Search Results (1,063)

2,4-Dichlorophenoxyacetic acid (2,4-D) is an extensively applied organic compound, primarily for agricultural weed control and plant growth agents. Although 2,4-D usually exists in the environment in low volumes, the detection of 2,4-D is critical for human health and environmental safety. In this work, a biophotonic biosensor was fabricated by coating the surface of a tapered optical fiber with gold nanoparticles (AuNPs) to excite the localized surface plasmon resonance (LSPR) and functionalizing the fiber with molybdenum disulfide nanosheets (MoS₂-NSs)/zinc oxide nanowires (ZnO-NWs) to extend the effective sensing area. Due to the inhibitory effect of 2,4-D on the hydrolytic activity of ALP, the refractive index (RI) around the sensor surface changes, leading to a shift in the LSPR peak wavelength. According to this sensing technique, the sensor can detect concentrations in the range of 1–10 mg/L, with a limit of detection (LOD) of 0.29 mg/L. The stability, repeatability and selectivity tests show that the sensor has good stability and selectivity. In the actual sample detection experiment, the recovery rates of apples and Chinese cabbage were 96.2–100.4% and 83.8–108.8%, respectively, which indicated that the detection method had good accuracy for the detection of target substances in actual samples. Thus, the proposed sensor has an important application in the detection of 2,4-D herbicides. Full article

Underwater laser range-gated imaging (ULRGI) effectively suppresses water backscattering via time-slicing mechanisms, making it a primary modality for underwater vision. However, factors such as the inherent optical properties of water, intra-slice residual scattering, gating timing errors, and sensor noise make it difficult to separate target signals from the background. Consequently, the resulting images are generally affected by texture degradation and low contrast, severely limiting the accuracy of downstream tasks like object detection and environmental perception. To this end, we propose the use of a Wavelet-guided Multi-stage Gated Enhancement Network (WMGNet). Operating progressively across three stages, WMGNet’s first two stages employ an encoder–decoder architecture that leverages multi-scale frequency decomposition in the wavelet domain to pinpoint intra-slice scattering and decouple target signals from noise. To precisely extract fine details, we design a TextureBlock integrating feature gating (ConvGLU) and high-frequency attention (HFAttention). Additionally, a pixel-wise ground-truth guided attention module (GGAM) is introduced to optimize the precision and target-specificity of multi-stage feature fusion. Extensive comparative and ablation experiments demonstrate that the proposed WMGNet effectively eliminates scattering interference and restores texture details in underwater imaging. On our custom ULRGI dataset, it achieves state-of-the-art performance with a PSNR of 36.31 dB, an SSIM of 0.921, an MAE of 2.672, and an LPIPS of 0.060. Notably, it outperforms the second-best method by a margin of 3.06 dB in PSNR and reduces the MAE by 50.69%. Furthermore, evaluations on three public datasets confirm its robust cross-scenario generalization, yielding competitive PSNR values of 33.22 dB, 31.59 dB, and 32.06 dB, respectively. Overall, WMGNet provides a highly effective and robust solution for high-resolution underwater imaging. Full article

Optical wireless communication (OWC) has emerged as a complementary technology to conventional radio frequency (RF)-based communication systems, particularly in scenarios requiring low electromagnetic interference, enhanced security, and efficient spectrum utilization. Within various OWC approaches, optical camera communication (OCC) has attracted increasing attention due to its ability to utilize commercially available image sensors as receivers. This paper presents a 2D multiple-input–multiple-output (MIMO) OCC system based on light-emitting diode (LED) arrays for reliable communication in mobile environments. The proposed system employs on–off keying (OOK) modulation, which supports both rolling shutter and global shutter cameras. To improve decoding reliability under mobility conditions, a deep learning-based decoding model is introduced to enhance LED state detection compared with conventional zero-crossing approaches. In addition, a sequence number-based synchronization is implemented to compensate for frame rate variation and packet missing in a real-time environment. Besides that, by applying YOLOv13 for light source detection and tracking, we can achieve 98% accuracy at 3 m/s velocity. Experimental results show reliable communication performance at transmission distances of up to 22 m under various mobility conditions. Furthermore, the proposed system is validated through real-time environmental data transmission using temperature and humidity sensors with 20 links. The results indicate that the proposed scheme provides stable and reliable OCC performance for mobility Internet of Things (IoT) applications. Full article

Introduction: Heavy metal contamination in water represents a major environmental and public health challenge because toxic ions frequently occur as complex multi-species mixtures rather than isolated pollutants. This study presents a numerical design and optimization of a surface plasmon resonance (SPR) sensor based on a LaF/Au/BaTiO₃/WS₂ heterostructure for monitoring refractive-index changes associated with mixed heavy metal ions in aqueous media. Methodology: The optical response of the multilayer sensor was evaluated using the transfer matrix method under TM-polarized illumination at 633 nm. Systematic optimization was performed for the prism substrate, Au thickness, dielectric oxide layer, and 2D nanomaterial interface. The final configuration consisted of a LaF prism, 50 nm Au film, 2.0 nm BaTiO₃ spacer, and 0.80 nm WS₂ monolayer. Sensor performance was assessed using resonance-angle shift, sensitivity, detection accuracy, quality factor, figure of merit, FWHM, attenuation, and estimated limit of detection. Results and Discussion: The optimized LaF/Au/BaTiO₃/WS₂ configuration produced stable simulated SPR responses across single, binary, quaternary, and five-ion heavy metal matrices. The WS₂ monolayer provided the highest angular displacement among the evaluated 2D materials, while BaTiO₃ improved field confinement and limited optical damping in the numerical model. The configuration maintained attenuation near 1.6%, FWHM values around 7.9°, detection accuracy between 0.030 and 0.032 deg⁻¹, and model-based refractometric LoD values down to 3.49 × 10⁻⁵ RIU under the assumed angular-resolution criterion. Conclusions: The proposed LaF/Au/BaTiO₃/WS₂ SPR configuration provides a numerical framework for label-free monitoring of refractive-index changes associated with complex heavy-metal-ion mixtures in contaminated water. Experimental fabrication and testing are required to validate the simulated performance. Full article

We developed a smartphone-assisted digital image-based optical biosensor array using a planar glass slide with sensor spots in a 2 × 5 array format for point-of-care multiplex detection of biomarkers. The detection is based on the integration of the capture antibody (Ab^C)-functionalized sensor array with a detection antibody-conjugated gold nanoparticle bioconjugate (AuNP@Ab^D) in the presence of interleukin-8 (IL8) to form a sandwich-type AuNP@Ab^D–IL8–Ab^C nanocomplex on the sensing spot surface. Thus, the colorimetric detection method can be applied to the quantitative analysis of IL8, a clinically relevant pro-inflammatory and pro-angiogenic biomarker. The sensing strategy utilizes digital image-based analysis via ImageJ software (V 1.54 g; Java 1.8.0_345 [64 − bit], Windows 8) to quantify the colorimetric signals generated by the light absorbance of surface-bound gold nanoparticles in response to an IL8 droplet sample of merely 8 μL on the planar glass surface, achieving a low detection limit of 0.23 pg/mL (27 fM) and good reproducibility with a coefficient of variation of 0.95%. Validation using IL8-spiked serum at concentrations of 1 × 10⁻⁹ M and 1 × 10⁻¹⁰ M showed minimal matrix effects with a detection accuracy of 99.5% and 106.1%, respectively. Hence, this low-cost portable digital image-based plasmonic nanoparticle-linked immunosorbent assay serves as an alternative to traditional enzyme-linked immunosorbent assays. Full article

This study explored a spatio-temporal deep learning approach for optical soft sensing of combustion emissions in a coal–biomass co-firing scenario. High-speed RGB flame sequences from a 0.5 MW test rig co-firing hard coal with 10% straw were synchronized with extractive measurements of O₂, CO₂, and NO. These sequences were used to train three shallow 3D CNNs and three 3D ResNet-50 architectures with squeeze-and-excitation attention. The proposed 3D CNN/ResNet models performed simultaneous regression of all three gas species from flame image volumes. The best configuration achieves R² values of 0.975, 0.987, and 0.980, accompanied by mean absolute errors of 0.23% by volume, 13.15 mg/m³, and 0.19% by volume for O₂, NO, and CO₂, respectively, at a resolution of 128 × 96 × 96 pixels. Within the scope of the available dataset, comprising a single measurement run and a single fuel mixture, the results indicate that a comprehensive spatio-temporal analysis of flame images can yield accurate estimates of multiple gas concentrations, thereby providing a promising foundation for the future development of soft optical sensors. At the same time, the study is limited to a single combustion experiment, a single biomass fraction, and a single borescope orientation, and the inference delay and hardware requirements were not quantified; therefore, issues regarding the generalizability of the proposed approach to different conditions and its implementation remain open for further work. Full article

Human kinematic analysis is an increasingly important tool in biomechanics, human performance, and wearable sensing research. Many emerging sensing modalities utilize custom sensors requiring accurate temporal alignment with ground-truth biomechanical movement data. Optical motion capture systems provide high-fidelity kinematic measurements but operate as closed, self-contained systems, making time synchronization with external sensor data non-trivial, particularly in wireless and mobile contexts. This work presents a wireless analog interface system built using commercially available components that enables alignment between analog sensor data (e.g., from custom wearables and Internet-of-Things devices) and a commercial motion capture system. The proposed architecture consists of a wearable data acquisition node and a receiver node interfaced directly with an optical motion capture system, allowing synchronized recording of analog sensor signals alongside kinematic data. Notably, the system reconstructs signals into the commercial hardware interface rather than relying on triggers or sync outputs, resulting in a single data file containing kinematics and sensor readings. Benchtop testing demonstrated a mean end-to-end frame delay of ~6 ms, with 95% of the sample exhibiting delay within 15 ms. Accounting for the typical offset, this leaves a standard deviation of 4 ms, within one motion capture frame of the true timestamp (at 100 Hz). Voltage reconstruction accuracy was within 30 mV across the tested conditions, with gain compression below 2.7%. Adjacent channel crosstalk remained below −83 dB across all test conditions. The use of commercial off-the-shelf components supports replication and adaptation by other research groups and integration with different optical motion capture systems. Full article

This study demonstrates single-channel fiber Bragg grating (FBG) sensing for relative vibration-state monitoring of a motor–support system under angle-dependent boundary conditions. A packaged FBG accelerometer-type sensing unit was mounted on the motor–support structure, and the reflected Bragg wavelength was recorded as a one-dimensional optical vibration response. Because the sensor was installed away from the rotating shaft, the measured wavelength fluctuation was interpreted as a coupled vibration-sensitive response of the motor, fixture, sensor package, bonding condition, and changing boundary state, rather than as a calibrated shaft speed or absolute acceleration signal. Adaptive variational mode decomposition (AVMD) was applied to track the time-varying narrowband spectral-response trajectory of the Bragg-wavelength signal. In parallel, raw wavelength windows were supplied to LSTM, 1D-CNN, and CNN–LSTM autoencoders to evaluate waveform departures from learned nominal fixed-angle behavior. The fixed-angle results showed stable but distinguishable optical vibration responses under different boundary states, whereas the dynamic angle-transition records produced local trajectory changes and alarm-candidate intervals. Baseline and autoencoder comparisons further clarified the trade-off between transition coverage and false-alarm tendency. The RMS threshold baseline was more sensitive to transition-related amplitude changes but produced more false alarms, whereas the CNN–LSTM autoencoder provided the most selective response among the tested autoencoder branches. The results are interpreted as task-specific evidence for relative vibration-state transition monitoring rather than as general motor fault diagnosis. Overall, the framework demonstrates a compact FBG-based route for relative vibration-state transition monitoring when speed references, dense sensor layouts, and labeled fault data are unavailable. Full article

Two-dimensional (2D) materials have emerged as a key platform for next-generation electronics due to their atomic thickness and tunable properties. Iron disulfide (FeS₂), known as pyrite, with a bandgap of ~0.95 eV, is suitable for solar energy applications. However, its performance is limited by defects in bulk crystals. Reducing FeS₂ to a single layer eliminates bulk defects and enables strain engineering of the bandgap. In this study, First-principles density functional theory (DFT) calculations are performed using the CASTEP code and the PBEsol functional to examine the structural, electronic, and optical properties of a distorted 1T′-phase FeS₂ monolayer. Full geometry optimization yields lattice parameters a′ = 17.594 Å, b′ = 3.20231 Å, c′ = 5.28091 Å, and Fe–S bond angles of ~75.8° and ~98.2°, confirming symmetry-breaking distortion. The monolayer is dynamically stable, showing no imaginary modes in the phonon dispersion, and remains structurally intact up to 1000 K in molecular dynamics simulations. The unstrained system has an indirect bandgap of 0.70 eV, with the valence band maximum at the Γ point (dominated by S-p states) and conduction band minimum near the X point (Fe-d states). Under mechanical strain (±4%), the bandgap decreases significantly: from 0.70 eV to 0.44 eV under +4% tensile strain along the y-axis, and to 0.53 eV under −4% compressive strain. Biaxial strain causes weaker modulation, reducing the gap to 0.66 eV (+4%) and 0.62 eV (−4%). Optical absorption exceeds 10⁴ cm⁻¹ for photon energies above the bandgap, with tensile strain causing redshifts and compressive strain inducing blueshifts. These findings demonstrate that 2D FeS₂ is mechanically robust, electronically tunable, and optically active, making it a promising candidate material for flexible strain sensors and photovoltaic devices. This work is intended to motivate and inform future synthesis efforts. Full article

This paper proposes and experimentally demonstrates a fiber ring laser (FRL) salinity sensing system based on a Sagnac loop (SL) formed by a tapered polarization-maintaining fiber (TPMF). The operating principle is that salinity modulates the birefringence of the polarization-maintaining fiber (PMF), causing a shift in the interference wavelength of the SL transmission spectrum, while the FRL narrows the optical spectrum and enhances the signal-to-noise ratio (SNR). In the experiment, the SL consists of a 20-cm-long PMF with a tapered waist diameter of 10.86 μm. Over the salinity range of 0‰ to 30‰, the sensitivity of the laser-based sensing system is 97 pm/‰, which agrees well with the 93 pm/‰ sensitivity obtained using a broadband light source (BBS), and the salinity exhibits a good linear relationship with the wavelength shift, with a coefficient of determination (R²) of 0.997. Meanwhile, the ring laser cavity improves the SNR of the sensing system from 22 dB to approximately 54 dB, and compresses the 3-dB bandwidth from 1.75 nm to 0.06 nm. Further adopting the support vector regression (SVR) algorithm for linear regression modeling of the spectral data, the results show that the mean absolute error (MAE) decreases from 0.50‰ to 0.04‰, the root mean square error (RMSE) decreases from 0.54‰ to 0.11‰, and R² reaches as high as 0.99988. To the best of our knowledge, this is the first work that combines salinity laser sensing with an artificial intelligence algorithm. The proposed sensor leverages the narrow linewidth and high SNR advantages of the FRL together with the high-precision linear fitting capability of the SVR algorithm, achieving significantly improved accuracy for salinity measurement compared to conventional spectral demodulation. Full article

Fiber optic gyroscopes (FOGs) are core sensors in inertial navigation systems, and their miniaturization and integration are currently hot research topics. This work presents an FOG system driven by a silicon photonics integrated circuit (PIC). The PIC, based on a 90 nm silicon-on-insulator (SOI) process, integrates core components such as polarizers, 3 dB couplers, and phase modulators within a compact footprint of 3 × 0.45 mm². These components exhibit excellent performance over a wide spectral range and play a crucial role in high-performance FOG systems. Experimental results show that the proposed FOG system can definitively measure the small angular velocity of the Earth’s rotation (±7.5 °/h). Further Allan variance analysis reveals that the FOG system has an angular random walk (ARW) of 0.00358 °/h^1/2 and a bias instability (BIS) of 0.1185 °/h. These results demonstrate the application potential of silicon photonics-based FOG systems. Full article

Multispectral temperature measurement technology based on blackbody radiation theory has been widely applied in the field of non-contact temperature measurement. However, its applicability is limited by the single-point measurement mode. To address this limitation, this study developed a spectral fusion temperature measurement device and proposed a new method for reconstructing the two-dimensional temperature field of deflagration fireballs by fusing spectral and imaging data. The device adopts a CCD sensor and a fiber optic spectrometer placed in parallel with parallel optical axes. To ensure the accuracy of the CCD’s response characteristics at different distances, the photo-response non-uniformity (PRNU) calculation method was used for precision validation. In this study, spectral and imaging data of deflagration fireballs were obtained through experiments. Spectral data of consecutive frames at 189 ms, 192 ms, 195 ms, and 198 ms were extracted and analyzed, confirming that the temperature range at the four time points is 1050 K to 1800 K. The proposed method generates temperature elements with equal temperature intervals and their probabilities within the temperature range, and calculates the theoretical radiation spectrum of each element. Then, least squares optimization fitting is performed on the experimentally measured spectra to obtain the optimal probabilities of the temperature elements in the temperature field. By combining these optimal probabilities with CCD grayscale images, the 2D temperature distribution of the deflagration fireball was reconstructed. Results show that: the PRNU value of the device at a distance of 9 m is less than 2.2% through experimental verification; fused images of the temperature field spectra of four consecutive frames of the deflagration fireball were obtained using the proposed method. The average temperatures reconstructed by the proposed method at 189 ms, 192 ms, 195 ms, and 198 ms were 1382 K, 1373 K, 1366 K, and 1357 K, respectively, while the corresponding temperatures obtained by conventional spectral inversion were 1430 K, 1422 K, 1414 K, and 1406 K. The relative errors were 3.2%, 3.4%, 3.3%, and 3.4%, respectively, with an average relative error of approximately 3.3%. Full article

This study investigates the dielectric relaxation and conduction mechanisms in $S{e}_{90}S{n}_{6}P{b}_4$ chalcogenide glassy material, which is of interest for applications in phase-change memory devices, optical memory, and thermoelectric sensors. Despite previous studies on chalcogenide glasses, the conduction mechanisms at varying temperatures and the role of correlated barrier hopping (CBH) remain unclear. Using impedance spectroscopy in the frequency range 1 Hz–1 MHz at temperatures from 288 K to 318 K, the real ($Z\prime$) and imaginary ($Z″$) parts of the complex impedance were recorded. The sample was also characterized by X-ray diffraction (XRD) to confirm its glassy nature, and X-ray photoelectron spectroscopy (XPS) to determine the surface chemical composition and oxidation states of the elements. Peaks in $Z″$ at each temperature were used to evaluate the relaxation time τ, revealing thermally activated processes with an activation energy of 0.62 eV. Nyquist plots showed semicircular behavior with decreasing radii at higher temperatures, indicating enhanced d.c. conductivity with an activation energy of 0.63 eV. A.C. conductivity analysis demonstrated frequency-dependent behavior consistent with the CBH model, with hopping energy calculated as 0.32 eV. The dielectric loss increased with temperature and decreased with frequency, stabilizing above 250 Hz at 318 K. These findings provide new insights into the dielectric and conduction properties of $S{e}_{90}S{n}_{6}P{b}_4$ glasses, supporting their optimization for practical electronic applications. Full article

This work presents an intrinsic optical fiber sensor based on plasmonic phenomena in modified plastic optical fibers (POFs). The sensing area is achieved by replacing the polymethyl methacrylate (PMMA) core with a molecularly imprinted polymer (MIP) containing gold nanorods (GNRs). Thus, in the sensing area, the MIP acts as both a selective recognition element and an optically sensitive guiding medium where plasmonic phenomena occur. This optical–chemical configuration has been developed as a proof-of-concept for the detection of furfural in aqueous solution. The proposed sensor achieves a limit of detection (LOD) of 27 pM, demonstrates high selectivity for the analyte of interest, and is applicable even in real-world scenarios, as demonstrated by experimental results (a commercially available infant milk). The proposed sensor presents a significant enhancement of the sensor response, of about six orders of magnitude, compared to a conventional configuration where the same (or a similar) mixture of MIP/GNRs is spun over the exposed PMMA of a D-shaped POF area for comparison. Notably, even if this study has been carried out via a proof-of-concept in furfural detection, this substantial improvement is achieved while preserving a simple, portable, and cost-effective optical setup, highlighting the potential of this sensing strategy for the development of highly selective sensors by changing the MIP template. Full article

This paper presents a hybrid methodology for the creation of educational mobile robots, combining the efforts of developers in design, construction, and instrumentation with the use of “Vibe Coding” as an alternative programming approach. To achieve this objective, the methodology integrates electronics and algorithmic thinking to enable adaptive behavior across three operating modes for robotics competitions. The mobile robot features a compact and modular architecture (430 g, 22 cm length × 14 cm width) with support components manufactured using 3D printing. Instrumentation included an Arduino Uno^® development board, a Syb-170^® proto shield, a buzzer, an HC-SR04^® ultrasonic sensor, an SG90 RC^® servomotor, a SSD1315 display, three TCRT5000^® reflective optical sensors, two DC motors with integrated 48:1 gearboxes, an L298N motor driver, and two 18650^® rechargeable lithium-ion batteries. Programming and algorithmic implementation were carried out using Vibe Coding, leveraging its intuitive environment to accelerate the development of three independent operating modes: (1) line follower on a racetrack, (2) obstacle avoidance with various objects, and (3) Bluetooth control via the free MIT Application Inventor. The mobile robot successfully demonstrated all three tasks, validating its suitability for educational and competitive purposes. Furthermore, its architecture supports AI-assisted decision-making through Vibe Coding, enabling dynamic responses to environmental disturbances. The multimodal configuration enhances navigation by correcting trajectory deviations, thereby improving robustness, adaptability, and overall functionality. Full article