Search Results (2,875)

Fiber Bragg Grating (FBG) sensors are widely used for strain and temperature monitoring due to their high sensitivity, compact size, electromagnetic immunity, and multiplexing capability. While conventional FBG interrogators remain bulky and costly, Photonic Integrated Circuit (PIC) platforms provide a promising route toward compact, scalable, and low-power FBG interrogation. However, the choice of architecture strongly determines the achievable resolution, bandwidth, multiplexing capacity, and robustness. This review compares on-chip demodulation architectures, evaluating their performance in resolution, bandwidth, and interrogation speed. We show that the optimal architecture depends strongly on the application: AWG-based schemes excel in compact, multi-FBG readout; ring-resonator systems are highly effective for tunable filtering; and interferometric phase-domain schemes offer the highest sensitivity for dynamic strain sensing. Despite these architectural advances, practical deployment remains constrained by system-level bottlenecks. These challenges primarily include source/detector integration, fiber–chip coupling, packaging robustness, and thermal drift. Overcoming these barriers requires a shift in future development from isolated photonic-device optimization toward comprehensive, system-level co-design. Full article

Structural damage classification in composite UAV wings is a key challenge in Structural Health Monitoring (SHM), particularly under barely visible impact damage conditions. Fiber Bragg Grating (FBG) sensor networks provide high-resolution strain data; however, systematic experimental benchmarking of lightweight neural architectures trained on real FBG datasets remains limited, especially under sensor degradation scenarios. This work presents a four-phase benchmarking study of Multilayer Perceptron (MLP) configurations using strain measurements from a composite UAV wing instrumented with 32 FBG sensors across five damage states and 210 loading experiments. The framework evaluates optimization strategies, hyperparameter sensitivity, architectural depth, and robustness under controlled sensor dropout, Gaussian noise, and wavelength drift perturbations. Results indicate that compact architectures with progressive dimensional reduction (256–128–64) trained using adaptive optimizers (AdamW and Nadam) achieve the best balance between macro-F1 performance (up to 0.85 during validation), stability, and computational efficiency. Robustness analysis shows gradual performance degradation under sensor loss, suggesting distributed strain-field learning. These findings provide practical guidelines for selecting computationally efficient and robust neural models for deployable FBG-based SHM systems in aerospace applications. Full article

This study focused on a three-dimensional cross-linked hydrophobic association (PS) hydrogel framework. Phytic acid (PA) was selected as both a dopant and an antifreeze agent, and it was combined with an ethylene glycol/water binary solvent to construct a dual antifreeze system. The resulting composite conductive hydrogel, E/PS/PA-PPy, exhibited synergistically enhanced electrical conductivity, mechanical strength, and antifreeze properties. At a PA concentration of 0.1 M, a structurally uniform and ordered three-dimensional network was formed. The PS/PA-PPy hydrogel exhibited an elongation at break of 2595.7% and a high conductivity of 1.8 S/m, while maintaining excellent flexibility and adhesion. Owing to the synergistic antifreeze effect, the freezing point of the E/PS/PA-PPy hydrogel was reduced to −42.3 °C, and after 35 days of room-temperature storage, the weight loss was less than 7%, indicating outstanding water retention. The assembled flexible strain sensor exhibited a sensitivity of 2.09, with response and recovery times both less than 0.25 s. Notably, it exhibited good cyclic stability and accurately monitored human movements. Furthermore, the sensing performance remained stable without significant attenuation even at −20 °C. The results demonstrate the broad application prospects of the hydrogel in flexible electronics such as wearable health monitoring systems and human–machine interfaces in extreme environments. Full article

Self-healing materials have attracted increasing attention as a strategy to enhance durability, extend service life, and reduce maintenance in advanced material systems. Among these, cellulose-based self-healing materials represent a sophisticated intersection between sustainable macromolecular chemistry and adaptive materials science. This review provides a synthesis of recent advancements in the field, systematically categorizing materials derived from cellulose raw materials. We evaluate the fundamental chemical strategies employed to achieve autonomous repair, distinguishing between extrinsic mechanisms—utilizing cellulose-based micro/nano-capsules to sequester healing agents—and intrinsic mechanisms governed by dynamic covalent chemistry (Schiff-base, boronic ester, Diels–Alder) and supramolecular interactions (hydrogen bonding, metal–ligand coordination, and host–guest assemblies). The analysis highlights how cellulose’s hierarchical structure and abundant surface functionality are leveraged to overcome the traditional trade-off between mechanical toughness and healing efficiency. Particular emphasis is placed on the transition from simple structural hydrogels to sophisticated multifunctional systems. These include ultra-stretchable strain and pressure sensors for e-skin applications, biocompatible and injectable matrices for chronic wound management and stem cell delivery, and advanced anti-freezing eutectogels for performance in extreme environments. Furthermore, we explore the integration of cellulose into traditional sectors, such as self-healing concrete utilizing microbe-induced calcification and smart, eco-friendly coatings for corrosion protection. Finally, we discuss critical challenges, including environmental stability, scalability, and the development of standardized evaluation protocols, providing a roadmap for the next generation of bio-derived, sustainable and intelligent materials. Full article

Micro- and nanoscale mass sensing is crucial for applications such as molecular detection and wearable monitoring. However, the observation of mass perturbations in flexible composite structures requires systematic theoretical evaluation. This study develops a dual-mode vibration-based mass-sensing model based on a film–substrate composite strip. By releasing and re-stretching pre-strain in the soft substrate, the ribbon can reversibly switch between a two-dimensional flat configuration (Mode 1) and a three-dimensional buckled configuration (Mode 2), leading to distinct dynamic responses. Under a finite-deformation Euler–Bernoulli beam assumption, displacement fields and kinematic relations are formulated for both configurations. An energy-based approach is employed to decompose the total energy into stretching and bending contributions, while an added-mass block is incorporated into the kinetic energy as a lumped mass. The governing equations of motion are derived using the Lagrange equations and the Hamiltonian function. Based on these results, the influence of the added mass on displacement signatures is examined, and the mode-dependent observability in the flat versus buckled states is compared, providing an analytical basis for mass sensor evaluation. Full article

This study proposes a knee joint motion detection method based on overlapping spectrum demodulation using fiber Bragg grating (FBG) technology. A flexible FBG encapsulated with polydimethylsiloxane (PDMS) is attached to the joint surface. Axial strain in the FBG sensor is generated due to the bending and extension movements of the joint, which leads to a central reflection wavelength shift of the FBG sensor. The overlapping spectrum between the FBG reflection and the output of a tunable fiber laser is related to the wavelength shift of the FBG. The variation is expressed as the changes in reflected optical power received by an optical power meter. It transforms complex spectral analysis into intuitive optical power measurement for demodulating the reflected wavelength of the FBG sensor. The relationship between the optical power of the overlapping spectrum and wavelength shift of the FBG induced by joint motion is theoretically and experimentally analyzed. The real-time demodulation of joint motion is realized based on this relationship. Experimental results demonstrate that the system exhibits good repeatability in monitoring knee joint motion. The performance and practical potential of the system are evaluated through a quantitative comparison with existing techniques and an analysis of its current limitations. Full article

This paper reports on the performance of an optical voltage transducer (MVT) module after undergoing lightning impulse withstand tests. The device was designed to monitor the output voltage of a dedicated capacitive voltage divider (CVD) to facilitate a voltage sensor dedicated for 132-kV high voltage (HV) networks. Hard piezoelectric transducer (PZT) and fiber Bragg grating (FBG) technologies were combined in the module to serve as a voltage-to-strain-to-wavelength converter. The FBG peak wavelength shifts were calibrated against the input voltage to provide precise measurements of the network voltage. The module was subjected to lightning impulse withstand tests as per the requirements of the IEC 60044-7 and IEC 60060-1 standards, and the impact of the lightning impulses on the performance of the MVT module was evaluated based on the accuracy tests performed before and after the lightning impulse tests. The experimental results demonstrated that the MVT module successfully withstood the lightning impulse tests without any disruptive discharges or voltage collapses. The performance of the module was not affected by the lightning impulse tests within the practical constraints of the reference measuring equipment: its amplitude and phase errors remained within the original limits of ±0.1% and ±0.1° at 80–120% of the rated voltage, and below ±4% and ±2° at 2% of the rated voltage, respectively. Full article

In this study, poly(vinylpyrrolidone) (PVP)-modified liquid metal (LM) particles were formulated into a mixed-solvent system comprising ethanol (EtOH), 1,2-propanediol (1,2-PG), and a trace amount of N,N-dimethylformamide (DMF). This design addresses the instability, poor wetting/adhesion on polydimethylsiloxane (PDMS), and limited rheological tunability of conventional LM inks for screen printing. By regulating solvent evaporation during drying, the system enables coordinated control over wettability, viscosity, shear-thinning behavior, and drying-induced film formation. At an LM:PVP weight ratio of 20:1, the contact angle on PDMS decreased from 115° to 17.8°. The ink exhibited pronounced shear-thinning characteristics with tunable viscosity in the range of 1000–3000 cP, meeting the screen-printing requirements of facile mesh passage and rapid setting. Following laser activation, the printed conductive patterns demonstrated stable electrical performance, with a resistance drift of less than 1% after 14 days of storage and a ΔR/R₀ of less than 1% after 3000 bending cycles at a bending diameter of 1 cm. Furthermore, a resistance drift of less than 3% was observed after 1000 stretching cycles at 30% strain. This study proposes a viable materials and processing strategy for the reliable screen printing of LM:PVP ink on PDMS substrates toward flexible conductive electronics. The motion-monitoring test is presented only as a preliminary proof-of-concept demonstration of motion-induced electrical resistance response, rather than as a sensor performance evaluation. Full article

With the rapid development of flexible electronics technology, flexible wearable sensors based on Lanthanum Strontium Manganese Oxide (La_1-xSr_xMnO₃) have garnered extensive attention in recent years due to their excellent multi-functional integration, environmental stability and biocompatibility. This review systematically analyzes the preparation methods, process optimization strategies, multi-performance integration technologies, and the expansion of the application field of La_1-xSr_xMnO₃-based flexible sensors. Firstly, the basic characteristics and sensing mechanism of the La_1-xSr_xMnO₃ material were presented, including its temperature sensitivity, strain response characteristics, and magnetoresistance effect. Secondly, the fabrication process of flexible sensors was elaborately discussed, with a focus on analyzing crucial technologies, such as laser induction and transfer printing technology. Subsequently, the strategies for regulating the electrical, thermal, and mechanical properties of materials through element doping, along with the multimodal sensing integration and signal decoupling methods, were expounded. Furthermore, the actual performance of this type of sensor in fields such as health monitoring, human–computer interaction, and extreme environment applications was summarized. Finally, the challenges and future development directions of La_1-xSr_xMnO₃-based flexible sensors are outlined, providing theoretical references for the design and optimization of next-generation flexible electronic devices. Full article

The next generation of naval and defense systems will strain current naval ship cooling systems. Throughout its life-cycle, this strain will alter the behavior of the physical system, and any virtual representation of the system will become outdated due to component aging. Digital twins are a trending tool that can assimilate real-time sensor data to tailor a virtual representation to its physical counterpart. The online faithful virtual representation of the physical system provided by digital twins can be used for real-time system optimizations and proactive fault detection, diagnostics, and control adjustments, alleviating the stress of component aging. To support these complex power systems throughout their lifecycles, data-driven solutions for digital twin tuning will become essential. This paper investigates the application of a parameter-tuning digital twin framework to enhance the performance of a multi-physics model. The digital twin framework comprises a digital twin tuning scheme, a physical testbed designed to emulate the cooling system of a ship, and a multi-physics representation of that system. The digital twin tuning scheme leverages a swarm of particles and online sensor data to evaluate permutations of parameters to update the digital representation periodically. The digital twin framework was applied to a physical system to provide experimental data results demonstrating the usefulness of the tuning system. The physical system was designed and constructed to emulate the heat generation and dissipation from 6 liquid-cooled power converters under loads ranging from 10–15 kW at 99% efficiency. Two scenarios were applied to evaluate the performance of the digital twin framework. Results demonstrate that the digital twin framework can adapt to system changes in real-time and improve the accuracy of the related virtual representation by more than 90% when measured at four points of the system under test. Full article

Acoustoelasticity provides the physical sensing principle for ultrasonic stress measurement. However, most existing formulations are restricted to isotropic media, simple stress conditions, and Cartesian coordinate systems, which limits their applicability in practical sensing scenarios involving curved and anisotropic structures. In this work, a general tensorial formulation of acoustoelasticity is developed based on the theory of incremental deformation. The proposed governing equations describe the motion of incremental displacement with explicit dependence on initial stress or strain, and are applicable to materials with arbitrary symmetry and general initial stress states. Owing to its coordinate-independent tensorial nature, the formulation can be expressed in any curvilinear coordinate system. To facilitate practical ultrasonic sensing applications, the general equations are further expanded in a cylindrical coordinate system for orthotropic materials. This enables the analysis of elastic wave propagation in curved structures such as pipelines, pressure vessels, and boreholes. The formulation establishes a direct relationship between initial stress and effective elastic properties, which determine wave velocities measurable by ultrasonic sensors, such as time-of-flight and phase velocity. The proposed approach provides a rigorous theoretical foundation for ultrasonic stress sensing and nondestructive testing, particularly for curved and anisotropic structures, and supports improved accuracy in sensor-based stress evaluation. Full article

Polyvinyl alcohol (PVA)-based hydrogels suffer from insufficient mechanical strength, while aramid nanofibers (ANF) have intrinsic insulation that limits their sensing applications, and the synergistic effect of composite fillers remains underexplored. This study aims to develop a multifunctional PVA/ANF/NaCl composite organohydrogel for high-performance flexible sensors. The gel was fabricated via freeze–thaw crosslinking, solvent exchange and NaCl impregnation, with systematic investigations of its microstructure, mechanical, electrical and multifunctional sensing properties, and a corresponding triboelectric nanogenerator (TENG) and self-powered handwriting recognition system were constructed. Results show that 2% ANF significantly enhances the gel’s mechanical performance, 0.5 M NaCl achieves optimal mechanical-electrical balance, the gel-based sensor exhibits excellent distance, pressure and strain sensing with high cyclic stability, the TENG delivers stable electrical output, and the recognition system achieves 95% accuracy on the test set. This work provides a new material and design strategy for advanced flexible electronic devices. Full article

Interfacial stress concentration induced by curing shrinkage during the manufacturing of epoxy resin is a primary trigger for micro-nano defect formation and electrical performance degradation in power equipment. To address the computational complexity of traditional viscoelastic models and the thermoelastic behavior wherein the stiffness of the epoxy resin varies with temperature during curing, this paper proposes an improved viscoelastic constitutive model incorporating a thermo-elastic factor. By coupling curing kinetics, heat conduction, chemical shrinkage, and mechanical effects, a multi-physics simulation framework is constructed to describe the complete epoxy curing process, thereby revealing the spatiotemporal evolution of curing stress deformation. To verify the model’s accuracy, an in situ monitoring system based on Fiber Bragg Grating (FBG) sensors was established. A temperature compensation method was utilized to effectively decouple temperature and stress within the complex exothermic curing environment. This study reveals a significant strain gradient effect during the resin curing process. Experimental measurements indicate strains of 21,609 με and 5800 με at the interface and surface, respectively, with numerical simulations exhibiting high agreement with the experimental data. This research not only provides an efficient simulation approach for predicting curing stress but also offers a theoretical basis for the crack-resistant structural design of high-performance epoxy-based power equipment. Full article

Roll forming is a sheet metal forming process characterised by high production rates and high material utilisation. Process-inherent inhomogeneous longitudinal strain distribution across the profiles cross-section requires the use of straighteners to maintain product quality within specifications during manufacturing. The straightening process is adjusted iteratively based on the expertise of line operators. A shortage of skilled workers leads to a loss of experience-based know-how in roll forming operations. To mitigate the effects on process productivity, machine learning (ML) can be applied to provide assistance to less experienced operators. In this context, force and position signals are recorded on a sensorically equipped straightener in order to predict corrective adjustments for line operators using convolutional neural networks. Furthermore, the integration of numerically generated data is investigated to reduce the required amount of labelled experimental data. Applying a Transfer Learning (TL) approach, the incorporation of numerical data reduces the mean error by 20.81% and the mean standard deviation by 36.62% for small experimental datasets. Full article

Facing the challenges in field monitoring of the mechanical response of geogrids in asphalt pavements, this study integrated two types of Fiber Bragg Grating (FBG) sensors, unarmored and armored, into geogrids using the pillar-stitching technique on industrial warp-knitting production lines. The integrated FBG-geogrid systems were comprehensively evaluated in both wound and flattened configurations, enabling the selection of a sensor type suitable for industrial production. After precise strain calibration, a full-scale field damage test was performed during the construction of the Qu-Gang Expressway in Hebei Province, China. The results demonstrate that the helical steel armor layer significantly enhances the mechanical durability of the FBG sensor. Specifically, the armored sensor maintained stable optical transmission over its entire 60-m length, with an average performance retention rate of 98.86% in the flattened state. Moreover, a strong linear correlation was established between the wavelength shift of the armored FBG sensor and the tensile strain of the geogrids. In contrast, the unarmored FBG sensor underwent irreversible shear deformation during production and contained at least two breakpoints. Additionally, a protection scheme employing fiberglass-reinforced silicone rubber on the hot side and standard silicone rubber on the cold side effectively shielded the sensors from high-temperature and compaction loads during asphalt paving. Consequently, the proposed FBG-geogrid integration method and the corresponding field protection strategy provide technical support for the real-time monitoring of geogrid performance in asphalt pavements and have significant engineering value. Full article