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.

Transient and in-situ monitoring of structural surface temperature and heat flux is critical in extreme aerodynamic heating environments, such as aerospace applications. While G-type coaxial thermocouples possess immense potential for ultra-high temperature measurement, the thermally activated leakage of conventional electrical insulation materials at extreme temperatures causes severe signal cross-talk, restricting their upper measurement limit. This study proposes a high-performance G-type coaxial thermocouple utilizing a flash Joule heating (FJH) HfO2 electrical insulation layer to enhance measurement accuracy and metrological stability under extreme conditions. The FJH process triggers rapid mass transport, yielding a highly dense, large-grain microstructure (average grain size of 1626.10 nm) that effectively eliminates high-temperature leakage channels, maintaining an insulation resistance of 61.13 kΩ at 1200°C. To systematically evaluate its metrological characteristics, a comprehensive calibration system was established. Static calibration results indicate excellent linearity (R2 = 0.99995) from 200°C to 1200°C, with a maximum absolute temperature accuracy error of 2.06%, hysteresis error below 1.06%, and an ultra-low high-temperature drift rate of 0.0172%/h. Dynamic response tests reveal exceptional transient sensing capabilities, exhibiting response times of 1.03 ms for temperature and 1.67 ms for heat flux. Furthermore, the sensor successfully withstood extreme heat flux impacts exceeding 25 MW/m². The achieved synchronous dual-parametric measurement provides a high-precision, highly reliable sensing solution for thermal protection evaluation in extreme aerodynamic heating environments.

Dynamic temperature drift severely compromises the accuracy of piezoresistive pressure sensors in marine environments. This study introduces a novel Adaptive Weighted Slime Mould Algorithm-Extended Kalman Filter (AWSMA-EKF) method to address time-varying thermal instability. The research begins with the design, fabrication, and comprehensive characterization of a silicon piezoresistive pressure sensor, where the analysis of its temperature sensitivity mechanisms provides a foundation for dynamic modeling. Test results showed that the fifth-order polynomial of the sensor has a very small fitting residual in static calibration, but exhibited a significant lag when exposed to dynamic temperature variations, highlighting the fundamental limitation of static compensation approaches. To overcome this, a dynamic compensation framework is proposed, which constructs an EKF model with temperature and pressure as combined state variables and employs the AWSMA to dynamically optimize the EKF's noise covariance matrices online. Through simulated tests in dynamic marine environments, the AWSMA-EKF reduced the sensor's mean absolute error (MAE) from 4.42% FS (before compensation) to a remarkably low 0.05% FS. The root mean square error (RMSE) decreased by a maximum of 76% and 68% compared to PSO-EKF and SMA-EKF, respectively, demonstrating the superior optimization performance of the proposed AWSMA. The algorithm's effectiveness was further validated in a real ocean environment, with experiments demonstrating a 94% reduction in pressure measurement fluctuations at a depth of 400 m, thus verifying its engineering practicality. This study provides an innovative and effective solution for high-precision dynamic compensation of MEMS pressure sensors in highly dynamic and harsh environments.

To address the issues of low light absorption efficiency and limited temperature gradient distribution in conventional planar Ag2Se-based photothermoelectric (PTE) detectors, this paper proposes a structured design strategy for the surface functional layer. Ag2Se-based PTE detectors with periodic surface microstructure arrays were fabricated using photolithography, and the influence of surface structure on the device's PTE response performance was systematically investigated. The results indicate that surface microstructures can enhance light absorption and localized photothermal conversion efficiency, thereby increasing the PTE output voltage. However, they also lengthen the thermal diffusion path and reduce the dynamic response speed. When the structural pitch is 6.7 um, the device exhibits optimal overall detection performance within the measured spectral range of 405-950 nm. Under irradiation at a wavelength of 950 nm and a laser power density of 120 mW/cm2, the device achieved a voltage sensitivity of 0.14 mV/W. This study reveals the trade-off between enhancing the response performance and response speed of Ag2Se-based PTE detectors through surface structural design, providing experimental evidence and design guidance for rationally optimizing device structural parameters and realizing room-temperature PTE detection.

Various studies have been conducted on the quantification of alkaline earth elements, such as Ca and Sr, using underwater laser-induced breakdown spectroscopy (LIBS). However, the used spectral lines are either ionic or atomic lines, while less attention has been paid to molecular emissions. In this study, we compared the ionic, atomic and molecular emissions for the quantitative analysis of Ca and Sr in underwater LIBS. The emission spectra of Ca II, Sr II, Ca I, Sr I, CaOH, and SrOH were collected at different concentrations of Ca and Sr dissolved in water. Both a quartz cuvette and a high-pressure chamber were used to establish the calibration curves for Ca and Sr under atmospheric pressure and high pressure conditions. This shows that the molecular emissions of CaOH and SrOH account for a significant portion of the total plasma emissions within the period from 200 ns to 2 & micro;s due to the rapid recombination process of Ca and Sr atoms with OH radicals. Although the LoDs of CaOH and SrOH are slightly higher than those of their atomic or ionic emissions, they exhibit unique features in reducing the influence of self-absorption on the calibration curves. Moreover, the CaOH and SrOH molecular emissions have good pulse-to-pulse stabilities and suffer less interference from varying ambient pressures, which can clearly improve the accuracy and precision of the calibration curves of Ca and Sr, especially under high pressure conditions. This study provides insights into improving the quantitative performance of Ca and Sr in underwater LIBS using molecular emissions instead of the commonly used ionic or atomic emissions.

This study proposes a novel micro-electromechanical system (MEMS) piezoelectric hydrophone with a dual-piezoelectric-layer structure to overcome the sensitivity limitations of traditional single-piezoelectric-layer devices in low-frequency bands and high-noise environments. The design is based on a silicon-on-insulator (SOI) wafer. It employs a five-layer stacked configuration consisting of a top electrode/first scandium-doped aluminum nitride (ScAlN) piezoelectric layer/middle electrode/second ScAlN piezoelectric layer/bottom electrode. By optimizing the neutral-axis position toward the middle electrode, the upper and lower piezoelectric layers experience opposite and nearly symmetric stress states, enabling a constructive combination of their differential electrical outputs. Additionally, a differential electrical connection is employed to enable an output voltage approaching twice that of the single-layer structure under ideal symmetric conditions, thereby significantly enhancing the electromechanical coupling efficiency and device sensitivity. Theoretical analysis confirms that this structure reduces energy loss in non-piezoelectric layers and enhances charge collection efficiency. Performance test results show that within the 100 Hz to 20 kHz frequency range, the average sensitivity reaches -172.5 +/- 1.7 dB (re: 1 V/mu Pa), with an equivalent noise density (END) of 49.6 dB (re: 1 mu Pa/root Hz) at 1 kHz, a piezoelectric coupling coefficient of 9.1%, and excellent linearity (R-2 = 0.998). Compared with reported single-piezoelectric-layer MEMS hydrophones and representative commercial bulk piezoelectric devices, this device exhibits improved sensitivity and noise performance under the compared conditions, indicating its potential for applications such as marine exploration, underwater communication, and pipeline leakage monitoring.

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.

Flexible strain sensors require a wide strain range and high sensitivity for applications from human joint monitoring to robotic motion detection. Conventional wired systems limit motion, especially in underwater and wearable scenarios. Here, we present an ion-electron synergy-enhanced flexible highly sensitive wireless sensing system (IESS) with wide strain range, in which ionic and electronic conduction synergistically amplify strain-induced resistance changes. By combining multi-walled carbon nanotubes (MWCNTs), ionic liquid, and a gold layer, a three-dimensional porous conductive network forms. Applied strain induces microcracks that interrupt electron pathways while reconfiguring ionic transport channels, enabling high sensitivity over a wide strain range (gauge factor, GF = 1.985 × 104, 100%). The system integrates sensing, power, and wireless communication in a compact platform for multimodal applications. With machine learning, it achieves 93.3% accuracy in phonation recognition and distinguishes diving, ascending, and forward swimming of bionic shark robots, as well as monitors buoy strain underwater. These results demonstrate the advantage of ion-electron synergy in enhancing sensing performance and highlight the system's versatility for bioinspired robotics and wearable health monitoring.

Flexible pressure sensors that mimic human skin are attractive for electronic skin, soft robotics, and healthcare, but it remains difficult to combine ultrahigh sensitivity, wide range, and long-term stability in one device. Here, we present an ultra-sensitive iontronic pressure sensor (USIPS) based on a fingerprint-inspired spiral interpolating electrode, a laser-processed spacer, and a thermoplastic polyurethane (TPU)/graphene/multi-walled carbon nanotube (MWCNT)/ionic-liquid composite layer microstructured into cylindrical protrusions by femtosecond-laser engraving. This architecture amplifies contact mechanics and electric-double-layer modulation, delivering ultrahigh sensitivities of ∼1.92 × 105 kPa-1 (0-110 kPa), ∼6.58 × 104 kPa-1 (110-300 kPa), and ∼1.91 × 104 kPa-1 (300-900 kPa), together with a wide detection range (from ∼577 Pa to 900 kPa), fast response (∼20 ms), and excellent stability under prolonged high-pressure loading. Coupled with a tailored signal-processing and machine-learning pipeline, the USIPS enables quantitative hardness/softness perception and accurate discrimination of representative materials, while also supporting pulse monitoring, robotic grasping, and plantar gait analysis. These results demonstrate a unified iontronic-AI platform for wide-range, high-fidelity tactile sensing and intelligent perception.

ABSTRACT Poly (vinylidene fluoride) (PVDF) and its copolymers have become pivotal materials for piezoelectric tactile sensing in wearable electronics, human–machine interfaces, and electronic skin. This review presents a structured overview linking material fundamentals, device engineering, and data‐driven intelligence. First, we outline PVDF fundamentals, including development history, polymorphism, electroactive properties, and operating principles of PVDF‐based piezoelectric tactile sensors. Second, we review material‐level optimization strategies, covering phase‐engineering routes (mechanical stretching, electrical poling, thermal annealing) and composite, copolymer, interfacial, and core–shell designs, and relate them to β‐phase formation, polarization, dielectric response, and figures of merit (d 33 , g 33 , dielectric loss). Third, we compare device‐level microstructured sensing layers, electrodes, substrates, and multilayer or array architectures in terms of sensitivity–linearity trade‐offs, response dynamics, and directional recognition capability. We then highlight the emerging role of machine learning in phase prediction, process optimization, tactile‐signal classification, and multimodal fusion for robust perception. Finally, we survey representative applications in health monitoring, wearable human–machine interaction, and electronic skin, and summarize key outlooks on scalable fabrication, standardized evaluation, sustainability, neuromorphic tactile computing, and AI‐assisted system design, outlining opportunities for PVDF‐based tactile sensors in intelligent healthcare, bioinspired robotics, and human–machine symbiosis.

Objective.This study focuses on a non-invasive blood pressure prediction method based on radial artery pressure pulse wave feature analysis, aiming to achieve rapid and accurate blood pressure measurement, help doctors diagnose diseases more accurately, promote the development of digital pulse diagnosis, and provide a new approach for blood pressure monitoring in wearable devices.Approach.By extracting 25 multidimensional features (including time-domain, frequency-domain, and nonlinear features) of the radial artery pressure pulse wave, and after feature correlation analysis and importance assessment, weights were assigned to establish a blood pressure prediction model based on the random forest algorithm.Main results.Experimental results show that the mean absolute errors of systolic and diastolic blood pressure prediction are 3.39 mmHg and 2.06 mmHg, respectively, with standard deviations of 4.76 mmHg and 2.78 mmHg, respectively, both meeting the commonly referenced AAMI performance criteria (MAE⩽5mmHg andSTD⩽84mmHg), which are widely used as benchmarking thresholds in non-invasive blood pressure estimation studies.Significance.Compared with current mainstream methods, such as Photoplethysmography combined with Electrocardiogram (ECG) prediction and pulse wave velocity estimation, this model demonstrates higher prediction accuracy and convenience advantages in wearable device applications.

With the widespread application of lithium batteries in electric vehicles and energy storage systems, battery-related safety and reliability issues have become increasingly prominent. Conventional monitoring methods often struggle to address dynamic changes under complex operando. In recent years, flexible sensing technology has emerged as a promising solution for battery health monitoring due to its high adaptability and conformability to complex structures. Meanwhile, empowered by artificial intelligence (AI) for data analysis, the collected data enables efficient and accurate state assessment, offering robust support for accident prevention. Against this background, this paper first explores the integrated applications of flexible sensors in battery health monitoring and their unique advantages in addressing complex battery operating conditions, while analyzing the potential of AI in battery state analysis. Subsequently, it systematically reviews mainstream flexible sensing technologies (e.g., film sensors, thermocouples, and optical fiber sensors), elucidating their mechanisms for revealing intricate internal battery processes during operation. Finally, the paper discusses AI’s role in enhancing monitoring efficiency and accuracy, and envisions future research directions and application prospects. This work aims to provide technical references for the battery health monitoring field as well as promote the application of flexible sensing technologies in improving battery system safety and reliability.

The development of high-performance, environmentally benign broadband photodetectors beyond the capability of silicon is a persistent challenge. Although tin selenide (SnSe) is a promising candidate due to its high carrier mobility and broad spectral absorption, its device performance is fundamentally limited by poor film quality and defect proliferation stemming from uncontrolled stoichiometry. Here, this bottleneck is overcome by a stoichiometry-controlled magnetron co-sputtering strategy, which enables in-situ and precise tuning of the Sn/Se ratio. This unique approach yields high-quality, stoichiometric SnSex films on silicon, forming a robust heterojunction. The research reveals that the optoelectronic performance of devices with different compositions is influenced by intrinsic material defects and bandgap characteristics. As a result, the fabricated self-powered photodetectors exhibit widely tunable bandgaps (0.83-1.14 eV) and a broad spectral response ranging from 220 to 1550 nm. The resulting photodetectors achieve a record-high specific detectivity (D*) of 1.57 & times; 1013 Jones, representing one of the highest performances reported for any self-powered SnSe-based device. This superior performance is coupled with a fast response (54.8/17.5 ms) and excellent operational stability. This work not only sets a new benchmark for SnSe photodetectors but also establishes a scalable and eco-friendly fabrication route for next-generation broadband optoelectronics.

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.

Correction for 'An ultra-sensitive iontronic pressure sensor with femtosecond-laser-engraved microstructures for machine-learning-based tactile sensing' by Yihui Lan et al., Nanoscale, 2026, 18, 5242-5254, https://doi.org/10.1039/d5nr05111h.

PurposeEnhancing the high-frequency performance of piezoelectric micro-electro-mechanical ultrasonic transducers (PMUTs), particularly the transmission performance of PMUT arrays, is a key focus in the advancement of MEMS technology. In this paper, a design strategy for a high-frequency ultrasonic transducer array based on aluminum nitride (AlN) is proposed.Design/methodology/approachThis paper proposes a design scheme for a high-frequency ultrasonic transducer array based on AlN material. This study established a three-dimensional finite element model of a PMUT and further designed two representative diaphragm structures - circular and hexagonal - as its sensing elements. Using multiphysics finite element simulation, this study focuses on optimizing the diaphragm shape of the PMUT-sensitive element. Concurrently, it proposes a honeycomb-like ultra-narrow equidistant arrangement scheme based on sealed silicon cavity technology and conducts acoustic performance simulations of the PMUT array.FindingsBy comparing the performance of different diaphragm shapes and array structures in terms of transmit voltage response, reception sensitivity, axial pressure distribution and array acoustic beam patterns, it is concluded that circular diaphragms and PMUT arrays based on equidistant narrow arrays exhibit outstanding acoustic performance.Originality/valueThis work not only provides valuable guidance for the design, simulation and fabrication of PMUT-sensitive units but also offers novel insights into the application of PMUT arrays in high-frequency scenarios.

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.

Leveraging graphene's giant third-order nonlinearity and ultrafast response, we experimentally demonstrate a graphene-integrated slot waveguide designed to maximize the effective light-matter interaction. Through four-wave mixing experiments, we achieved a conversion efficiency of -29.1 dB in a slot waveguide covered with an optimized graphene-light interaction length of 60 mu m, outperforming the slot waveguide structures by approximately 18.4 dB. This device not only provides pathways for realizing high-speed and low-power all-optical signal processing, but also lays a sound theoretical and experimental foundation for the design and integration of highly nonlinear silicon photonic devices.

Nonlinear integrated photonic devices, distinguished by their ultra-strong nonlinear response, wide tunability, and broad bandwidth, are pivotal for advancing optical communications, all-optical signal processing, and photonic computing. Here, we propose a silicon multimode slot waveguide integrated with graphene that exhibits an extraordinary nonlinear coefficient of 5 & times; 108 W- 1m- 1, representing an enhancement of over six orders of magnitude compared to conventional silicon waveguides. The design of structure is discussed in detail. We also further elucidate the relationship between graphene's intrinsic properties and the device's nonlinear response. To validate the performance, we demonstrate all-optical multimode hexadecimal operations at 2.56 Tb/s with 16-PSK signals enabled by efficient four-wave mixing (FWM). This design is expected to contribute to the advancement of ultrafast on-chip optical communication networks.

Flexible pressure sensors have become pivotal in the advancement of wearable electronics and underwater monitoring, particularly when augmented by artificial intelligence. Nevertheless, the development of a unified sensing platform capable of seamless operation in both health monitoring and underwater communication remains a significant challenge. To address this issue, a highly sensitive flexible iontronic pressure sensor featuring a micro-pyramidal architecture was developed. The device is fabricated using molding involving a bespoke composite ink comprising carbon nanotubes (CNTs) and ionic liquid as the sensing layer. This layer is then sandwiched between screen-printed silver electrodes. The sensor demonstrated exceptional performance metrics, including high sensitivity (370 kPa−1), rapid response and recovery times (20 ms), and outstanding reliability (about 20000 cycles). In the domain of wearable health monitoring, the sensor demonstrated its capacity to discern faint human pulse signals, facilitating the acquisition of high-fidelity pulse waveforms. Concurrently, within the domain of underwater intelligent communication, the sensor was used to detect Morse code signals, which were then accurately classified by a deep learning algorithm. This work not only validates the sensor’s high performance but also demonstrates its dual functionality, seamlessly connecting human healthcare with intelligent underwater interaction and significantly broadening the scope of flexible sensing applications.