Fabry-Perot interferometer (FPI) optical fiber temperature sensors were designed based on tapered single-mode fiber (SMF) and miniature glass microspheres. First, the SMF is tapered into microstructures a glass microsphere is bonded or fabricated via fusion discharge on the end face of the tapered SMF. The incident light forms reflected light between the two surfaces of the microsphere that forms an FPI. When the glass microsphere is heated, variations in the refractive index and diameter of the microsphere induce changes in the optical path of the Fabry-Perot cavity. This leads to the changes of the dip wavelength and the dip intensity in the FPI spectrum, thus allowing temperature measurement. Two sensors are fabricated: S1 based on adhesive bonding of a glass microsphere to the tapered SMF end face and S2 based on discharge ablation of a glass microsphere on the tapered SMF end face. For S1, the sensitivity of Dip 1 wavelength and intensity to temperature changes were 36.1 pm/degrees C and -0.145 dB/degrees C, respectively. S1 has good reversibility and stability, making it suitable for measuring low temperatures. For S2, the sensitivity of Dip 2 wavelength and intensity to temperature changes were 13.1 pm/degrees C and -0.0782 dB/degrees C, respectively. S2 has good reversibility and stability, making it suitable for measuring slightly higher temperatures. The proposed sensor structure is small, stable, suitable for temperature measurements in harsh environments, and suitable for confined spaces.
This paper presents an automatic lens decentering detection and adjustment system that integrates machine vision with embedded motion control. A hybrid image-processing algorithm combining morphological enhancement, Hough transform, and least-squares circle fitting is proposed to extract edge features and accurately compute the optical center. A multi-degree-of-freedom alignment platform driven by stepper motors is utilized, and motion control is implemented via an embedded controller, where detected deviations are converted into real-time pulse signals for micron-level pose compensation. The overall architecture enables closed-loop correction between vision feedback and mechanical motion. Experimental validation on a dedicated test platform demonstrates that the proposed method achieves an average centering accuracy of 3.5 mu m and repeatability within +/- 2 mu m, improving processing efficiency by approximately 40% compared to the traditional manual centering process using a standard optical centering microscope (which typically requires 5-8 min per lens). The results confirm that the developed system offers a robust, scalable, and cost-effective solution for high-precision optical component alignment, providing a promising foundation for intelligent manufacturing applications in optical processing industries.
Wheat protein content is an important indicator of wheat quality. Conventional analytical methods, such as the Kjeldahl method, are accurate but labor-intensive and time-consuming. Near-infrared spectroscopy (NIRS) provides a rapid and nondestructive alternative with simple sample preparation. However, calibration models developed on one instrument often perform poorly on another instrument because of systematic spectral differences, which limits the broader application of NIRS in grain quality assessment. In this study, a multi-scale attention fusion network (MSAF-Net) was developed for the quantitative prediction and cross-instrument transfer of wheat protein content. The model integrates channel-wavelength attention, dynamic multi-scale feature fusion, and domain-adversarial transfer to improve feature extraction and reduce the influence of instrument-related spectral variation. Experiments on the IDRC 2016 wheat protein spectral dataset showed that MSAF-Net achieved a coefficient of determination (R2) of 0.9473 and a root mean square error (RMSE) of 0.0340 on the primary instrument, corresponding to a 16.9% reduction in RMSE compared with partial least squares (PLS). In cross-instrument transfer, fine-tuning with 50 target-instrument samples achieved an R2 of 0.9774 and reduced RMSE by 63.2% compared with the traditional PLS transfer strategy. With 0.026 million trainable parameters and 14.258 M MACs for single-sample inference, the model also showed potential for deployment on portable NIR instruments. These results indicate that MSAF-Net can improve the accuracy and transferability of NIRS-based wheat protein prediction.
A high-sensitivity optical fiber temperature sensor based on the Vernier effect is proposed in this paper for seawater temperature measurement. The device is composed of a sensing Fabry-Perot interferometer (SFPI) with a large-offset structure and a reference Fabry-Perot interferometer (RFPI). SFPI was fabricated by offset fusion splicing of single-mode fiber (SMF) and its FP cavity was completely filled with polydimethylsiloxane. RFPI was fabricated by splicing SMF and hollow-core fiber (HCF) to form an SMF-HCF-SMF structure. By precisely controlling the cavity lengths, the Vernier effect was generated through the spectral superposition of two FPIs, leading to a significant amplification of the temperature response. The temperature measurement range of the sensor was 20 degrees C to 32.5 degrees C, and both simulation and experimental results demonstrated a high temperature sensitivity of up to -9.5 nm/degrees C, approximately 20 times higher than that of a single FPI. The experimental results indicate that the sensor exhibits favorable reversibility, repeatability and stability. Overall, the temperature sensor features a simple structure, low cost and facile fabrication, indicating its potential for seawater temperature measurement.
Lateral flow immunoassay (LFIA) is the most widely used platform for point-of-care testing. However, the sensitivity of LFIA remains insufficient for detecting low-abundance biomarkers. In this study, a rod-shaped nanozyme with an osmium-cerium oxide heterojunction (Os-CeO2) was developed to enhance the sensitivity of LFIA. The surfaces of these nanorods were decorated with ultrasmall Os nanoparticles (diameter < 2 nm), which exhibited excellent peroxidase-like activity, enabling the efficient catalytic oxidation of substrates for colorimetric signal generation. The nanozyme was integrated into an LFIA platform (Os-CeO2 LFIA). Compared with conventional gold nanoparticles, the Os-CeO2 LFIA significantly improved the sensitivity. The platform demonstrated high specificity, good stability, and reliable accuracy. These results underscore the strong potential of Os-CeO2 LFIA for clinical analysis, offering a promising strategy to overcome the sensitivity limitations of conventional LFIA for low-abundance biomarker detection.
Recently, a new generation of gas detectors, typically comprising a tunable diode laser absorption spectroscopy (TDLAS) sensor mounted on a pan-tilt unit, has been deployed in gas transmission stations. This pan-tilt configuration significantly expands monitoring coverage compared to conventional stationary sensors. Consequently, the design of the scanning scheme becomes critical for ensuring detection effectiveness. This study introduces a novel quantitative approach to evaluate the efficiency of TDLAS scanning schemes. Computational Fluid Dynamics (CFD) was employed to simulate gas concentration fields across various potential leakage scenarios. Subsequently, a space-time dynamic mapping (STDM) model is established to map the gas dispersion data onto the dynamic scanning process. By simulating the detector's scanning cycles, concentration data were extracted along each scanning path. A novel indicator, cumulative detection time per unit time considering scenario probability (CDTU-SP), is proposed to quantify detection performance. The feasibility and effectiveness of the proposed method were validated through a comparative analysis of three distinct scanning schemes. This approach provides a robust tool for optimizing existing scanning protocols and guiding the development of more effective detection systems during the design phase.
This study addresses the problem of inlet edge rounding of orifice plates during operation which leads to systematic errors in flow rate measurements. A dimensionless model is developed to predict the rounding radius as a function of the material's mechanical properties. The proposed approach com-bines dimensional analysis (p-theorem) with finite element modeling, enabling the incorporation of key parameters, including ultimate tensile strength, yield strength, Young's modulus, shear modulus, and Poisson's ratio. The model establishes a quantitative relationship between edge geometry and material properties, allowing the influence of different materials on the rounding process to be evaluated under identical operating conditions. Numerical simulations were car-ried out for copper, carbon steel, and silicon nitride, represent-ing materials with fundamentally different mechanical behavior. A quantitative comparison with a classical empirical model for carbon steel shows a high level of agreement, with relative errors not exceeding 5%, confirming the validity of the proposed formulation. The results demonstrate that the model can reliably reproduce the evolution of the rounding radius and predict edge degradation over time. The obtained relationships provide a basis for introducing correction factors to account for flow rate measurement errors caused by edge rounding and enable the assessment of material influence on measurement accuracy. The model can also be used for pre-liminary material selection to improve resistance to edge deg-radation. It should be noted that the proposed model primarily considers mechanical deformation effects and does not explicitly account for chemical corrosion or other multi- physical interactions, which may influence edge rounding under certain operating conditions.
Accurate diagnosis of the diesel particulate filter (DPF) soot loading states is critical for optimizing regeneration strategies and sustaining engine performance. However, conventional monitoring techniques based on the differential pressure (DP) sensor are constrained by inherent sensitivity limitations. This study introduces a highly sensitive diagnostic method for the assessment of DPF soot loading states utilizing electrostatic particulate matter (PM) sensors. Initially, a DPF pressure drop model was developed to elucidate the exhaust pressure drop characteristics across varying soot loading states. Subsequently, a heavy-duty diesel engine experimental platform incorporating a dual PM and DP sensor configuration was established to comprehensively evaluate the diagnostic efficacy under diverse engine speeds, loads, and soot loading states. The experimental results indicate that the signal ratio between the DPF upstream and downstream PM sensors rises progressively with increasing soot loading, reaching a dimensionless signal ratio in excess of 1000 under extreme engine load conditions. Notably, the sensitivity of the PM sensor surpasses that of the DP sensor by up to two orders of magnitude under identical operating conditions, demonstrating that this sensing approach can enhance the monitoring resolution and precision of DPF diagnostic systems.
This study reports the development of a novel bulk optical sensor (optode) for the determination of thulium (Tm3 & thorn;) ions. The optode is based on a plasticized poly(vinyl chloride) (PVC) mem-brane incorporating 5-(benzothiazolylazo)-2,5-naphthalenediol (BTAND) as the ionophore and sodium tetraphenylborate (NaTPB) as an anionic additive, while dibutyl phthalate (DBP) serves as the plasticizer. Experimental variables, including the composition of the membrane matrix and solvent mediator, reagent concentration, and solution pH, were systematically optimized. Under the optimized conditions, the proposed method exhibited a linear response in the range 10-260ng/mL, with limits of detection and quantification of 3.0 and 9.8 ng/mL, respectively. An EDTA solution (0.15M) was applied to success-fully regenerate the sensor, and it responded reversibly with a relative standard deviation (RSD) below 2.3% across six repeated measurements of 150 ng/mL of Tm3 & thorn;in variable mem-branes. No significant interference from common anions and cations was observed. The optode displays excellent stability, a response time of approximately 5 min, and no detectable leach-ing of BTAND. The sensor remains stable for at least one month without noticeable change in performance. To evaluate the ana-lytical performance of the proposed Tm3 & thorn;sensor, it was effect-ively employed as an optode for the determination of thulium in wastewater, mixed environmental samples, and certified ICP/ DCP standard solutions.
This study presents the development and comparative analysis of prism-coupled surface plasmon resonance (SPR) biosensors utilizing barium titanate (BaTiO3) and zinc oxide (ZnO) for the highly sensitive detection of human immunoglobulin G (IgG). BaTiO3 provides a strong foundation due to its high dielectric constant and unique crystalline structure, while ZnO contributes excellent optoelectronic properties, chemical stability, and biocompatibility. In IgG detection experiments, both sensors exhibited a red shift in the resonance angle with increasing IgG concentration. The BaTiO3-based sensor demonstrated a linear response within the concentration range of 1-20 mu g/mL, achieving a sensitivity of 0.12 degrees/(mu g/mL) and an ultra-low limit of detection (LOD) of 0.02 mu g/mL. The ZnO-based sensor also showed a strong linear relationship (1-20 mu g/mL) with a sensitivity of 0.08 degrees/(mu g/mL) and an LOD of approximately 0.03 mu g/mL, along with superior response speed and long-term stability. Comparative analysis indicates that BaTiO3-based sensors offer higher sensitivity, whereas ZnO-based sensors provide better cost-effectiveness and stability. This work provides valuable guidance for material selection and performance optimization of SPR sensors in biomedical detection and is expected to facilitate advancements in human IgG detection technologies.
Noninvasive biomedical analysis remains a central objective in modern diagnostics, where early detection significantly improves therapeutic outcomes. Tissue fluorescence has emerged as a powerful optical strategy owing to its molecular specificity, rapid acquisition, and practically no sample preparation. Originating from endogenous fluorophores, such as Nicotinamide Adenine Dinucleotide in its reduced form (NADH), Flavin Adenine Dinucleotide (FAD), collagen, elastin, keratin, and porphyrins, fluorescence encodes information on metabolic activity, extracellular matrix organization, oxidative stress, glycation, and other physio-pathological processes. Advances in excitation sources, fiber-optic probes, MultiSpectral Imaging (MSI), HyperSpectral Imaging (HSI), and Fluorescence Lifetime Imaging Microscopy (FLIM) have expanded clinical applicability, while computational tools including multivariate analysis and machine learning now enable automated interpretation of complex optical signatures. Current evidence demonstrates strong diagnostic potential across cancer screening, diabetes, systemic lupus erythematosus, osteoporosis, dermatological disorders, and other metabolic or degenerative diseases. Despite these advances, challenges persist, including limited penetration depth, inter-individual variability, spectral overlap, and lack of standardized acquisition protocols. Future progress will rely on harmonized clinical validation, multimodal optical platforms, curated spectral databases, and artificial intelligence-assisted decision support. Collectively, tissue fluorescence represents a promising foundation for next-generation, portable, real-time, and patient-centered precision diagnostics.
A high-sensitivity temperature sensor based on a polydimethylsiloxane (PDMS) encapsulated, U-shaped tapered Mach-Zehnder interferometer (MZI) is presented. The sensor utilizes a tapered no-core fiber (NCF) spliced between single-mode fibers (SMFs) and configured into a U-shape. High sensitivity is achieved by leveraging the intense evanescent field of the microfiber alongside the significant thermo-optic and thermal expansion coefficients of the PDMS cladding to amplify temperature-induced phase shifts. Experimental results yield a maximum sensitivity of -2.3086 nm/degrees C within 30-60 degrees C. The investigation establishes that sensitivity is inversely proportional to the taper waist diameter and is significantly enhanced by the U-shaped geometry, whereas the PDMS coating thickness has a negligible effect. Characterized by its compact design and stable performance, this device offers a robust solution for precision thermal monitoring in wearable photonics and micro-environments.
Leuco-malachite green (LMG), a metabolite of malachite green (MG), exhibits stronger bioaccumulation and higher toxicity to humans. Therefore, development of a rapid and sensitive detection method for LMG is of great significance. In this study, LMG-specific split aptamers (SPALMG) with a dissociation constant (Kd) of 1.538 +/- 0.1261 mu M and enhanced affinity compared to the parent aptamer are generated using A5b-12C, a high-affinity variant, as the parent aptamer for LMG detection. Subsequently, a colorimetric aptasensor is developed, employing thiol-modified SPAlmg as probes and gold nanoparticles (AuNPs) as signal indicators. In the presence of LMG, the SPAlmg preferentially bind to LMG to form a ternary complex, causing the AuNPs to aggregate and induce a color change from red to blue. The sensor exhibits high sensitivity and specificity, with a limit of detection (LOD) of 46 nM, a linear range of 1-7 mu M, and good recoveries of 96-113%. Compared with other studies, a more excellent LOD is achieved. This work provides a novel affinity probe that holds great promise for the development of sandwich-type sensors for LMG detection.
Integrating spheres have wide application in radiometry and photometry. However, they are costly due to advanced internal coating techniques and lack versatility for applications requiring different port configurations and sizes. This makes customization challenging for specific needs, such as light extinction measurements, where a diffuse beam must match the dimensions of test media. To address this limitation, we propose a low-cost alternative integrating sphere design fabricated through 3D printing and coated with a commercially available white paint. Reflectance measurements of various coating materials were performed on sample coupons, and the commercially available white paint was selected for the internal coating due to its low cost, ease of application, and high durability despite its moderate reflectance of 87%. Radiometric measurements showed that the fabricated sphere exhibited a throughput of around 11%, wall reflectance of about 90%, and an output beam uniformity with a maximum radiance deviation of 4% in the visible spectrum. The sphere was then employed as a background illumination source for light extinction measurement of an ethylene-air diffusion flame. The measured soot concentration showed agreement with literature data, highlighting the viability of a low-cost solution, despite its lower performance compared to commercial alternatives. This confirms that a low-cost integrating sphere with custom port sizes can be 3D-printed and coated in-house, offering a practical solution for light extinction measurements and other specialized optical applications.
This article proposes a high-sensitivity nanoscale fiber-optic displacement sensor based on two cascaded Fabry-Perot interferometers (FPI1 and FPI2) and the mechanism of the 2nd-order harmonic Vernier effect (HVE). The HVE-based sensor consists two cascaded or paralleled interferometers, reference and sensing interferometers, and the free spectral ranges (FSRs) of the two interferometers satisfy the relationship FSRref approximate to (i + 1) x FSRsen (where i is an integer and denotes the harmonic order, i >= 1), thus forming a periodic inner envelope curve. In this work, FPI1 is made up of a single-mode fiber (SMF) and a capillary with a large inner diameter, while FPI2 is made up of a SMF, a ceramic sleeve, and ultraviolet (UV) glue. Since the cavity length of FPI1 changes with displacement, while FPI2 is insensitive to displacement variations, cascading FPI1 and FPI2 - whose FSR have an approximate 3:1 ratio - can generate the 2nd-order HVE. Compared with most HVE-based sensors, this sensor is less difficult in terms of FSR matching. Experimental results show that the displacement sensitivity of the sensor is 0.152 nm/nm, which amounts to roughly 11 times the displacement sensitivity of the single FPI1. Additionally, the sensor possesses the advantages of low cost, easy fabrication, and good repeatability, providing an innovative design approach and scheme for the manufacturing of high-sensitivity nanoscale displacement sensors.
Reconstruction accuracy is a crucial indicator for assessing the combustion conditions in combustion diagnostics. Existing flame-temperature measurement methods require comparison and verification using simulations, infrared thermal imaging cameras, and thermocouples, which increase measurement complexity and reduce efficiency. This study introduces a method based on tunable diode laser absorption spectroscopy (TDLAS) that utilizes an array sensor to capture specific absorption and radiation spectral signals to measure the combustion field temperature. Active and passive spectral information at different temperatures was obtained through calibration experiments. The temperature field of the K+-doped premixed stable flame was imaged. The difference between the active and passive spectral temperature field images did not exceed 8%, and the maximum relative error compared to the thermocouple measurements did not exceed 6%, demonstrating good reconstruction accuracy. Simultaneous active and passive spectral temperature measurements provide a novel approach for developing high-precision flame measurements and reducing system complexity.
This study presents the development and field application of a multi-range analyzer for continuous detection of ammonia emissions from sludge incineration flue gas, addressing the challenges posed by increasing sludge production and rising incineration rates. Based on Tunable Diode Laser Absorption Spectroscopy (TDLAS), the developed analyzer achieves multi-range detection through optimized hardware and software design, overcoming limitations such as narrow measurement ranges and susceptibility to interferences in conventional methods. Field validation at a sludge incineration plant demonstrated high accuracy and real-time performance compared to the continuous emission monitoring system (CEMS), with a response time of approximately 46 s enabling effective tracking of dynamic ammonia emission variations. This study calculated the sludge incineration ammonia emission factor at 188.7 g/t-Ds, filling a critical gap in domestic emission inventories. Statistical analysis identified a strong negative correlation between ammonia emission factor and dry sludge feed rate, providing valuable insights for denitrification process optimization.
