Search Results (1,901)

Flexible pressure sensors serve as core sensing components for wearable health monitoring systems, electronic skins for soft robots, and flexible human–machine interaction devices. Benefiting from the interfacial electric double-layer polarization effect, iontronic sensing delivers far higher pressure response sensitivity than conventional parallel-plate capacitive sensors, endowing it with distinctive advantages in the detection of weak physiological signals. Nevertheless, current dense ionic thin-film dielectric layers suffer from limited deformation space under compression and poor low-pressure sensing capability. Mainstream high-precision micropillar arrays are fabricated via photolithography, 3D printing, and metal etching molds, which require costly equipment and complicated fabrication procedures, making large-area mass production unfeasible. Random frosted concave-convex microstructures feature disordered dimensions, leading to severe device hysteresis and narrow linear ranges, which fail to achieve ultrahigh sensitivity alongside a wide pressure detection range simultaneously. To address the aforementioned multiple bottlenecks, this paper proposes a low-cost resin template replication process to fabricate TPU-based ionic thin-film dielectric layers with ordered micropillar array microstructures. Combined with inkjet-printed silver conductive PI flexible electrodes, an iontronic flexible pressure sensor with a sandwich layered structure is constructed. Multi-dimensional investigations including microscopic morphology characterization, electromechanical sensing performance calibration, and human wearable application tests are systematically implemented to thoroughly elucidate the synergistic enhancement mechanism of the arrayed micropillars. Test results demonstrate that the effective pressure detection range of the sensor spans 0–1038 kPa, accommodating ultra-low pressures such as pulse signals as well as medium-to-high-pressure loads including joint bending. The sensitivity reaches 23.27 kPa⁻¹ within the low-pressure range of 0–200 kPa and remains stable at 3.52 kPa⁻¹ in the high-pressure range of 200–1038 kPa, with piecewise linear fitting correlation coefficients of 0.93 and 0.96 respectively. Both the response time and recovery time of the device are 40 ms, and the hysteresis error throughout the loading-unloading cycle is merely 2.62%. After 20,000 consecutive cyclic loading-unloading tests, the peak capacitance output only decays by 5.1%, verifying outstanding mechanical fatigue resistance and electrical stability. Validations in multi-scenario applications prove that the sensor can accurately capture human physiological and motion signals including radial artery pulses, laryngeal deformation induced by multi-syllable vocalization, and multi-angle bending of fingers and elbow joints, suitable for home-based health monitoring, quantitative rehabilitation training, flexible tactile interaction and other scenarios. The entire fabrication process eliminates high-precision micro-nano processing equipment such as photolithography systems, plasma etchers and 3D printers; only general chemical raw materials and conventional laboratory instruments are adopted. The reusable templates enable low manufacturing costs and large-area coating forming, offering a novel low-cost technical solution for the engineering implementation and industrialization of high-performance iontronic flexible pressure sensors. Full article

The present work presents a new synthetic jet piezoelectric pump designed to address the airflow delivery needs arising from the increasing power density of high-performance microelectronics. Traditional miniaturized cooling techniques suffer from low efficiency, bulky size, and high cost, while microfluidic cooling has emerged as a vital chip thermal management method with outstanding miniature heat removal capacity. We systematically designed the vibration mode and pump structure, adopting the sixth-order resonant frequency as the operating frequency. A dual resonant layer with stiffness-guided fixed boundaries was employed to enhance vibration efficiency and energy conversion, together with an optimized flow channel layout and parametric design. Experiments conducted under 35 V square-wave excitation demonstrate that the 20 mm × 20 mm × 2.5 mm pump delivers a flow rate of 1.6 L/min and a back pressure of 2.7 kPa. This work provides a feasible technical route for large-scale airflow delivery applications of synthetic jet piezoelectric pumps, with potential for thermal management in microelectronic devices, while balancing excellent performance and low manufacturing cost. Full article

Enhancing the proof mass without enlarging the chip area or introducing structural asymmetry is a central challenge in the development of low-noise microelectromechanical system (MEMS) accelerometers. Here, we present a symmetric triple-layer MOEMS accelerometer and analyze its device-level sensitivity trade-off, cross-axis coupling behavior, and dynamic consistency between measurement and finite-element simulations. The proposed sensing element sandwiches one without-beam mass layer between two identical with-beam layers, thereby increasing the effective proof mass while preserving mirror symmetry. A lumped-parameter model is developed to explain the sensitivity trade-off among single-layer, asymmetric double-layer, and symmetric triple-layer configurations. Finite-element simulations are used to distinguish translational cross-axis coupling from rotational cross-axis coupling. The experimental characterization of one packaged triple-layer prototype demonstrates a mechanical sensitivity of 193.91 µm/(m/s²), a 10 min output RMS fluctuation of 1.81 µg, and a measured first-order resonant frequency of 11.23 Hz, in close agreement with the tolerance-included finite-element prediction of 11.40 Hz. The resonance bandwidth further yields an apparent package-level quality factor of approximately 374 under ambient pressure, providing additional characterization of the packaged device dynamics. Full article

Smart chemical sensors have emerged as promising tools for real-time monitoring of food spoilage in both fermented and non-fermented products. By detecting key spoilage indicators—including biogenic amines, ammonia, hydrogen sulfide, methane, pH variations, and microbial volatile organic compounds (MVOCs)—these systems enable rapid, on-site assessment of food quality, offering a viable alternative to conventional, time-consuming laboratory analyses. Recent advances encompass diverse sensing mechanisms, including chemiresistive platforms based on conducting polymers and MEMS (Microelectromechanical Systems); optical/colorimetric systems using dyes, metal–organic frameworks, and porphyrins; and electrochemical and biosensing approaches employing enzymes, antibodies, aptamers, and whole-cell recognition elements. These sensors demonstrate high sensitivity (ppb–ppm range), enabling early detection of spoilage before sensory perception or microbiological threshold exceedance. Their applicability has been validated across a wide range of food matrices, including meat, fish, dairy products, vegetables, beverages, and fermented foods. Despite significant progress, key challenges persist, including signal drift, limited specificity, susceptibility to environmental factors such as humidity and temperature, and interference from complex food matrices. Furthermore, integration into intelligent packaging requires the development of flexible, food-safe, and regulatory-compliant materials. Emerging approaches that combine sensor arrays with machine learning and MVOC pattern recognition are enhancing predictive accuracy and enabling food classification across commodity types. Overall, smart chemical sensing technologies are rapidly transitioning from laboratory prototypes to practical applications in intelligent packaging and wireless monitoring systems, with ongoing research focused on improving robustness, standardization, and scalability for commercial deployment. This article provides an overview of the topic, drawing on the available bibliography from the last five years and the most-cited scientific databases. Full article

Microelectromechanical systems (MEMS) have a widespread use in several applications such as signal filtering, time referencing, and sensing. This paper explores the nonlinear dynamic behavior of a MEMS resonator using a reduced-order modeling approach. The study focuses on how multi-modal formulation and detuning affect the system’s response under primary resonance. Using the method of multiple scales, amplitude–phase response equations are derived, and time-domain simulations are generated with the Runge–Kutta method. Two mode combinations are examined: the first mode combined with the second mode and the first mode with the third mode for multi-modal influence evaluation. Results indicate that the first mode provides the dominant behavior to MEMS response, while the second and third modes exhibit minimal participation despite the nonlinearities retained in the presented multi-modal model. Additionally, a detuning study reveals that the geometric and forcing nonlinear effects are stronger near resonance and diminish as the system moves away from it. The analysis suggests that the significant features of the response can be captured in the case of primary resonance using only the first mode, which offers an effective modeling approach. From a design perspective, finding that the first mode alone is sufficient means that the essential dynamic behavior of the MEMS resonator can be predicted and controlled by focusing on its first mode of vibration. In practical terms, this greatly allows engineers to optimize geometry, driving voltage, or control parameters to target the first mode natural frequency without accounting for higher modes, which reduces computational cost and design complexity. Full article

The full operation of BDS-3 enables users to obtain high-performance positioning services, benefiting from the surge in the number of Global Navigation Satellite System (GNSS) observations with multi-constellation multi-frequency signals. This overabundance is beneficial to improve in-flight navigation accuracy for small unmanned aerial vehicles (UAVs). However, it brings about two-fold challenges for conventional airborne GNSS/SINS tightly coupled (TC) systems. On one hand, limited airborne computing resources suffer from the “curse of dimensionality” caused by extremely high-dimensional GNSS observations (i.e., GNSS pseudo-ranges, pseudo-range rates, and time-differenced carrier phases from multi-system and multi-frequency, such as GPS L1/L2 and BDS B1/B2/B3, totaling up to over 100 observables per epoch), leading to increased calculation burden and potential latency. On the other hand, possible outliers can degrade the obtained navigation accuracy. To enhance overall performance, this paper proposes a computationally efficient Kalman filtering framework for tight integration between airborne GNSS and SINS via a high-dimensional robust information filter. The strategy of kinematic and static information filtering is utilized to handle the matrix inversion complexity caused by high-rate and high-dimensional Kalman measurement updates, and the technique of robust adaptive factor is used to resist the adverse effects of GNSS outliers and modeling errors. Both land vehicular and UAV flight tests indicate that the proposed algorithm outperforms its traditional TC counterparts, demonstrating an over 90% improvement in overall computational efficiency without any loss in accuracy, compared with conventional batch or sequential tightly coupled Kalman filtering. Full article

Micro-Electro-Mechanical System (MEMS) gyroscopes serve as core components for attitude-sensing systems in low-speed unmanned vehicles and mobile robots. The long-term dynamic stability of MEMS gyroscopes under actual continuous low-speed vehicle operation is significantly inferior to the nominal performance derived from laboratory-based static calibration. This paper adopts a navigation-grade fiber optic gyroscope as the high-precision angular velocity reference. Angular velocity error sequences between the MEMS gyroscope and fiber optic gyroscope are established using field test data collected from a low-speed vehicle experiment lasting approximately 3.3 h. Three analytical approaches are applied to systematically characterize noise evolution features of the MEMS gyroscope under static and dynamic conditions from multiple dimensions. These approaches include time-domain drift analysis, angle random walk evaluation via Allan Variance, and frequency-domain interpretation based on Welch power spectral density. The test results reveal that vehicle motion significantly degrades the 10 s averaged bias stability of the MEMS gyroscope. The bias stability values under dynamic conditions increase by 2.1, 2.7 and 7.7 times compared with static states respectively. Root mean square analysis through band segmentation integration of Welch power spectral density indicates that vehicle motion induces the most obvious rise in noise energy in the middle frequency band. The amplification factor of noise along the Y axis reaches 24.7 times. The joint analytical framework proposed in this paper takes fiber optic gyroscope measurements as the reference and integrates time-domain analysis, Allan Variance and frequency-domain methods. It can provide sufficient experimental evidence and technical support for dynamic error compensation of MEMS gyroscopes deployed on low-speed mobile platforms. Full article

Microgears enable mechanical power transmission, speed reduction, motion conversion, and synchronization in compact devices ranging from microelectromechanical systems to miniature robots and optically driven micromachines. Their behavior cannot, however, be inferred by geometrically scaling conventional gears alone. As size decreases, relative manufacturing errors, surface forces, friction, adhesion, environmental sensitivity, and metrological uncertainty become increasingly important, while torque capacity and stored kinetic energy decrease rapidly. This critical review integrates the design, manufacture, tribology, and system-level performance of microgears and microgear trains. It first clarifies dimensional terminology and derives the principal scaling relationships. It then compares external, internal, planetary, worm, bevel, compliant, and reconfigurable transmission architectures; evaluates silicon micromachining, electroforming, micro powder injection molding, microforming, micro-electrical discharge machining, ultrashort-pulse laser ablation, and additive microfabrication; and examines adhesion, friction, wear, lubrication, and environmental effects. Particular attention is paid to transmission efficiency, starting torque, backlash, transmission error, lifetime, and the influence of the measuring instrument on the observed response. The literature remains strongly weighted toward manufacturability and isolated components, whereas reproducible, loaded, system-level tests are comparatively scarce. On this basis, the review proposes a unified hierarchy of validation, a minimum functional test matrix, and scale-aware design indicators. The central conclusion is that successful microgear transmissions require concurrent design of geometry, process, surface condition, environment, load path, and measurement strategy. Full article

Ultraviolet (UV) and near-infrared (NIR) dual-band photodetection is critical for applications ranging from secure optical communication and environmental monitoring to biomedical imaging. However, existing dual-band systems typically rely on discrete single-band detectors combined with optical filters, leading to complex alignment and high cost. Herein, we demonstrate a bipolar heterojunction (BHJ) based on a 2H-MoSe₂/Si/1T-WS₂ structure for filter-free, high-speed UV-NIR dual-band photodetection. The optimized device achieves high responsivities of 0.7 A/W@365 nm and 0.8 A/W@1064 nm at bias voltage of −2 V, along with a fast response time of 1.28 μs and a −3 dB bandwidth of 70 kHz. Furthermore, in comparative evaluations with broadband Si photodiodes, the BHJ successfully enabled high-quality NIR single-pixel imaging, reconstructing high-resolution images with 128 × 128 pixels under ambient lighting conditions. This work validates the potential of such transition metal dichalcogenides-Si heterojunctions for next-generation multispectral sensing and imaging systems. Full article

Early detection of faults in internal combustion engines (ICEs) remains an important challenge for improving operational reliability and supporting condition assessment. Conventional approaches based on On-Board Diagnostics II OBD-II data, vibration, or exhaust gas analysis face practical limitations, motivating the exploration of non-invasive acoustic diagnostics. This study proposes and validates a pipeline that combines acoustic signals, Symmetrized Dot Pattern (SDP) transformations, and convolutional neural networks (CNNs) to identify injector and ignition failures in multicylinder ICEs. Acoustic measurements were acquired in a purpose-built semi-anechoic chamber using a smartphone equipped with Micro-Electro-Mechanical Systems (MEMS) microphones, ensuring high-signal-to-noise-ratio recordings under controlled fault conditions. A total of 540 independent acoustic recordings were transformed into 540 grayscale SDP images, which were used to train and test a custom CNN architecture. Results demonstrated an overall accuracy of 81.11% in independent verification, with outstanding performance in Spark Plug 3 and Spark Plug 4 (F1 = 1.000), as well as Injector 4 and normal operation (F1 = 0.947). More challenging cases included Injector 1, Injector 3, and Spark Plug 1 (F1 ≈ 0.70–0.78), where spectral overlap led to cross-misclassifications. These findings are consistent with recent studies reporting the robustness of SDP for representing transient acoustic phenomena and the superior classification capability of CNNs for pattern recognition tasks. The study extends SDP applications to internal combustion engines, demonstrating the feasibility of detecting injection and ignition faults using acoustic measurements. The proposed SDP–CNN pipeline represents a non-invasive, low-cost approach to acoustic fault analysis using consumer-grade sensing devices. Future research should focus on expanding datasets, incorporating domain adaptation techniques, and validating performance under real-world operating conditions to improve generalization and support practical acoustic monitoring applications. Full article

Microelectromechanical systems (MEMS) gyroscopes are now standard rate sensors in inertial navigation, automotive electronics, industrial automation, and medical instrumentation because they are inexpensive, compact, and readily integrated. Their accuracy, however, degrades with temperature: damping and quadrature coupling change, and readout-electronics behavior shifts, producing temperature-dependent zero-rate-output drift, elevated random noise, and poorer long-term stability. Hardware- and structure-based temperature compensation address part of the problem but carry cost and generality penalties, which has moved recent work toward data-driven software-based temperature-drift compensation. This review focuses on the fusion algorithms that have come to dominate that literature, organized as a four-stage pipeline: signal decomposition, learning-based drift modeling, adaptive filtering, and signal reconstruction. We examine how optimizer-tuned variational mode decomposition and improved empirical-mode-decomposition variants separate temperature-related components from noise; how deep temporal networks and optimizer-coupled learners model the nonlinear, time-lagged drift; and how adaptive Kalman variants and time-frequency filtering reconstruct a stable output. We close by identifying four open problems that recur across the recent gyroscope-specific work—cross-device generalization, temperature hysteresis, embedded real-time deployment, and physics-informed lightweight modeling. Full article

To address the challenge that microelectromechanical system (MEMS) gyroscope electrostatic force-modulated closed-loop self-excited driving circuits experience significant dynamic variations in capacitive load and driving demand under different operating conditions, such as start-up, steady-state resonance maintenance, and environmental perturbations, making it difficult to simultaneously achieve strong driving capability, stable oscillation, and low power consumption, this paper proposes a high-energy-efficiency adaptive output operational amplifier architecture. Based on a dynamic load-sensing mechanism, the design introduces a three-threshold decision scheme combining a high threshold, a low threshold, and a mid-supply reference voltage. By coordinating a continuous-time voltage detection circuit with a bidirectional shift register, the proposed approach enables accurate identification of the output state and the load level. A time-division-multiplexed two-stage control strategy is adopted to rapidly compensate for the drive capability under abrupt load changes, while proactively disabling redundant output units under steady-state conditions, thereby achieving power delivery on demand. The output stage employs a Class-AB push–pull structure integrating an improved low-leakage single-pole double-throw (SPDT) switch, which hard shuts off the power transistors in the non-operating state to effectively eliminate the subthreshold leakage current. Circuit simulations in a 0.18 μm CMOS process demonstrate that the proposed operational amplifier can adaptively regulate its output current in real time according to variations in the gyroscope driving demand, ensuring sufficient an electrostatic driving force and oscillation stability during transient conditions while significantly reducing static power consumption during the resonance steady state. The proposed design provides an effective solution for high-performance and high-energy-efficiency interface circuit design in MEMS gyroscope electrostatic force-modulated closed-loop self-excited driving systems. Full article

This paper investigates the precision and stochastic error propagation of navigation solutions obtained from a low-cost microelectromechanical system inertial measurement unit (MEMS-IMU) under static conditions. A modern smartphone equipped with an MEMS-IMU was rigidly mounted at a calibrated fixed location to establish a zero-reference scenario, and inertial measurements were collected while the device remained stationary. The dataset was divided into 75 non-overlapping segments, each comprising 30 s of data sampled at 10 Hz, to enable statistically robust analysis. For each segment, velocity and position, which are theoretically zero under static conditions, were computed using strapdown inertial mechanization. A comprehensive statistical framework was then applied to characterize the stochastic behavior of both the raw inertial measurements and the derived navigation states. The methodology first assessed data normality, stationarity using the Augmented Dickey–Fuller (ADF) test, and variance homogeneity using Bartlett’s test. Subsequently, ARIMA models were identified and validated using the Ljung–Box (LB) test, while power spectral density (PSD) analysis provided complementary frequency-domain characterization. In addition, a multivariate, non-negative least squares variance component estimation (NNLS-VCE) method was employed to jointly estimate the variance components of multiple navigation state variables. The results demonstrate that the accelerometer and gyroscope measurements along all three axes are well characterized as stationary white-noise processes, with standard deviations in the order of ${10}^{-2} \mathrm{m}/{\mathrm{s}}^2$ and ${10}^{-4} \mathrm{rad}/\mathrm{s}$, respectively. The estimated velocity random walk (VRW) coefficients are $\left[0.197,0.201,0.160\right] \mathrm{m}/\mathrm{s}/\sqrt{\mathrm{h}}$, while the corresponding angular random walk (ARW) coefficients are $\left[0.009,0.012,0.008\right] \mathrm{rad}/\sqrt{\mathrm{h}}$. In contrast, the derived velocity and position estimates exhibit random walk behavior caused by error accumulation in the inertial mechanization process and are best represented by ARIMA$(0,1,0)$ and ARIMA$(0,2,0)$ models, respectively, consistent with the corresponding Allan variance analysis. After 30 s of static navigation, the average standard deviations of the ENU velocity estimates are ${\sigma }_v=[0.77,0.44,0.29] \mathrm{m}/\mathrm{s}$, while the corresponding position standard deviations are ${\sigma }_p=[1.30,0.69,0.46] \mathrm{m}$. The proposed framework provides a comprehensive approach for the stochastic modeling, precision assessment, and error characterization of low-cost MEMS-IMU navigation systems. Full article

Global navigation satellite system (GNSS) has the characteristics of high-precision positioning, which makes it an essential part of mobile terminal positioning. In urban environments, satellite signals are easily blocked and reflected, which affects the positioning results. In this case, inertial sensors manufactured by Micro Electromechanical Systems (MEMS) technology become the key to achieving continuous positioning. Although the integration of a micro inertial system (MINS) and GNSS provides the continuous output of position information, the position error will increase with time. This paper proposes a prediction model based on an adaptive neuro-fuzzy inference system (ANFIS) and a method to obtain model parameters. The model takes the position error $\delta P^b$ of the carrier system as the output, and the rejection time (the time when GNSS positioning information remains unavailable), accelerometer data, and gyroscope data as the model inputs. Aiming at the model parameters, the consequent parameters acquisition method based on the least squares method and the antecedent parameters acquisition method based on a genetic algorithm are proposed. When GNSS outages last for 60 s, the maximum value of horizontal positioning error is reduced by 80% in six outage sections. Therefore, the proposed method in this paper is a potential method to predict the positioning error of a MINS/GNSS integrated navigation system. Full article

The potential to precisely control both the linear and nonlinear index of refraction through optical manipulation of the atomic states has recently pushed warm alkali vapors to the forefront of research in the field of quantum sensors, quantum memories, and quantum fluids of light. Rubidium (Rb) vapor in centimeter-scale glass cells or millimeter-scale micro-electro-mechanical system (MEMS) cells has proven to be a very promising platform for these applications, yet only a handful of research works have been dedicated to the investigation of the (non)linear refractive index of Rb vapor. We present results of theoretical calculations of the (non)linear refractive index of warm Rb vapor, based on the optical Bloch equations for 6-level Rb atoms interacting with a probe laser. They are compared to the experimental results obtained using an interferometric technique, showing very good quantitative agreement. A Kerr nonlinear refractive index $n_2$ of up to $-1.2\times {10}^{-4}$ cm²/W is obtained. Python scripts for all theoretical calculations presented in this work are provided, including the refractive index calculation, that can readily be used in practical implementations for simulating the (non)linear refractive index of Rb vapor including the effects of Doppler broadening, transit time broadening, pressure broadening, saturation, optical pumping, and spin-exchange collisions. Full article