experimentally. The results show that the optimization does indeed have the potential to improve the quality of SHM while reducing the instrumentation costs significantly. Full article