Search Results (330)

This paper provides a comprehensive review of Type IV hydrogen tanks, with a focus on materials, manufacturing technologies and structural issues related to high-pressure hydrogen storage. Recent advances in the use of advanced composite materials, such as carbon fibers and polyamide liners, useful for improving mechanical strength and permeability, have been reviewed. The present review also discusses solutions to reduce hydrogen blistering and embrittlement, as well as exploring geometric optimization methodologies and manufacturing techniques, such as helical winding. Additionally, emerging technologies, such as integrated smart sensors for real-time monitoring of tank performance, are explored. The review concludes with an assessment of future trends and potential solutions to overcome current technical limitations, with the aim of fostering a wider adoption of Type IV tanks in mobility and stationary applications. Full article

The advancement of sports science has heightened demands for precise monitoring of athletes’ technical movements, physiological status, and performance. Optical fiber sensing (OFS) technology, with its unique advantages including high sensitivity, immunity to electromagnetic interference, capability for distributed sensing, and strong biocompatibility, demonstrates significant application potential in sports science. This review systematically examines the technical principles, innovative breakthroughs, and practical application cases of optical fiber sensors in various domains: monitoring key human physiological parameters such as respiration, heart rate, and body temperature; capturing motion and analyzing movement covering muscle activity, joint angles, and gait; integrating within smart sports equipment and protective gear; and monitoring sports apparatus and environments. The value of OFS technology is further analyzed in areas including sports biomechanics analysis, training load monitoring, injury prevention, and rehabilitation optimization. Concurrently, current technical bottlenecks such as the need for enhanced sensitivity, advancements in flexible packaging technologies, cost control, system integration, and miniaturization are discussed. Future development trends involving the integration of OFS with artificial intelligence, the Internet of Things, and new materials are explored, aiming to provide a theoretical foundation for sports medicine and training optimization. Full article

Aldehyde dehydrogenase 1A1 (ALDH1A1) has emerged as a significant biomarker associated with tumor progression, chemoresistance, and poor prognosis in various cancers, including breast, lung, prostate, and lymphoma. Current diagnostic methods for ALDH1A1, such as flow cytometry and ELISA, are limited by long detection times, the need for labeling, and a reduced sensitivity in complex biological matrices. This study presents a novel optical fiber biosensor based on magnesium silicate nanoparticle-doped fibers for the label-free detection of ALDH1A1. The biosensor design incorporated protein G for enhanced antibody orientation and binding efficiency and anti-ALDH1A1 antibodies for specific recognition. Several sensor configurations were fabricated using a semi-distributed interferometer (SDI) format, and their performances were evaluated across a wide concentration range (10 fM–100 nM) in both phosphate-buffered saline (PBS) and fetal bovine serum (FBS). Our findings demonstrated that the inclusion of protein G significantly improved sensor sensitivity and reproducibility, achieving a limit of detection (LoD) of 172 fM in PBS. The sensor also maintained a positive response trend in FBS, indicating its potential applicability in clinically relevant samples. This work introduces the first reported optical fiber biosensor for soluble ALDH1A1 detection, offering a rapid, label-free, and highly sensitive approach suitable for future use in cancer diagnostics. Full article

Fiber optic technologies have strong potential to augment and improve existing areas of sensor performance across many applications. Magnetic sensing, in particular, has attracted significant interest in structural health monitoring and ferromagnetic object detection. However, current technologies such as fluxgate magnetometers and inspection gauges rely on measuring magnetic fields as single-point sensors. By using fiber optic distributed strain sensors in tandem with magnetically biased magnetostrictive material, static and dynamic magnetic fields can be detected across long lengths of sensing fiber. This paper investigates the relationship between Fiber Bragg Grating (FBG)-based strain sensors and the magnetostrictive alloy Metglas^® 2605SC for the distributed detection of static fields for use in a compact cable design. Sentek Instrument’s picoDAS system is used to interrogate the FBG based sensors coupled with Metglas^® that is biased with an alternating sinusoidal magnetic field. The sensing system is then exposed to varied external static magnetic field strengths, and the resultant strain responses are analyzed. A minimum magnetic field strength on the order of 300 nT was able to be resolved and a variety of sensing configurations and conditions were also tested. The sensing system is compact and can be easily cabled as both FBGs and Metglas^® are commercialized and readily acquired. In combination with the robust and distributed nature of fiber sensors, this demonstrates strong promise for new means of magnetic characterization. Full article

Underground electrical conductors, both medium-and high-voltage, play a crucial role in energy infrastructure. However, they present a maintenance challenge due to their difficult access. Unlike overhead installations, these cables remain hidden, making it harder to obtain key parameters, such as their temperature or structural condition, in a simple manner. Current temperature measurement methods, including fiber-optic-based systems (DTS and LTS), involve high costs that limit their feasibility in medium-voltage networks, where more economically accessible alternatives are required. This study introduces an alternative system for monitoring the temperature of underground cables using NTC thermistors. Its design allows for reducing the number of connection conductors for sensors to just four regardless of the number of measurement points. The implemented measurement technique is based on the sequential activation of sensors and the integration of the recorded current to achieve an accurate thermal assessment. The tests conducted validate that this proposal represents an efficient, cost-effective, and highly scalable solution for implementation in electrical distribution networks. Full article

Exhaled breath analysis (EBA) is an advanced, non-invasive diagnostic technique that utilizes volatile organic compounds (VOCs) to detect and monitor various diseases. This review examines EBA’s historical development and current status as a promising diagnostic tool. It highlights the significant contributions of modern methods such as gas chromatography–mass spectrometry (GC-MS), ion mobility spectrometry (IMS), and electronic noses in enhancing the sensitivity and specificity of EBA. Furthermore, it emphasizes the transformative role of nanotechnology and machine learning in improving the diagnostic accuracy of EBA. Despite challenges such as standardization and environmental factors, which must be addressed for the widespread adoption of this technique, EBA shows excellent potential for early disease detection and personalized medicine. The review also highlights the potential of photonic crystal fiber (PCF) sensors, known for their superior sensitivity, in the field of EBA. Full article

Tethered unmanned aerial vehicles (UAVs) operating in terrestrial environments face critical safety challenges from power cable breaks, yet existing solutions—including fiber optic sensing (cost > USD 20,000) and impedance analysis (35% payload increase)—suffer from high cost or heavy weight. This study proposes a dual innovation: a real-time break detection method and a low-cost multi-core parallel sensing system design based on ACS712 Hall sensors, achieving high detection accuracy (100% with zero false positives in tests). Unlike conventional techniques, the approach leverages current differential (ΔI) monitoring across parallel cores, triggering alarms when ΔI exceeds I_rate/2 (e.g., 0.3 A for 0.6 A rated current), corresponding to a voltage deviation ≥ 110 mV (normal baseline ≤ 3 mV). The core innovation lies in the integrated sensing system design: by optimizing the parallel deployment of ACS712 sensors and LMV324-based differential circuits, the solution reduces hardware cost to USD 3 (99.99% lower than fiber optic systems), payload by 18%, and power consumption by 23% compared to traditional methods. Post-fault cable temperatures remain ≤56 °C, ensuring safety margins. The 4-core architecture enhances mean time between failures (MTBF) by 83% over traditional systems, establishing a new paradigm for low-cost, high-reliability sensing systems in terrestrial tethered UAV cable health monitoring. Preliminary theoretical analysis suggests potential extensibility to underwater scenarios with further environmental hardening. Full article

Integrating carbon nanotubes (CNTs) into electrospun polyvinylidene fluoride (PVDF) fibers is a promising approach for developing conductive and multifunctional materials. This study systematically compared two CNT deposition techniques, electrophoretic deposition (EPD) and dip coating (DC), in terms of their effectiveness in modifying the surface of aligned electrospun PVDF mats. Morphological characterization revealed that EPD produced more homogeneous and compact CNT coatings. In contrast, DC resulted in discontinuous and irregular layers regardless of deposition time. A key distinction between the two methods was the tunability of the coating: EPD allowed for precise control over CNT layer thickness and mass accumulation by adjusting the deposition time. In contrast, DC showed no significant changes in thickness with longer immersion. These structural differences translated into distinct electrical behaviors. Resistance measurements showed that EPD samples exhibited a substantial decrease in resistance with increasing deposition time, from 5.9 ± 2.5 kΩ to 0.2 ± 0.1 kΩ, indicating the formation of well-connected conductive pathways. On the other hand, DC samples maintained relatively constant, higher resistance values across all conditions. Additionally, EPD-coated mats demonstrated enhanced touch sensitivity, generating higher and more stable current responses compared to DC-deposited samples. These results confirm that EPD is a more effective, tunable method for fabricating conductive CNT coatings on electrospun PVDF mats, particularly for applications in flexible electronics and wearable sensors. Full article

An optical fiber force sensor based on the Vernier effect in cascaded Fabry–Perot interferometers (FPIs) formed by a barium tantalate microsphere and a section of polymethyl methacrylate (PMMA) optical fiber is proposed and investigated. Optical fiber sensors offer numerous advantages over their electronic counterparts, including immunity to electromagnetic interference and suitability for harsh environments. Despite these benefits, current optical fiber force sensors often face limitations in sensitivity, reliability, and fabrication costs. The proposed sensor has the potential to address these issues. Simulations and experimental results demonstrate that the sensor achieves a sensitivity of 9279.66 nm/N in a range of up to 3 mN. The sensor also exhibits excellent repeatability, making it a promising candidate for high-performance force monitoring in various challenging environments. Full article

Machine learning (ML)-based techniques have received significant attention in various fields of industry and science. In civil and bridge engineering, they can facilitate the identification of specific patterns through the analysis of data acquired from structural health monitoring (SHM) systems. To evaluate the prediction capabilities of ML, this study examines the performance of several ML algorithms in estimating the total weight and location of vehicles on a bridge using strain sensing. A novel framework based on a combined model and data-driven approach is described, consisting of the establishment of the finite element (FE) model, its updating according to load testing results, and data augmentation to facilitate the training of selected physics-informed regression models. The article discusses the design of the Fiber Bragg Grating (FBG) sensor-based Bridge Weigh-in-Motion (BWIM) system, specifically focusing on several supervised regression models of different architectures. The current work proposes the use of the updated FE model to generate training data and evaluate the accuracy of regression models with the possible exclusion of selected input features enabled by the structural specificity of a bridge. The data were sourced from the SHM system installed on a network arch bridge in Wolin, Poland. It confirmed the possibility of establishing the BWIM system based on strain measurements, characterized by a reduced number of sensors and a satisfactory level of accuracy in the estimation of loads, achieved by exploiting the network arch bridge structural specificity. Full article

Carbon fiber-reinforced polymer (CFRP) laminates have been widely coated on aged and damaged structures for recovering or enhancing their structural performance. The health conditions of the coated composite structures have been given high attention, as they are critically important for assessing operational safety and residual service life. However, the current problem is the lack of an efficient, long-term, and stable monitoring technique to characterize the structural behavior of coated composite structures in the whole life cycle. For this reason, bare and packaged fiber Bragg grating (FBG) sensors have been specially developed and designed in sensing networks to monitor the structural performance of CFRP-coated composite beams under different loads. Some optical fibers have also been inserted in the CFRP laminates to configure the smart CFRP component. Detailed data interpretation has been conducted to declare the strengthening process and effect. Finite element simulation and simplified theoretical analysis have been conducted to validate the experimental testing results and the deformation profiles of steel beams before and after the CFRP coating has been carefully checked. Results indicate that the proposed FBG sensors and sensing layout can accurately reflect the structural performance of the composite beam structure, and the CFRP coating can share partial loads, which finally leads to the downward shift in the centroidal axis, with a value of about 10 mm. The externally bonded sensors generally show good stability and high sensitivity to the applied load and temperature-induced inner stress variation. The study provides a straightforward instruction for the establishment of a structural health monitoring system for CFRP-coated composite structures in the whole life cycle. Full article

Resistive stretching sensors are currently used in healthcare robotics due to their ability to vary electrical resistance when subjected to mechanical strain. However, commercial sensors often lack the softness required for integration into soft structures. This study presents a detailed methodology to characterize fabric-based resistive stretching sensors, focusing on both static and dynamic performance, for application in a smart vascular simulator for surgical training. Five sensors, called #1–#5, were developed using conductive fabrics integrated into soft silicone. Stability and fatigue tests were performed to evaluate their behavior. The surface structure and fiber distribution were analyzed using digital microscopy and scanning electron microscopy, while element analysis was performed via Energy-Dispersive X-ray Spectroscopy. Sensors #1 and #3 are the most stable with a low relative standard deviation and good sensitivity at low strains. Sensor #3 showed the lowest hysteresis, while sensor #1 had the widest operating range (0–30% strain). Although all sensors showed non-monotonic behavior across 0–100% strain, deeper investigation suggested that the sensor response depends on the configuration of conductive paths within and between fabric layers. Soft fabric-based resistive sensors represent a promising technical solution for physical simulators for surgical training. Full article

With the continuous advancement of optical fiber micromachining technology, C-type optical fibers have demonstrated significant potential in the field of optical fiber sensing. By partially or completely removing specific regions of the cladding, a “leakage window” is created, enabling interaction between the optical field and external substances. This structure has facilitated the development of various sensors. This paper reviews recent progress in the research and applications of C-type optical fibers in optical sensing. Based on sensing principles and application scenarios, C-type optical fiber sensors can be categorized into two main types: interferometric and photonic crystal types. This article discusses the fundamental operating principles and structural characteristics of each type, and provides a detailed comparison of their respective advantages and disadvantages. Studies have shown that sensors based on C-type fiber structures offer notable benefits such as simple fabrication, excellent mechanical performance, strong anti-interference capability, and high sensitivity. Therefore, they hold great promise for applications in intelligent monitoring, environmental detection, and healthcare. Finally, this review outlines future research directions for C-type fiber sensors. As technology continues to evolve, future studies are expected to focus on improving sensor stability, expanding application scenarios, and addressing challenges in current fabrication techniques. Full article

Integrated photonics is a transformative technology for enhancing communication and computation in Cloud and Fog computing networks. Photonic integrated circuits (PICs) enable significant improvements in data-processing speed, energy-efficiency, scalability, and latency. In Cloud infrastructures, PICs support high-speed optical interconnects, energy-efficient switching, and compact wavelength division multiplexing (WDM), addressing growing data demands. Fog computing, with its edge-focused processing and analytics, benefits from the compactness and low latency of integrated photonics for real-time signal processing, sensing, and secure data transmission near IoT devices. PICs also facilitate the low-loss, high-speed modulation, transmission, and detection of RF signals in scalable Radio-over-Fiber (RoF) links, enabling seamless IoT integration with Cloud and Fog networks. This results in centralized processing, reduced latency, and efficient bandwidth use across distributed infrastructures. Overall, integrating photonic technologies into RoF, Fog and Cloud computing networks paves the way for ultra-efficient, flexible, and scalable next-generation network architectures capable of supporting diverse real-time and high-bandwidth applications. This paper provides a comprehensive review of the current state and emerging trends in integrated photonics for IoT sensors, RoF, Fog and Cloud computing systems. It also outlines open research opportunities in photonic devices and system-level integration, aimed at advancing performance, energy-efficiency, and scalability in next-generation distributed computing networks. Full article

Light detection and ranging (LiDAR) sensors are essential for applications where high-resolution distance and velocity measurements are required. In particular, frequency-modulated continuous wave (FMCW) LiDAR, compared with other LiDAR implementations, provides superior receiver sensitivity, enhanced range resolution, and the capability to measure velocity. Integrating LiDARs into electronic and photonic semiconductor chips can lower their cost, size, and power consumption, making them affordable for cost-sensitive applications. Additionally, simple designs are required, such as FMCW signal generation by the direct modulation of the current of a semiconductor laser. However, semiconductor lasers are inherently nonlinear, and the driving waveform needs to be optimized to generate linear FMCW signals. In this paper, we employ pre-distortion techniques to compensate for chirp nonlinearity, achieving frequency nonlinearities of 0.0029% for the down-ramp and the up-ramp at 55 kHz. Experimental results demonstrate a highly accurate LiDAR system with a resolution of under 5 cm, operating over a 210-m range through single-mode fiber, which corresponds to approximately 308 m in free space, towards meeting the requirements for long-range autonomous driving. Full article