Reliable underwater tactile sensing technology can promote the effective exploitation of marine resources, which is of significant importance for sustainable human development. Such technology typically requires a combination of waterproofing, intelligence, and high efficiency. Herein, we leverage the in-sensor computing (ISC) neuromorphic device architecture as an innovative platform to construct a diamond nitrogen-vacancy (NV) center-based wireless magnetic tactile sensor (ISC-NVTS). This sensor achieves highly linear force-to-magnetic signal conversion through an array of NdFeB magnetized flexible films and accomplishes ultra-fast magnetic signal detection using diamond NV center magnetic sensing unit arrays operating in a fixed-frequency mode. This wireless connection approach effectively solves the waterproofing issue for tactile electronic devices. Furthermore, we enable adjustable pressure responsivity of the sensing unit array through a microwave multi-parameter NV center electron spin resonance control method, endowing it with the advanced intelligence to execute ISC architecture-based artificial neural network algorithms. Finally, the ISC-NVTS achieved a recognition rate of 95.1% (Random noise 0.1, offline simulation) in a classification task involving five types of marine debris and organisms, with a recognition time of only 6.45 ms. We anticipate that this work will advance the further development of tactile sensors and provide support for the sustainable exploitation and utilization of marine resources.

A fully closed-loop system model for the multi-curved surface fusion metal resonant gyroscope (MSFMRG) under force-rebalance mode is proposed, enabling accurate identification of its output errors. First, the load equation (incorporating inertial and piezoelectric driving forces) is established; combined with the vibration differential equations of the multi-curved surface fusion resonator, it is used to derive the drive loop's output models and analyze how each parameter affects the amplitude voltage. Then, the detection piezoelectric signal is modeled and analyzed, the detection closed-loop control system is established, and the mathematical model of the detection loop is built on this basis. The drive loop model and the detection loop model constitute the MSFMRG fully closed-loop system model. Finally, the amplitude signal as well as the output signal of the MSFMRG are tested. Comparative analysis of experimental and simulation results confirms that the model can accurately identify MSFMRG output errors caused by resonant cavity deformation, detection errors, and quality factor in-homogeneity, which can provide assistance for the subsequent error compensation of the MSFMRG, and thus improve the accuracy of the MSFMRG.

Although WSe2 has shown great potential for NO2 sensing, its development remains challenging because conventional hydrothermal synthesis often relies on hazardous reducing agents such as NaBH4 and N2H4⋅H2O, which may cause rapid H2 release, toxicity, and environmentally harmful byproducts. In addition, the sensing performance of WSe2 is often limited by insufficient active sites and inefficient charge transfer. Herein, flower-like WSe2 nanostructures were synthesized via a one-step hydrothermal route using benign, biodegradable, eco-friendly, and cost-effective C6H8O6 as the reducing agent. The obtained WSe2 was vacuum-sintered at the optimized temperature of 450 ∘C and further decorated with Pd nanoparticles to enhance NO2 sensing performance. The optimized 3% Pd-WSe2-450 ∘C sensor exhibited a response (Ra/Rg) of 2.26 toward 16 ppm NO2 at an operating temperature of 114 ∘C, with response and recovery times of 20 and 190 s, respectively. The enhanced sensing performance is attributed to the synergistic effects of Pd decoration, improved crystallinity, abundant surface oxygen species, and efficient interfacial charge modulation. This work demonstrates a Pd-modified WSe2-based NO2 gas sensor, where green synthesis, vacuum sintering, and interfacial Pd engineering synergistically enhance the sensing performance.

Two-dimensional imaging of microwave electric field gradients was performed using a MEMS cesium vapor cell based on Rydberg atoms. The variations in probe laser fluorescence intensity, continuously monitored via high-sensitivity CCD imaging at 24.038 GHz, enabled detailed analysis of electromagnetically induced transparency (EIT) and Autler-Townes (AT) splitting effects. By establishing the quantitative relation between the splitting interval (Δf) and the electric field intensity, spatial electric field gradient distributions were accurately measured. The experimental results demonstrated a minimum detectable electric field strength of 1.22 V/m and a spatial resolution of 151.2 µm, with the measurement error in electric field strength maintained below 2%.

Real-time detection of hydrogen sulfide (H2S) and ammonia (NH3) is important for occupational safety in humid, mixed-gas environments, yet conventional chemiresistive sensors remain limited by cross-sensitivity, humidity dependence, and poor portability at room temperature. Here, we report a flexible dual-channel sensor array coupled with a gated recurrent unit-convolutional neural network (GRU-CNN). A polythiophene/Cu-MOF-derived CuO composite (PTh/CuO) targets H2S through sulfur-affinitive CuO surface sites, whereas a polyaniline/Ti3C2Tx MXene composite (PANI/MXene) targets NH3 through PANI deprotonation and interfacial charge modulation. At 25 ± 1 °C, the optimized PTh/CuO and PANI/MXene channels exhibited responses of 87.5% to 100 ppm H2S and 189% to 100 ppm NH3, respectively, together with linear responses over 1-10 ppm (R2 = 0.997 and 0.998) and theoretical limits of detection of 73.3 and 40.4 ppb. Distinct responses were retained at 300 ppb H2S and 200 ppb NH3, with response/recovery times of 15/22 and 25/43 s, respectively. A GRU-CNN model trained using dynamic four-channel signals supported gas classification and concentration estimation across 0-10 ppm and 30%-80% relative humidity within the tested dataset; the maximum concentration-prediction mean squared errors were 0.020 for H2S and 0.015 for NH3. The channels retained 98.4% and 98.1% of their initial responses under 30° bending. Integration with a wireless wristband enabled remote signal acquisition and a wearable monitoring demonstration. This work combines chemically differentiated sensing channels, humidity-inclusive data analysis, and flexible wireless readout within a single room-temperature platform.

Conventional radar antennas are constrained by phase noise and physical aperture size, which limits further improvement in velocity detection resolution. To address these challenges, this study demonstrates a high-precision velocity detection method based on Rydberg atoms. By constructing a quantum precision measurement system utilizing Rydberg atoms as microwave mixers, high-accuracy frequency measurement of automotive radar-band microwave signals is achieved. Through optimization of signal acquisition time, the frequency resolution is enhanced to the millihertz (mHz) level. Subsequently, based on a frequency-velocity conversion model, precise detection within a velocity range from static conditions to 120 cm/s is realized, achieving a resolution of 62.4 mu m/s. Furthermore, by measuring the electric field sensitivity of the system (516 nV & sdot;cm-1 & sdot;Hz-1/2), the maximum detectable distance is theoretically calculated to be approximately 2000 m, which far exceeds the detection range requirements for long-range radar in the automotive radar industry. Finally, leveraging the atomic system's bandwidth, the upper limit of measurable velocity can be extended to 310 m/s. This research establishes a critical technical foundation for high-precision velocity detection based on atomic quantum systems, with broad application prospects and significant socio-economic value in fields such as inertial navigation, precision manufacturing, and aerospace.

With the increasing demand for industrial gas detection, methods based on deep learning (DL) are becoming increasingly important for gas recognition. However, sensor drift is inevitable, making recognition models trained on the source domain difficult to adapt to the target domain, thereby reducing gas recognition performance. Therefore, this article proposes a domain generalization (DG) network with inverse Gaussian weighted contrastive learning (WCDG) to achieve gas recognition under sensor drift. The network decouples multidomain features to separate recognition-related and domain-specific features, employs saliency-guided feature fusion to extract domain-invariant features, and applies weighted contrastive constraint to reduce intraclass divergence and enlarge interclass margins, thereby enabling effective transfer of source domain knowledge derived solely from multiple batches of early stage data to a target domain that contains mid-stage and late-stage data. Specifically, a multiscale feature extraction (MSFE) module is first proposed to capture the latent discriminative features of the input samples. Second, adversarial training is incorporated to achieve decoupling between recognition-related and domain-specific features. Subsequently, a saliency-guided feature fusion (SGFF) module is proposed to further extract domain-invariant features without drift. Finally, an inverse Gaussian weighted contrastive learning (IGWC) strategy is proposed to optimize the feature space and enhance discriminability. Experimental results demonstrate that the proposed method achieves average recognition accuracies of 91.80% and 94.00% on a public dataset and a laboratory-collected dataset, respectively, significantly outperforming the compared methods, thereby confirming its superiority under sensor drift and further indicating strong potential for practical applications.

This work demonstrates the superior performance of silica microsphere cavities fabricated from coreless fiber (CLF) for optical sensing applications. The optical resonances within CLF microspheres, excited through total internal reflection-induced whispering gallery modes, enable detectable spectral shifts in response to refractive index variations caused by nanoparticle binding. Compared to conventional microspheres using single-mode fibers (SMFs), CLF microspheres exhibit significantly higher quality factors (Q > 10(8)) and enhanced sensitivity of resonance wavelength shifts for nanoparticle detection in aqueous environments. The experimental results reveal that the exceptional material homogeneity and sub-nanometer surface roughness (Ra < 0.5 nm) of CLF microspheres consistently sustain Q-factors exceeding 10(8). In underwater nanoparticle adsorption experiments, CLF cavities demonstrate more than 1.25 times higher resonance wavelength shift sensitivity than their SMF counterparts. Finite-difference time-domain simulations confirm that the order-of-magnitude improvement in Q-factor constitutes the fundamental mechanism for sensitivity enhancement. These findings establish a novel platform for high-precision underwater biosensing and environmental monitoring.

To address the limitations of conventional discrete ammonia-monitoring schemes, this study proposes a closed-loop sensing–inference architecture that integrates a distributed chemical-sensing network with a physics-informed neural network (PINN) to achieve high-fidelity three-dimensional dynamic reconstruction of gas diffusion in confined environments. A distributed sensor array based on a PPy/Graphene/WO3 ternary nanocomposite provides real-time wireless monitoring and reliable observational data streams owing to its sub-ppm detection limit and high signal-to-noise ratio. Fick’s second law of diffusion and Neumann no-flux boundary conditions are embedded in a mesh-free PINN, and rolling horizon data assimilation (RHDA) is introduced to dynamically fine-tune the network weights with high-frequency real-time observations, thereby establishing a real-time closed-loop correction between theoretical inference and the monitored environment. The proposed method achieves accurate three-dimensional concentration-field reconstruction under steady-state conditions, markedly suppresses errors and maintains robustness under unknown abrupt concentration disturbances, and mitigates the temporal divergence of conventional data-driven models during long-term purely physical extrapolation without data support. Thus, the framework combines hardware sensitivity, computational efficiency, and macroscopic physical generalization.

Ultrasonic wind speed measurements performed in complex flow fields face challenges related to low signal-to-noise ratio (SNR) and non-stationary waveform distortion. In this study, we aim to address this issue by proposing a measurement system that employs a polyvinylidene fluoride (PVDF) piezoelectric film ultrasonic transducer integrated with a microphone (MIC). In addition, a signal processing framework is proposed based on the joint optimization of variational mode decomposition (VMD) and an extended Kalman filter (EKF) and integrating cross-correlation interpolation. By leveraging the low Q-factor and wide bandwidth characteristics of the PVDF, the system achieved omnidirectional transmission and high-fidelity reception within a compact structural design. The experimental results demonstrated that the proposed VMD-reference signal-assisted EKF method enhanced the SNR by approximately 26% and reduced the wind speed measurement error by approximately 35% compared with the conventional EKF. The proposed system exhibited superior robustness and measurement linearity across a wide wind speed range of 0-60 m/s. The proposed scheme significantly enhances the accuracy and environmental adaptability of ultrasonic wind speed measurements and provides an essential theoretical basis and engineering reference for the development of precision instruments in fields such as meteorological monitoring and wind energy assessment.

The integration of surface-enhanced Raman scattering (SERS) with microfluidic technology has enabled significant progress in the detection of trace biomarkers; however, its practical application remains constrained by two key challenges: insufficient enrichment of target analytes and poor signal reproducibility, which compromise detection reliability. To address these limitations, we developed an integrated optofluidic SERS platform incorporating probe DNA (pDNA)-functionalized magnetic microspheres and a silver nanowire (AgNWs) substrate immobilized on a microfluidic chip. The electromagnetic coupling between the magnetic microspheres and the AgNWs array was theoretically validated through electromagnetic field simulations, achieving secondary amplification of the Raman signal. Computational fluid dynamics further confirmed that the serpentine microchannel facilitates uniform mixing of viral genes with the magnetic particles. The platform was applied to the SERS-based detection of SARS-CoV-2 target DNA (tDNA), achieving a limit of detection below 1 pM and an enhancement factor (EF) of 1.33 & times; 108, while exhibiting excellent discrimination against non-target sequences. This study presents a novel SERS-microfluidic strategy that synergistically integrates magnetic capture with interfacial electromagnetic coupling to achieve dual enhancement of signal stability and detection sensitivity, offering a promising approach for viral and microbial diagnostics.

Microwave field detection and recognition technology, leveraging its safety features such as noncontact and nonradiation characteristics, has achieved widespread application in medical diagnosis, industrial nondestructive testing, and public security screening. However, high latency from antennas, multiple analog-to-digital conversions, and the serial operation mode of sensing/storage/computation result in significant system delays, preventing the capture and recognition of highly dynamic microwave information and leading to low operational efficiency. Here, we construct an NV color-center in-sensor computing microwave sensor (NV-ISCMS) using a diamond array. Leveraging the high linear correlation between the electron spin resonance intensity of NV color centers and microwave power, ultrafast detection of microwave fields is achieved. Controllable adjustment of the device operating frequency is realized based on the Zeeman splitting resonance frequency shift effect. Each diamond, regulated by a positive-negative voltage follower circuit, serves as a tunable responsivity NV color-center microwave sensing unit. By implementing an in-sensor computing architecture that performs matrix multiplication of microwave field intensity/responsivity and Kirchhoff's law current summation, parallel microwave sensing and data processing are achieved without introducing any analog-to-digital conversion processes. Experimental verification shows that the device's single microwave field target detection and recognition time is only 153.2 ns, providing a reliable technical solution for achieving fast response, low power consumption, and reduced hardware overhead in intelligent microwave field detection and recognition technology.

Beam-island structure accelerometers are prone to damage under high overload with varying amplitudes and durations. To predict this damage, this study establishes a pressure-impulse equivalent damage model based on a novel cumulative energy criterion. The dynamic response is modeled analytically using unconventional Hamilton variational principles. This model derives a cumulative energy Pressure-Impulse (P-I) equivalent damage model, revealing a negative correlation between pressure and pulse. The finite element simulation is used to validate the model, and the theoretical and simulated P·I value show that the maximum error is 3.18%. The free Hopkinson pressure bar impact tests indicates that the critical P·I value of the tested structure can be determined with 95% confidence to lie within the range [5.03 × 106, 5.19 × 106] Pa2·s. This study not only quantifies the relation between impact parameters and structural failure effectively, but also provides a theoretical foundation for optimizing the shock resistance design of accelerometers, selecting their installation locations, and improving their reliability under high overload conditions.

Tungsten oxide (WO3) is widely used for detecting harmful gases, such as NO2, because of its excellent gas-sensing properties. However, pure WO3 sensors suffer from high operating temperatures, poor selectivity, and rapid sensitivity degradation. In this study, for the first time, Bi2O2Se crystals were synthesized via a high-temperature solid-state reaction under a vacuum, and WO3/Bi2O2Se heterojunction composites were fabricated using a one-step hydrothermal method to enhance the NO2 sensing performance. By adjusting the Bi2O2Se content, the optimal performance was achieved at 15 wt% Bi2O2Se (WB-15), which showed a high response (Rg/Ra = 3.42) toward 50 ppm NO2 at 190 degrees C, a low detection limit (100 ppb), rapid response/recovery times (83/91 s), excellent repeatability (S = 2.42%), and good long-term stability. Characterization results showed that acidic etching (pH 1.7) increased the specific surface area and the number of active sites of Bi2O2Se, enhancing its gas adsorption capacity. Analysis of gas-sensing mechanism revealed that the n-n heterojunction between WO3 and Bi2O2Se effectively modulated the interface barrier and carrier migration, intensifying the electron depletion effect under NO2 and significantly improving the sensing response. This study introduced Bi2O2Se as a novel sensitizing material, offering a new approach for designing high-performance heterojunction gas sensors with enhanced sensitivity and selectivity.

Polyaniline (PANI) is an important conductive-polymer gas-sensing material with working temperature and mechanical flexibilities superior to those of conventional metal oxide sensing materials. However, its applicability is limited by its low sensitivity, high detection limits, and long response/recovery times. In this study, we prepared PANI/WS2 composites via chemical oxidative polymerization and mechanical blending. A multilayer sensor structure—sequentially printed silver-paste heating electrodes, fluorene polyester insulating layer, silver interdigitated electrodes, and sensing material layer—was fabricated on a polyimide substrate via flexible microelectronic printing and systematically characterized using scanning electron microscopy, X-ray diffraction, and Fourier-transform infrared spectroscopy. The optimized 5 wt% WS2 composite showed enhanced gas-sensing performance, with 219.1% sensitivity to 100 ppm ammonia (2.4-fold higher than that of pure PANI) and reduced response and recovery times of 24 and 91 s, respectively (compared to 81 and 436 s for pure PANI, respectively). Notably, the PANI/WS2 sensor detected an ultralow ammonia concentration (100 ppb) with 0.104% sensitivity. The structural characterization and performance analysis results were used to deduce a mechanism for the enhanced sensing capability. These findings highlight the application potential of PANI/WS2 composites in flexible gas sensors and provide fundamental insights for PANI-based sensing materials research.

In the critical applications of geological disaster prediction, aerospace, and other fields, the development of accelerometers is increasingly trending toward higher precision, larger dynamic range, and miniaturization. Although the existing MEMS or some optical devices already possess these advantages, the majority of devices reported typically require the difficulty and complex technology in the manufacturing process. Such as the creation of ultrathin cantilever beam structures to the pursuit of high sensitivity often results in devices are fragile and unreliable. This article presents a Fabry-P & eacute;rot (F-P) damped all-metal-elastic fiber-optic accelerometer that utilizes the optical interference detection method to convert the input acceleration into displacement. The all-metal hard friction design required careful consideration of friction-induced insensitivity. To mitigate this, high-precision laser cutting and polishing techniques were used to accurately process materials like brass. The results shown that the proposed accelerometer exhibits the overall noise floor of 21 ng/ root Hz at 92 Hz, with a measurement range of 8.4 mg and a dynamic range of 112 dB, which is the superior level for this frequency band in the existing damped accelerometers as far as we know, and it maintains a small overall volume (about 25 cm3). Compared with reported devices, the device proposed uses all-metal-elastic materials can greatly reduce the material and manufacturing cost and enhance the durability and reliability of the device, overcoming the limitations of traditional MEMS and optical devices. Therefore, the proposed accelerometer is expected to be used in practical applications, such as earthquake monitoring, navigation and other complex environments.

This study tackles the challenges of low sensitivity and slow response/recovery in polythiophene (PTh)‐based NO 2 sensors by integrating multi‐walled carbon nanotubes (MWCNTs) and tin metal–organic framework (Sn‐MOF)‐derived tin dioxide (SnO 2 ) nanoparticles, resulting in a PTh/MWCNT/SnO 2 ternary composite flexible gas sensor. The composite, prepared via chemical oxidative polymerization, hydrothermal synthesis, and mechanical blending, is fabricated into a multi‐layered sensing structure on a polyimide substrate using flexible electronics printing. Doped with 3% MWCNT and 6% SnO 2 , the sensor significantly outperforms pure PTh. At 150°C, it achieves a 76.8% response to 100 ppm NO 2 , with response/recovery times dramatically reduced to 23 and 75 s, respectively. Simultaneously, the sensor's response to a low concentration of 500 ppb NO 2 was 1.28%. The sensor exhibits excellent NO 2 selectivity (3.9 times higher than for C 3 H 6 O), good repeatability (less than 5% deviation over 8 cycles), and strong long‐term stability (82.4% initial response over 40 days). Mechanistic analysis indicates enhanced performance from a dual approach: p–n heterojunctions optimize interface depletion layer regulation and charge transfer, while the MWCNT's 3D conductive network accelerates electron transport and gas diffusion. This research offers a new avenue for high‐performance, flexible environmental monitoring.