Fiber extrinsic Fabry-Perot interferometric (EFPI) sensor has been widely applied in various scenarios due to its ability to detect multiple physical quantities such as pressure, noise, and vibration. Therefore, it is very important to demodulate its interference signal. Here we propose and experimentally demonstrate a high-accuracy largebandwidth phase-calibrated three-wavelength demodulation technique for measurement of static and dynamic signals from EFPI sensors. Three interference signals are introduced by selecting three arbitrary laser wavelengths. A phase calibration system is used for eliminating the errors in each signal. This allows for the determination of the true EFPI phase difference, which is then used to demodulate the cavity length variation. The technique can effectively eliminate the phase demodulation error introduced by the imbalance of optical power in the three-way signals, regardless of whether the EFPI is modulated with a phase over 2 pi or not. With a realtime demodulation frequency of 250 kHz, the system can correctly demodulate sinusoidal signals with amplitudes from 100 to 1340 nm and frequencies from 20 Hz to 120 kHz. When the cavity length of EFPI varies from 19 to 250 mu m, the demodulation signal is not distorted. Furthermore, when the technique is applied to demodulate a self-designed pressure sensor in the range of 0-14 MPa, a demodulation error of only 0.67 % and a cavity length resolution of 0.142 nm are obtained. This technique has the advantages of strong robustness, large dynamic range, fast demodulation rate, and high accuracy. Therefore, it has great potential for application in the engineering field.

ABSTRACT Sustainable triboelectric materials that combine high sensitivity, mechanical compliance, and wearable integration are highly desirable for next‐generation human‐machine interfaces. Here, we report a fish‐scale‐derived triboelectric composite for conformal self‐powered sensing and sign‐language recognition. Calcined fish‐scale powder (CFSP) is incorporated into a polyvinylidene fluoride (PVDF)/polydimethylsiloxane (PDMS) architecture through electrospinning and spin coating, forming heterogeneous micro/nano interfaces that are favorable for interfacial charge accumulation and interfacial polarization. The resulting triboelectric layer exhibits stable output over 10 000 cycles and a multi‐region pressure response, with pressure sensitivities of 2.82, 1.41, and 0.42 V kPa −1 in the pressure ranges of 0.25–1.25, 1.25–5, and 5–12.50 kPa, respectively. Based on this designed material, a structurally decoupled 9‐channel sensor array is constructed to improve channel independence while conforming to the three‐dimensional curvature of the hand. The glove enables distributed acquisition of hand‐motion signals ranging from subtle skin deformation to forceful gripping. Coupled with a spatial‐temporal graph convolutional network, the platform achieves 94.43% recognition accuracy for representative sign‐language gestures and supports real‐time translation of continuous motion sequences. This work provides a biowaste‐to‐functional‐material route for triboelectric interfaces and a conformal self‐powered sensing front‐end for wearable sign‐language interaction.

Microsphere-assisted super-resolution imaging technology, due to its ability to break through the diffraction limit, has become a powerful tool for achieving optical observations at the micro-nano scale. However, there remains a significant discrepancy between the simulation results of microsphere focusing behavior and experimental observations in existing studies, necessitating a more precise physical explanation. This study proposes that the interface reflection characteristics are a key factor influencing the focusing behavior of microspheres. We constructed a numerical simulation model based on ray optics theory using MATLAB, explicitly considering the reflection and transmission of light at the microsphere-medium boundary, and systematically analyzed the imaging process and focal position of the microsphere. Experimental results demonstrate that after accounting for energy loss due to reflection, the focal position obtained from the simulation calculations shows a high degree of consistency with the experimental results. The average deviation of our model from experimental results is reduced by 76% compared to conventional paraxial theory and by 86% compared to Finite-Difference Time-Domain (FDTD) simulations. Additionally, the findings validate the reliability of determining microsphere focusing theory using irradiance.

Abstract In order to realize the high-precision inversion of the two-dimensional temperature of the engine flame, a multispectral radiation thermometer based on the confocal imaging is designed in this paper. The thermometer utilizes the beam splitter to realize multi-channel output with a common field of view. Relying on a dedicated spectral diagnosis channel, it analyzes combustion flame spectra through eliminating characteristic spectral lines and selecting the continuous radiation spectra applicable to temperature measurement. The flame spectral radiation image at the selected wavelength is captured by three imaging channels. A three-dimensional regulating structure was designed for the thermometer. After the confocality calibration, optical path calibration and radiant intensity calibration of the thermometer, the engine flame temperature measurement experiments are conducted. The experimental results show that the highest temperature of the flame is 2057.21 °C and the average temperature is 1643.05 °C in the most intense combustion 4 s. And the maximum relative error is found to be within 8%. The designed thermometer can effectively realize the two-dimensional temperature inversion of the combustion flame and provide a diagnostic basis for the combustion characteristic test.

Chaotic lasers have been extensively studied for myriad applications. Yet, for typical chaotic devices, the power spectra are usually uneven with severe energy deficit at low frequencies, which poses a significant hurdle for realizing their practical applications. Here, we present a simple method to enhance chaotic low-frequency energy by injecting original chaos signal into a micro-ring resonator. The effects of detuning between the resonance frequency and chaotic frequency on the ratio of energy distribution in low-frequency bands are investigated. Our experimental results show that when the frequency detuning is 0 GHz, flat chaos with a bandwidth of 8.3 GHz can be obtained, and the energy ratios of the low-frequency bands concentrate at approximately 11%. Furthermore, we demonstrate that the significant enhancement of low-frequency energy is caused by the mode competition. The proposed scheme provides an attractive solution for flat chaos generation and shows great potential for chaos-related applications.

Self-powered and skin-conformal sensors capable of quantitatively monitoring subtle facial motions are of great significance for objective evaluation of facial paralysis and rehabilitation progress. Herein, a fully encapsulated circular triboelectric nanogenerator (TENG) is developed for self-powered facial motion monitoring and facial paralysis assessment. The device adopts a multilayer sandwich architecture composed of polyurethane (PU) encapsulation layers, a copper electrode, a 3 M double-sided adhesive frame, and an electrospun polyvinylidene difluoride (PVDF) nanofiber film doped with carbon nanotubes (CNTs). Benefiting from the flexible encapsulation and circular geometry, the sensor exhibits excellent mechanical compliance and conformal attachment to high-curvature facial regions, such as the canthus and mouth corner. Operating in a contact–separation triboelectric mode, the device efficiently converts facial muscle–induced mechanical deformations into electrical signals without requiring an external power supply. The optimized device demonstrates a high sensitivity of 11.1995 V N⁻1, enabling reliable detection of subtle facial muscle activities. Stable and distinguishable electrical outputs are achieved under different facial expressions, allowing quantitative analysis of facial motion intensity and left–right asymmetry. Importantly, the device enables robust quantification of facial left–right asymmetry for facial paralysis assessment, which is difficult to achieve using conventional non-uniform or locally sensitive wearable TENG configurations. This work provides a promising self-powered sensing platform for objective facial paralysis evaluation, long-term rehabilitation monitoring, and future wearable healthcare applications.

In this paper, a comprehensive investigation into the single-mode capability of curved sapphire fiber is performed, ranging from theoretical simulation to experimental verification. The equivalent refractive index theoretical model for curved sapphire fiber is proposed based on stress-optic effects and the conformal mapping technique. According to the finite element method, when the radius of curvature is 0.02 m, the curved losses' difference between high-order modes and the fundamental mode is as high as five orders of magnitude, demonstrating the best single-mode potential. In addition, the curving experiments of sapphire fiber and sapphire fiber Bragg grating are completed. The transmission spectrum of the curved sapphire fiber with a curving radius of 0.02 m is the closest to that of the single-mode fiber. As for curved sapphire fiber Bragg grating (CSFBG), the 3 dB bandwidth of reflection spectrum with the same radius of curvature is also the smallest, with a value of 3.7 nm. Furthermore, the temperature performance of the proposed CSFBG is measured from 22 °C to 1600 °C. The sensitivity is 37.88 pm/°C (@1600 °C), and the measurement accuracy is ±2.98 °C. This study provides theoretical support for single-mode signal transmission of curved sapphire fibers and facilitates high-precision sensing applications under extreme high-temperature conditions.

Geometric microstructures fabricated by advanced micro- and nanofabrication techniques are widely employed to tailor optical functionalities such as scattering, diffraction, and wavefront modulation. However, geometric design has rarely been explored as an independent means to regulate visual accessibility itself, as structural visibility is generally regarded as inseparably coupled to imaging modality and material contrast. Here, we propose and experimentally validate a geometry-driven visibility decoupling strategy that enables microstructures to exhibit fundamentally different visual appearances under incoherent intensity imaging and coherent optical reconstruction. By precisely controlling the slope continuity of microstructures, the spatial frequency distribution at structural boundaries is systematically regulated, elevating visual accessibility to a physical, parameterizable design degree of freedom. We demonstrate that continuous slope-mediated geometries effectively suppress observable contrast under numerical-aperture-limited incoherent bright-field imaging while fully preserving phase information, allowing faithful reconstruction under coherent holographic imaging. This mechanism emerges intrinsically from geometric continuity rather than material heterogeneity or algorithmic encryption. Using a two-photon-polymerized Mnemosyne Chip as a unified validation platform, this work establishes a general design paradigm for micro-scale information storage, optical anti-counterfeiting, and selective visualization, positioning physical microstructures as selectively accessible information carriers.

Although sapphire optical fibers exhibit excellent tolerance to extremely high temperature environments, their multimode characteristics and the resulting fluctuations in the wide reflection spectrum limit their sensing applications. To address this issue, we propose and demonstrate a novel sapphire optical fiber high-temperature sensor that utilizes a decreasing-ring grating structure fabricated through a femtosecond laser direct writing process. This design effectively suppresses the higher-order modes of the sapphire optical fiber. By employing arc discharge technology to create the fusion points between sapphire fibers and single-mode fibers, we generate a single, narrow reflection peak with a full width at half maximum (FWHM) of approximately 0.27 nm at 1530.67 nm, measured at room temperature (approximately 24 degrees C). This sensor exhibits high temperature sensitivity and can maintain stable operation above 1500 degrees C in harsh high-temperature environments. Due to its exceptional temperature tolerance, narrow bandwidth, and resistance to electromagnetic interference, this sensor holds significant potential for precise temperature sensing in extremely challenging conditions.

Mental health disorders, especially those involving subtle somatization symptoms, urgently require continuous and noninvasive monitoring tools capable of capturing fine emotional cues. Microexpressions originate from transient, low-amplitude muscle activations and therefore demand sensing systems with high sensitivity and robust spatiotemporal resolution. In this work, we introduce a triboelectric sensing platform enabled by interface micro-engineering, realized through a microsphere-enhanced PVDF/CNT-PDMS composite architecture. By embedding uniformly distributed microspheres at the PDMS interface and coupling them with a PVDF/CNT electrospun layer, the engineered micro-contact configuration significantly increases effective contact area, local deformation uniformity, and interfacial charge density. The composite architecture maintains excellent flexibility and skin conformity, enabling stable performance under natural facial deformation. This design yields a sensitivity of 12.376 V & sdot;N-1 and ensures stable electrical output under rapid and complex facial dynamics. When applied to key facial regions, the sensor resolves distinct pressure signatures associated with microexpressionlevel muscle fluctuations. Integrated with a Temporal Convolutional Network, the system recognizes seven emotional states with an accuracy of 93.43 % by extracting characteristic spatiotemporal features from both subtle and large-scale facial motions. This interface-engineered, self-powered sensing framework provides a promising route toward real-time and privacy-preserving mental health monitoring as well as more intuitive human-machine interaction.

When conducting infrared temperature measurements on anodized aluminum materials, the measurement results of the thermal imager are prone to be affected by factors such as distance variation, working time, and observation angle. To address these issues, a two-stage temperature compensation strategy that combines physics-guided feature engineering with deep learning is proposed. The method leverages physically-derived feature representations based on infrared radiation transmission and geometric features to guide the neural network, which effectively eliminating the interference of multi-factor nonlinear errors. In the first stage, a multi-head attention mechanism is employed to obtain the feature correlations between the test distance and working time, in order to solve the system temperature errors caused by distance variation and time drift. In the second stage, a directional emissivity compensation model is constructed using three-dimensional point clouds and temperature data to address the three-dimensional geometric errors caused by changes in observation angle. Experimental results show that this method reduces the average measurement error from 6.34 °C to 0.41 °C, and can achieve three-dimensional temperature reconstruction of anodized aluminum materials in complex environmental conditions.

The in situ sensing of parameters such as pressure and vibration in extreme environments poses a demand for high-temperature resistance, and Fabry-Perot (F-P) cavities based on quartz volume Bragg gratings (VBGs) are well-suited to this demand. The fabrication of quartz VBGs relies on femtosecond lasers, and the method of optimizing the performance of VBGs through trial and error requires considerable time investment. Machine learning technology, which performs tasks through statistical techniques and numerical algorithms, offers a new approach to addressing this challenge. This study proposed and constructed a machine learning-driven bidirectional intelligent prediction model for the relationships between parameters and performance of VBGs. Forward prediction from parameter to performance was implemented using the outlier labeling random forest (OLRF) model, which integrates the random forest algorithm with an outlier labeling optimization method. The model achieves R2 values of 0.994 and 0.992 on the test set, respectively. Inverse prediction from performance to parameter was constructed using particle swarm optimization and genetic algorithms, with errors of less than 1% in reflectivity and 0.1 nm in FWHM. The OLRF-PSO model completes the inverse design of VBG micro-parameters in only 25 s. To demonstrate the practical feasibility, the designed VBG structures are applied to a Fabry-Perot cavity simulation. The results confirm that the generated grating parameters are well-suited for cavity integration, thereby providing data and technical support for the design optimization and application of F-P cavity-based optical sensors. (c) 2026 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

Flame velocity detection using optical techniques remains challenging in in-field combustion diagnostics, and this type of detection is urgently needed to improve the propulsion capability and combustion stability of aerospace engines. However, the measurement error caused by plasma self-evolution using plasma velocimetry has not yet been effectively addressed. In this study, we report the application of the laser-induced plasma tracking velocimetry (LIPTV) method for fluid velocity measurements in airflow and in situ combustion fields. This method captures the temporal images of laser-induced plasma excited by a nanosecond pulsed laser using a high-speed imaging technique, and the results show that there is a superior correlation between the fluid velocity and the plasma motion. We established a self-perception velocity estimation network (SPVEN) by designing a weight-aware attention module to decrease the effect of the plasma self-evolution. This module dynamically adjusts the plasma image weights at different stages. The proposed SPVEN reduced the mean absolute error of prediction by more than 6.9 times compared with the conventional algorithms for the plasma images in the airflow field. For the supersonic flame in the extreme environment, the velocities were measured using a LIPTV-based homemade system with the mean relative error of less than 4.8 %. The results suggest that the proposed LIPTV with deep learning effectively improved the accuracy of the velocity measurement, which enables it to play a significant role in measuring velocity for supersonic wind tunnels and combustion fields.

With the growing demand for foot-state measurement and motion monitoring in smart wearables, harvesting biomechanical energy from human gait offers a sustainable power solution for wearable sensing systems. However, existing foot-based harvesters often suffer from discomfort and low efficiency under real gait conditions, which limit their practical applications in long-term wearable monitoring. This study proposes an Energy Harvesting and Buffering Integrated Smart Shoe (EHBI-SS) that converts gait kinetic energy into electricity based on the electrodynamic transducer principle for self-powered motion-state recognition. A rectangular Halbach magnet array is designed to enhance the output power of the energy harvester by intensifying the spatial gradient of the magnetic flux density. Finite element simulations indicate that its magnetic flux density is increased by approximately 60.6% compared with the conventional array, while the staggered spring buffer layer effectively disperses impact forces and maintains structural stability. Coordinated optimization of harvesting and buffering is realized through gait analysis. Experiments show that under a 2 Hz excitation frequency, the EHBI-SS delivers a peak power of 78.6 mW, volumetric power density of 1.97 mW/cm³ and gravimetric power density of 0.93 mW/g. In addition, heel acceleration peaks are reduced by 29.2%, indicating effective mechanical buffering. Based on the acquired signals, a low-power adaptive algorithm enables accurate identification of four motion states with a recognition accuracy of 99.17%. These results demonstrate that the proposed EHBI-SS provides a reliable self-powered solution for stable wearable sensing, achieving efficient energy harvesting and highly accurate motion-state recognition under real walking conditions.

Fiber optic vibration sensors are widely used in extreme environments such as aerospace and energy equipment for structural health monitoring due to their resistance to electromagnetic interference, corrosion, and high temperatures. Traditional fiber Bragg grating (FBG) or Fabry-P & eacute;rot (F-P) vibration sensors are often used to measure a single physical parameter, so they can encounter some challenges such as limited measurement accuracy, significant influence of temperature perturbation, and inability to self-calibration. Here, we propose a high-temperature dual-parameter self-calibrating fiber-optic vibration sensor by integrating an F-P resonant cavity with a dual FBGs structure in a double-cantilever beam sensitive diaphragm to simultaneously obtain two vibration signals from the same location. Two sets of FBGs with different periods are inscribed in the fiber core by using a femtosecond laser. One FBG is attached to a silicon-based diaphragm for vibration sensing, while the other is attached to the surface of the sensor package for temperature measurement and compensation. The F-P cavity is composed of the fiber end face with the diaphragm to sense vibration signal. The recombination-type sensor has outstanding optical responses from room temperature to 500 degrees C. And it has a high measurement accuracy of 0.1 g for weak signals and a large dynamic measurement range of 0.1-2000 g for strong vibration. Moreover, the two types of vibration sensors can be mutually calibrated in different application environments. Therefore, this study provides a self-calibrating solution for vibration sensing in high-temperature and strong-vibration environments, expanding the applicability of fiber optic sensing technology under extreme conditions.

Traditional sensing technologies usually can only measure a single physical parameter and lack temperature compensation functionality, making it difficult to meet the urgent demands for multi-parameter synchronization and precise monitoring. In order to address these issues, we present an enhanced high-temperature broadband multi-parameter in-situ fiber-optic sensor based on cascaded fiber Bragg grating (FBG) and open Fabry-Perot (F-P) interferometer probe. The sensitive core structure prepared by arc discharge technology is composed of two sets of FBGs, multimode fiber (MMF), hollow-core fiber (HCF), and photonic crystal fiber (PCF). Two sets of FBGs written by femtosecond laser are placed on a sensitive diaphragm with a mass block and a packaging surface to monitor acceleration and temperature, respectively. An open-type F-P cavity formed by HCF and PCF is used to enhance noise response. Besides, FBG placed on the packaging surface is also used to achieve temperature compensation to prevent the influence of temperature on the other two parameters. Experimental results show that the self-designed sensor can perform self-calibration within the range of 25-600 degrees C. And it has a maximum sensitivity of 0.082 mV/Pa in the range of 100 Hz-10 kHz for noise signals. It can also simultaneously conduct acceleration tests over a wide range of 0-1938 g, and has a large bandwidth of 10-2100 Hz for acceleration signals. Furthermore, the sensor exhibits excellent consistency, repeatability, and stability, which can be applied to long-term in-situ multi-parameter health monitoring and fault diagnosis in extremely harsh environments such as aviation engines and gas turbines.

In a frigid environment, individuals engaging in outdoor activities or work face a serious risk of frostbite on their feet. Existing self-heating and heated shoes struggle to meet the urgent demand for continuous, lightweight, and self-regulating warmth. This study proposes a lead screw-regulated electromagnetic energy harvester (LSR-EEH) with high active power for developing lightweight self-heating shoes. The LSR-EEH employs the lead screw structure to convert potential energy from the foot into rotational motion of the Halbach magnet array. By designing the LSR-EEH resonance frequency to match the one-step walking cadence, the maximum mechanical-to-electrical energy conversion efficiency of 63.65% is achieved. With the diameter of 47 mm and the weight of 97.5 g, the LSR-EEH can be easily integrated into the heel area of various types of shoes. Experimental results indicate that under impedance-matching load of 80 Ω and the pressing frequency of 1 Hz, the LSR-EEH generates an active power of 0.732 W and a power density of 9.32 mW/cm3. After operating for 1100 s, the lithium battery can be charged from an initial 3.42 V to 3.97 V. Additionally, a BP neural network-based constant-temperature power prediction model is proposed for predicting the minimum power required to maintain constant temperature inside the shoes. This work provides an effective self-power strategy to address the high power and lightweight demand for foot heating, demonstrating promising application prospects for foot warmth in extreme environments.

Triboelectric nanogenerators (TENGs) are effective at harvesting weak acoustic energy, yet existing devices still lack sufficient sensitivity and stability. This study develops a high-performance acoustic TENG (A-TENG) comprising an acoustic cavity and a triboelectric acoustic sensor (TAS). The TAS features an innovative negative triboelectric layer made by hot-pressing a FEP/P-PTFE/FEP three-layer film, significantly enhancing charge and output performance, while a thinner PI-based positive layer with screen-printed Ag enables an ultra-high sensitivity of 1.24 × 105 V kPa−1. The A-TENG detects sound as low as 45 dB across 20–20,000 Hz, maintains stable output over three weeks, and delivers up to 388.5 V peak-to-peak voltage, an RMS voltage of 137.35 V and 48.9 V/cm2 output density at 400 Hz and 100 dB. The acoustic cavity incorporates a power-law thickness variation structure coupled with a diffuser, enabling simultaneous harvesting of acoustic energy and wind energy. Coupled with deep learning, the system achieves 99.11% accuracy in sound distance detection and 99.5% in song recognition, reaching 99.6% across genres, demonstrating strong potential for human-computer interaction and smart home applications.