Search Results (130)

Optical fiber multi-analyte biosensors have become an important cutting-edge technology for the simultaneous detection of multiple biochemical substances in complex samples due to their unique advantages such as small size, anti-interference capability, and remote and label-free detection. This paper systematically reviews the recent research progress of optical fiber multi-analyte biosensors in the field of simultaneous detection of various types of targets. The review is organized by detection target type and elaborates on the simultaneous detection of biomarkers and proteins, viruses and bacteria, biological metabolites and nutrients, heavy metal ions, gases, organic pollutants, cells, and mixed detection of different types of biochemical substances. The advantages and disadvantages of existing optical fiber multi-analyte biosensors are summarized. Key technical challenges are also discussed, including issues of selectivity, long-term stability, real-sample validation, and system integration that currently hinder practical deployment. Finally, the future challenges and development directions of optical fiber multi-analyte biosensors are briefly discussed, providing references for relevant research teams. Full article

Intelligent completion technologies, including Inflow Control Valves (ICVs), have become increasingly important for remotely managing zonal production in complex well architectures. However, quantifying flow rates and phase fractions in such systems remains challenging due to space constraints and the harsh downhole environment, which limit the deployment of conventional sensors. Distributed Acoustic Sensing (DAS) provides a promising solution by converting standard fiber-optic cables into dense arrays of acoustic sensors. While DAS has been successfully applied in applications such as integrity monitoring and leak detection, its use for direct two-phase flow characterization within intelligent completions remains largely unexplored. In this study, we present a DAS-based methodology to monitor and analyze oil–water two-phase flow in horizontal experiments that mimic field conditions. Acoustic data collected from DAS are transformed into time–frequency spectrograms using Short-Time Fourier Transform (STFT) to extract dynamic spectral features. These features are then correlated with pressure drop across the ICV and flow rate, revealing distinct frequency band behaviors associated with fluid changes. To quantify flow characteristics, a power-law model is trained using spectral features to predict flow rate and phase fractions. The results demonstrate strong predictive capability for pressure drop and flow rate under controlled laboratory conditions, highlighting the potential of DAS for multiphase flow diagnostics in field applications with intelligent completions, while water cut prediction remains challenging due to the complex and non-unique relationship between flow conditions and DAS response and is left for future work. This research not only provides new insights into the acoustic response of oil–water flows but also introduces a data-driven framework for leveraging DAS in real-time flow monitoring and control within ICV-equipped completions. Full article

To address the limited spatial localization accuracy of partial discharge (PD) in high-voltage cable terminations and the difficulty in accurately determining the trigger time in traditional ultrasonic detection, this paper proposes an electro-acoustic synergistic localization technology based on a high-frequency current transformer (HFCT) and a Sagnac optical fiber interferometer. A high-sensitivity Sagnac acoustic sensor based on a 3D-printed photosensitive resin mandrel was developed. Through structural design and 0–50 kHz amplitude–frequency testing, the sensor exhibits a dominant resonant response at 33.2 kHz. This narrow-band, high-sensitivity characteristic effectively enhances the perception capability for weak PD ultrasonic signals. An electro-acoustic synergistic detection system was constructed, in which the high-frequency PD current signal captured by the HFCT was used as the electrical time reference, and a dual-channel Sagnac sensor array was used to extract the arrival times of ultrasonic waves. In a 12 kV laboratory cable-termination PD experiment, the proposed system identified the representative built-in air-gap PD source with an absolute localization error of 5 mm under the tested laboratory configuration. This value should be interpreted as the localization result for the tested representative defect, rather than as a generally validated accuracy specification of the system. This study provides a proof-of-concept laboratory demonstration of an electro-acoustic localization strategy that combines the fast electrical response of HFCT detection with the electromagnetic-interference immunity and acoustic sensitivity of Sagnac fiber-optic sensing. Full article

To address the demand for underwater acoustic detection with hydrostatic pressure resistance, this paper proposes a fiber-optic Fabry–Perot (F-P) underwater acoustic sensor based on micro-electromechanical system (MEMS) technology. According to the F-P interference principle, the diaphragm deforms under acoustic pressure, inducing variations in the F-P cavity length which modulate the interference spectrum and enable the measurement of underwater acoustic signals. A sensing diaphragm with a composite structure consisting of a central boss and a micro-hole array is designed, which improves the optical signal quality while reducing the influence of the pressure difference between the inner and outer surfaces of the diaphragm on sensor operation. MEMS fabrication, computer numerical control (CNC) machining, and laser fusion splicing technologies are employed to achieve batch fabrication of the sensing units and adhesive-free integration of the sensor. Experimental results show that the proposed sensor exhibits a flat frequency response within ±1.5 dB over the range of 1 kHz to 10 kHz, with an average signal-to-noise ratio (SNR) of 86.35 dB. The sensitivity reaches −181.79 dB re 1 rad/μPa at 10 kHz, with a maximum nonlinearity of 0.48% F.S., a repeatability error of 0.15% F.S. and a dynamic range of 100.83 dB. The proposed sensor features miniaturization, high consistency, hydrostatic pressure self-balancing capability, and immunity to electromagnetic interference, providing a solid foundation for hydrostatic-pressure-resistant underwater acoustic measurements in deep-sea environments. Full article

The growing demand for high-fidelity, multi-parameter, distributed sensing in critical domains such as structural health monitoring, oil and gas exploration, and secure perimeter surveillance is pushing traditional optical fiber sensors (OFS) to their performance limits. Although conventional multiplexing techniques such as time-division and wavelength-division multiplexing (TDM, WDM) have been commercially successful, they are rapidly approaching fundamental bottlenecks in sensor density, spatial resolution, and data capacity. This review argues that the synergistic convergence of space-division multiplexing (SDM) and artificial intelligence (AI) represents a paradigm shift, enabling a new generation of intelligent, high-dimensional sensing networks. We comprehensively survey the state of the art in SDM-based OFS, detailing the operating principles and applications of multi-core fibers (MCFs) for ultra-dense sensor arrays and 3D shape sensing, as well as few-mode fibers (FMFs) for mode-division multiplexing and enhanced multi-parameter discrimination. However, the unprecedented spatial parallelism provided by SDM introduces significant challenges, including inter-channel crosstalk, complex signal demultiplexing, and massive data volumes. This paper systematically explores how AI, particularly machine learning (ML) and deep learning (DL), is being leveraged not merely as a tool but as an indispensable core technology to mitigate these impairments. We critically analyze AI’s role in digital crosstalk suppression, intelligent mode demultiplexing, signal denoising, and solving complex inverse problems for parameter estimation. Furthermore, we highlight how this AI–SDM synergy enables capabilities beyond the reach of either technology alone, such as super-resolution sensing and predictive analytics. The discussion is extended to include the critical supporting pillars of this ecosystem, such as advanced interrogation techniques and the associated data management challenges. Finally, we provide a forward-looking perspective on the trajectory of the field, outlining a path toward cognitive sensing networks that are self-calibrating, adaptive, and capable of autonomous decision-making. This review is intended to serve as a foundational reference for researchers and engineers at the intersection of photonics and intelligent systems, illuminating the pathway toward tomorrow’s intelligent sensing infrastructure. Full article

The combination of gold nanoparticles (AuNPs) with hydrogels has drawn significant interest in the design of smart materials as advanced platforms for biomedical applications. These systems endow light-responsiveness enabled by the AuNPs localized surface plasmon resonance (LSPR) phenomenon. In this study, we propose a nanocomposite hydrogel in which gold nanorods (AuNRs) are included in an agarose–carbomer–hyaluronic acid (AC-HA)-based hydrogel matrix to study the correlation between light irradiation, local temperature increase, and drug release for potential light-assisted drug delivery applications. The gel is obtained through a facile microwave-assisted polycondensation reaction, and its properties are investigated as a function of both the hyaluronic acid molecular weight and ratio. Afterwards, AuNRs are incorporated in the AC-HA formulation, before the sol–gel transition, to impart light-responsiveness and optical properties to the otherwise inert polymeric matrix. Particular attention is given to the evaluation of AuNRs/AC-HA light-induced heat generation and drug delivery performances under near-infrared (NIR) laser irradiation in vitro. Spatiotemporal thermal profiles and high-resolution thermal maps are registered using fiber Bragg grating (FBG) sensor arrays, enabling accurate probing of maximum internal temperature variations within the composite matrix. Lastly, using a high-steric-hindrance protein (BSA) as a drug mimetic, we demonstrate that moderate localized heating under short-time repeated NIR exposure enhances the release from the nanocomposite hydrogel. Full article

In this study, a two-dimensional gamma-ray source localization system using a mesh array of plastic scintillating fibers and an artificial neural network is presented. The system covers a 200 cm by 100 cm area using SCSF-78 multi-cladded fibers. A novel U-shaped fiber topology connects both fiber ends to one side, requiring only two data-acquisition systems. Silicon photomultiplier arrays measure fast time-of-flight under optimized operating conditions to maximize signal yield. An independent artificial neural network model map measured time-of-flight values to spatial coordinates, compensating for systematic non idealities. Performance was validated using a Cesium-137 source at 20 random test positions. The artificial neural network method achieved a mean full-scale error of 4.6%. This demonstrated a 79.34% accuracy improvement over direct theoretical calculation, which had a mean full-scale error of 22.5%. The system showed consistent performance, achieving a two-dimensional standard deviation of 0.492 cm during repeatability assessment. This methodology provides a practical, efficient approach to two-dimensional radiation source localization suitable for real time monitoring and contamination mapping. Full article

Distributed acoustic sensing (DAS) technology is gaining popularity for real-time monitoring during the hydraulic fracturing of unconventional reservoirs. By transforming a standard optical fiber into a dense array of acoustic sensors, DAS provides continuous spatiotemporal measurements along the entire wellbore. Although accurate DAS-based real-time diagnosis of multistage hydraulic fracturing is critical for optimizing the efficiency of stimulation operations and mitigating operational risks in horizontal tight oil wells, existing methods often fail to provide integrated qualitative and quantitative insights. To address this gap, we present an original diagnostic workflow that synergistically combines frequency band energy (FBE), low-frequency DAS (LF-DAS), and surface injection data for simultaneous fluid/proppant allocation and key downhole anomaly identification. Field application of the proposed framework in a 47-stage well demonstrates that FBE (50–200 Hz) enables robust cluster-level volume estimation, while LF-DAS (<0.5 Hz) reveals fiber strain signatures indicative of mechanical integrity threats. The workflow can successfully diagnose sand screenout, diversion, out-of-zone flow, and early fiber failure—events often missed by conventional monitoring. By linking distinct acoustic fingerprints to specific physical processes, our approach transforms raw DAS data into actionable operational intelligence. This study provides a reproducible, field-validated framework that enhances understanding in the context of fracture treatment, supports real-time decision making, and paves the way for automated DAS interpretation in complex completions. Full article

The primary objective of layered settlement monitoring of deep soil is to obtain settlement data for both the soil and superstructure, enabling appropriate measures to be taken to ensure the structure’s safety and stability. Traditional deep soil monitoring technologies are either limited in the number of measurement points (e.g., fiber Bragg grating sensing technology) or exhibit low measurement accuracy (e.g., distributed fiber optic sensing technology). This study proposes a layered settlement monitoring technique for deep soil based on the ultra-weak fiber Bragg grating sensors. First, ultra-weak fiber Bragg grating strain sensors packaged by fiber-reinforced polymer (FRP) were developed, and experimental research on the sensors’ sensing and directional recognition characteristics was conducted. Subsequently, the sensors were deployed for ground subsidence monitoring in Gaoyang County, China, with investigations conducted on sensor installation techniques and long-term measurement data. Experimental and engineering test results demonstrate that the strain and temperature sensing coefficients of the sensors are 1.22 pm/με and 17.06 pm/°C, respectively. Sensors incorporating dual ultra-weak fiber Bragg grating arrays can simultaneously detect both vertical and lateral soil displacement. Long-term monitoring data effectively reflects subsidence changes in the Gaoyang region. Full article

For large-scale measurement of microbubble parameters on the ocean surface beneath breaking waves, a buoy-type bubble sensor (BBS) is proposed. This sensor integrates a panoramic bubble imaging sub-sensor with a circular array fiber-optic sub-sensor. The sensitive unit of the latter sub-sensor is designed via theoretical modeling and experimental validation. Theoretical calculations indicate that the optimal cone angle for a quartz fiber-optic-based sensitive unit ranges from 45.2° to 92°. A prototype array element with a cone angle of 90° was fabricated and used as the core component for feasibility experiments in static and dynamic two-phase (gas and liquid) identification. During static identification, the reflected optical power differs by an order of magnitude between the two phases. For dynamic sensing of multiple microbubble positions, the reflected optical power varies from 13.4 nW to 29.3 nW, which is within the operating range of the array element’s photodetector. In theory, assembling conical quartz fiber-based sensitive units into fiber-optic probes and configuring them as arrays could overcome the resolution limitations of the panoramic bubble imaging sub-sensor. Further discussion of this approach will be presented in a subsequent paper. Full article

This study reports the development of a fiber-optic localized surface plasmon resonance (FO-LSPR) sensor incorporating a three-dimensional micropillar array functionalized with gold nanoparticles. The micropillar structures were fabricated on the fiber facet using a single-mask imprint lithography process, followed by nanoparticle immobilization to create a composite plasmonic surface. Compared with flat polymer-coated fibers, the micropillar array markedly increased the effective sensing surface and enhanced light trapping by providing anti-reflective conditions at the interface. Consequently, the sensor demonstrated superior performance in refractive index sensing, yielding a sensitivity of 4.54 with an R² of 0.984, in contrast to 3.13 and 0.979 obtained for the flat counterpart. To validate its biosensing applicability, Interleukin-8 (IL-8), a cancer-associated cytokine, was selected as a model analyte. Direct immunoassays revealed quantitative detection across a broad dynamic range (0.1–1000 pg/mL) with a limit of detection of 0.013 pg/mL, while specificity was confirmed against non-target proteins. The proposed FO-LSPR platform thus offers a cost-effective and reproducible route to overcome the surface-area limitations of conventional designs, providing enhanced sensitivity and stability. These results highlight the potential of the micropillar-based FO-LSPR sensor for practical deployment in point-of-care diagnostics and real-time biomolecular monitoring. Full article

This paper presents a high-sensitivity multi-point seawater temperature detection system based on the virtual Vernier effect, achieved through multiplexed Fabry–Perot (FP) cavities combined with optical frequency-modulated continuous wave (FMCW) interferometry. To address the nonlinear frequency scanning issue inherent in FMCW systems, this paper implemented a software compensation method. This approach enables accurate positioning of multiple FP sub-sensors and effective demodulation of the sensing interference spectrum (SIS) for each FP interferometer (FPI). Through digital signal processing (DSP) algorithms and spectral demodulation, each sub-FP sensor generates an artificial reference spectrum (ARS). The virtual Vernier effect is then achieved by means of a computational process that combines the SIS intensity with the corresponding ARS intensity. This eliminates the need for physical reference arrays with carefully detuned spatial frequencies, as is required in traditional Vernier effect implementations. The sensitivity amplification can be dynamically adjusted with the modulation function parameters. Experimental results demonstrate that an optical fiber link of 82.3 m was achieved with a high spatial resolution of 23.9 $\mathsf{\mu }\mathrm{m}$. Within the temperature range of ${30 }^{\circ }\mathrm{C}$ to ${70 }^{\circ }\mathrm{C}$, the temperature sensitivities of the three enhanced EIS reached −275.56 $\mathrm{pm}{/}^{\circ }\mathrm{C}$, −269.78 $\mathrm{pm}{/}^{\circ }\mathrm{C}$, and −280.67 $\mathrm{pm}{/}^{\circ }\mathrm{C}$, respectively, representing amplification factors of 3.32, 4.93, and 6.13 compared to a single SIS. The presented approach not only enables effective multiplexing and spatial localization of multiple fiber sensors but also successfully amplifies weak signal detection. This breakthrough provides crucial technical support for implementing quasi-distributed optical sensitization sensing in marine environments, opening new possibilities for high-precision oceanographic monitoring. Full article

The growing need for efficient and safe high-energy lithium-ion batteries (LIBs) in electric vehicles and grid storage necessitates advanced internal monitoring solutions. This work presents a comprehensive simulation model of a novel integrated optical sensor based on ethylene carbonate-filled photonic crystal fiber (EC-PCF). The proposed design synergistically combines a chirped fiber Bragg grating (FBG) and an extrinsic Fabry–Pérot interferometer (EFPI) on a multiplexed platform for the multifunctional sensing of refractive index (RI), temperature, strain, and pressure (via strain coupling) within LIBs. By matching the RI of the PCF cladding to the battery electrolyte using ethylene carbonate, the design maximizes light–matter interaction for exceptional RI sensitivity, while the cascaded EFPI enhances mechanical deformation detection beyond conventional FBG arrays. The simulation framework employs the Transfer Matrix Method with Gaussian apodization to model FBG reflectivity and the Airy formula for high-fidelity EFPI spectra, incorporating critical effects like stress-induced birefringence, Transverse Electric (TE)/Transverse Magnetic (TM) polarization modes, and wavelength dispersion across the 1540–1560 nm range. Robustness against fabrication variations and environmental noise is rigorously quantified through Monte Carlo simulations with Sobol sequences, predicting temperature sensitivities of ∼12 pm/°C, strain sensitivities of ∼1.10 pm/$\mathsf{\mu }$$\mathsf{\epsilon }$, and a remarkable RI sensitivity of ∼1200 nm/RIU. Validated against independent experimental data from instrumented battery cells, this model establishes a robust computational foundation for real-time battery monitoring and provides a critical design blueprint for future experimental realization and integration into advanced battery management systems. Full article

Conducting structural monitoring of highway subgrades is crucial for investigating damage evolution mechanisms under dynamic load-temperature coupling effects. However, existing sensing technologies struggle to achieve distributed, long-term, and high-precision measurements of subgrade structures. Therefore, this study employs next-generation fiber-optic array sensing technology to construct a distributed monitoring system based on weak reflection grating arrays. A dual-parameter sensing network for strain and temperature was designed and installed during the expansion and renovation of a highway in Fujian Province, enabling high-precision monitoring of the entire continuous strain field and temperature field of the subgrade structure. Through a comprehensive analysis of dynamic loading test data and long-term monitoring records, the system revealed the dynamic response patterns of subgrade structures under the interaction of modulus differences, burial depth effects, temperature gradients, and load parameters. It elucidated the mechanical sensitivity of flexible base layers and the interlayer stress redistribution mechanism. The study validated that grating array sensors not only offer advantages such as easy installation, a high survival rate, and excellent durability but also enable high-capacity, long-distance, and high-precision measurements of subgrade structures. This provides a new technical approach for full lifecycle monitoring of expressways. Full article

Sensor arrays are arrangements of sensors that follow a certain pattern, usually in a row–column distribution. This study presents a systematic review on sensor arrays. For this purpose, several systematic searches of recent studies covering a period of 10 years were performed. As a result of these searches, 361 papers have been analyzed in detail. The most relevant aspects for sensor array design have been studied. In relation to sensing technologies, different categories were identified: resistive/piezoresistive, capacitive, inductive, diode-based, transistor-based, triboelectric, fiber optic, Hall effect-based, piezoelectric, and bioimpedance-based. Other aspects of sensor array design have also been analyzed: applications, validation experiments, software used for sensor array data analysis, sensor array characteristics, and performance metrics. For each aspect, the studies were classified into different subcategories. As a result of this analysis, different emerging technologies and future research challenges in sensor arrays were identified. Full article