Abstract The precise measurement of skin friction on the surfaces of high-speed vehicle models is fundamental for refining aerodynamic designs and developing effective drag-reduction strategies. However, the compact size of conical models and the complex nature of the surface flows present significant challenges, making it absolutely essential to pursue research into high-precision skin friction measurement that can conformally adapt to the model's surface contours.This paper introduces the design of a capacitive MEMS skin friction sensor with a conformal measurement head that matches the surface of a conical model with a 20° half-angle. A key innovation lies in using the same fabrication process for both the conical model and the MEMS sensor, enabling an integrated, co-fabricated package where the sensitive element and its housing are seamlessly merged. This approach successfully tackles the long-standing issue of standard MEMS sensor heads failing to conform to curved model surfaces.Moreover, to cut down on assembly errors that could disturb the flow over the conical surface, we've fine-tuned the sensor's assembly technique. This optimization ensures that the step height between the sensor's active area and the surrounding wall is kept under 10 micrometers-a negligible value compared to the several-hundred-micrometer-thick boundary layer, meaning minimal interference with the wall-bounded flow.Following static calibration, the MEMS skin friction sensor performed excellently across a 0-100 Pa range, boasting a repeatability better than 1‰, linearity under 0.5%, and a resolution of 0.2 Pa. The sensor was then put to the test in a wind tunnel under Mach 8 conditions. The results confirmed that our conformal MEMS skin friction sensor successfully combines seamless surface integration, miniaturization, and high sensitivity. Measurements from multiple sensors showed a consistency deviation of 3.37%, while the repeatability for a single point was 5.33%.

Abstract The characteristic of our hearing is essentially based on the mechanics in our inner ear. Around 3000 hair cells in the cochlea decode vibrations into electrical signals, covering frequencies from 0.020-20 kHz with relative resolutions normalized by their natural frequency of 0.1-0.4% and a high dynamic range of 0-120 dB. These dynamic properties can be described by critical oscillators as they provide high resolution and nonlinear response near their critical points. However, the wide frequency range cannot be achieved as high sensitivity requires high Q-factors and is therefore associated with narrow frequency range. To overcome this, frequency tunability could be used to increase the detectable frequency range while maintaining high sensitivity. One solution to achieve frequency tuning is the mutual coupling of oscillators. To this end, a bio-inspired sensing system based on coupled resonators tuned near their critical points is presented, whose frequency can be tuned by varying the feedback of the individual resonator. In the coupled system three Andronov-Hopf bifurcations are identified, where two of them enable frequency tunability. We show that this adaptability of the frequency enables the coverage of a wide frequency range with limited number of resonators and yet preserves a high resolution with low number of resonators, which make them suitable for hardware implementation.

Gas sensors based on metal oxide semiconductors (MOS) have attracted significant attention in monitoring of methane emission and leakage monitoring due to their high sensitivity, fast response time, simple structure and low cost. However, the high power consumption caused by long-term high-temperature operation of MOS sensors restricts their application in mobile and portable devices. In this study, MOF-derived Co3O4 dodecahedrons for low-concentration methane detection at room temperature was prepared using Zeolitic Imidazolate Framework-67 (ZIF-67) as a template and with various calcination temperatures. Among them, the Co3O4-350 calcined at 350 °C exhibited the optimal CH4 sensing performance at room temperature, with a response of Rg/Ra = 1.53 to 2000 ppm CH4. This enhanced gas sensing performance is attributed to the highest Co3+ proportions and the largest specific surface area in Co3O4-350 nanomaterials, which provided more active sites for gas adsorption and reaction. To address the challenge of slow response speed and irrecoverability during CH4 detection at room temperature, the Co3O4 nanomaterials were printed onto a micro-heater plate (MHP) to form a MEMS gas sensor. By introducing a pulse heating mode to the MEMS sensor, the response and recovery time were significantly reduced to 26 s and 21 s, respectively. This enhancement improves both the efficiency and reliability of the MEMS gas sensor for early-stage detection of CH4 leaks in various industrial applications.

Piezoresistive pressure sensors are critical in aerospace, biomedical and industrial applications where high sensitivity and reliability are essential. This study presents a simulation-based performance analysis of MEMS piezoresistive pressure sensors using COMSOL Multiphysics 6.1. Four materials—Aluminum Gallium Arsenide (AlGaAs), Germanium (Ge), Silicon (Si) and Titanium Germanium Carbide (Ti₃GeC₂)—were evaluated under identical sensor architectures. The design features a square diaphragm with an X-shaped piezoresistor layout to intensify stress concentration and boost sensitivity. Simulations were conducted under pressure load of 100 kPa. Key performance indicators—diaphragm displacement, shear and von Mises stress and sensitivity (V/Pa)—were analyzed. Ti₃GeC₂ exhibited the highest sensitivity (5.08×10⁻⁶ V/Pa), outperforming Si by 49% and showed superior mechanical resilience with the lowest deflection. These results highlight Ti₃GeC₂ as a promising candidate for next-generation MEMS sensors operating in harsh environments. The model is built on fabrication-relevant parameters and provides predictive insight into material selection for high-performance piezoresistive sensors. Bangladesh Journal of Physics, Vol. 32, Issue 2, pp. 29 – 41, December 2025

Abstract Thermal stress generally exists in wafer-level silicon on glass (SOG) bonding devices, which significantly affects the stability of the devices. And the thermal stress mainly originated from temperature-dependent Young’s modulus of silicon and mismatched coefficient of thermal expansion (CTE) between silicon and glass. This paper presents a novel SD-2 borosilicate glass-based wafer-level SOG fabrication process for reducing thermal stress, in which the SD-2 glass exhibits closer CTE alignment with silicon substrate than conventional Borofloat 33 (BF33) glass. Then the SOG bonding parameters between the SD-2 and silicon substrate are characterized by theoretical analysis and experimental validation. And through well-designed contrast experiments, the thermal stress from the mismatched CTE is isolated from the temperature-dependent Young’s modulus. The measured results demonstrate convincingly that the SD-2-based device exhibits 36.73% lower CTE-induced thermal stress compared to the conventional BF33-based counterpart. The presented fabrication process can be used to fabricate temperature-susceptible MEMS sensor, such as accelerometer and inertia switch.

Highly tunable airflow velocity MEMS sensor based on resonant frequency monitoring

Auditory perception and localization are fundamental tasks for many species, allowing them to detect, identify, and spatially localize sound sources in their environment. While biological systems have evolved sophisticated neural mechanisms for auditory adaptation, artificial auditory systems still struggle to match their performance, particularly in dynamic and noisy environments. Our research focuses on whether sensor adaptation, driven by efferent feedback from the processing stage to the sensory stage, can improve localization performance. Inspired by human sound source localization based on interaural level differences (ILD) and efferent feedback, the proposed neuromorphic system architecture is composed of two bio-inspired acoustic sensors connected to a neural processing stage, represented by two neurons of the medial nucleus of the trapezoid body (MNTB) and two neurons of the lateral superior olive (LSO). The LSO neuron response was analyzed in the following ways: (i) using measured sensor responses at different ILD without efferent feedback and with a fixed local feedback for each sensor measurement; (ii) simulated with synthetically generated sounds with varying ILDs for four different feedback configurations from the LSO neuron to the acoustic sensors. Results from (i) showed how the feedback tuning can be used to overcome mismatches due to fabrication tolerances between different MEMS sensors, and (ii) showed the influence of different feedback configurations and simulation parameters on the LSO neuron response with respect to different ILDs.

Hand Gesture Controlled Smart Robot Using Arduino and MEMS Sensors

Abstract Background The reliable earthquake magnitude estimation is a critical component of earthquake early warning (EEW) systems. Conventional P-wave–based amplitude parameters, such as peak vertical displacement ( P d ), are widely used but often suffer from saturation and instability, particularly for larger earthquakes and when low-cost MEMS sensors are employed. The cumulative absolute absement (CAA), a time-integrated displacement parameter, has recently emerged as a promising alternative for improving early magnitude estimation. Methods This study analyzes strong-motion records from the dense P-Alert low-cost MEMS sensor network in Taiwan to evaluate the performance of CAA for earthquake magnitude estimation. CAA values were computed using P-wave windows ranging from 1 to 5 s after P-wave arrival, using stations within a hypocentral distance of 70 km as well as the nearest six stations. Empirical regression relations were developed to estimate magnitude from CAA and P d , and the resulting magnitudes were compared with the catalog moment magnitude ( M w ). A generalized moment magnitude ( M wg ) was additionally used to assess magnitude-scale consistency and bias in small to moderate earthquakes. Results The standard deviation between CAA-derived magnitude ( M caa ) and M w decreases systematically with increasing window length, from ±0.383 for a 3 s window to ±0.333 for a 5 s window when using all stations within 70 km. In contrast, P d -derived magnitudes ( M pd ) show larger deviations, reducing from ±0.504 (3 s) to ±0.398 (5 s). Reliable magnitude estimates are also achieved using only the nearest six stations, with standard deviations of ±0.341 (CAA) and ±0.460 ( P d ) for the 5 s window. CAA exhibits a stable scaling with earthquake magnitude, while P d tends to stagnate and underestimate events approaching M w 7.0. Magnitude scale consistency tests using M wg confirm the robustness of the proposed CAA relations after correcting for M w bias. Conclusions The results demonstrate that CAA provides a more stable and reliable early magnitude estimator than P d , particularly for low-cost MEMS sensor networks and limited station availability. The reduced dispersion, lower saturation tendency, and robustness across different window lengths highlight the strong potential of CAA for operational on-site EEW systems using dense, cost-effective seismic networks.

Abstract: This project focuses on the development of an intelligent IoT-based accident detection and alerting system enhanced with machine learning techniques. The proposed system is designed for vehicle safety by continuously monitoring the driver’s physical condition, vehicle movement, and surrounding environment to reduce the risk of road accidents. A Raspberry Pi acts as the central processing unit and is interfaced with various sensors such as a USB camera, heart rate sensor, alcohol sensor, MEMS sensor, GPS module, ultrasonic sensor, and GSM module. The system analyzes real-time data collected from these sensors to assess driver alertness, health status, and driving behavior. The camera tracks eye movements to identify signs of drowsiness or fatigue, while the heart rate sensor provides information about the driver’s physiological condition. The alcohol sensor detects the presence of alcohol in the driver’s breath and generates alerts if unsafe levels are detected. Accident detection is achieved using a MEMS sensor that identifies abrupt changes in acceleration or orientation, indicating a possible collision. Additionally, the ultrasonic sensor measures the distance to nearby vehicles to assist in collision avoidance.

Accurate sea level prediction is crucial for coastal communities facing rising sea levels and increasing storm-surge threats. However, the black-box nature of existing predictive models limits their acceptance in operational decision-making contexts, creating an urgent need for transparent, cost-effective, and reliable forecasting solutions. Here, we propose a novel framework that combines Micro-Electro-Mechanical System (MEMS)-based ocean sensors with an interpretable machine learning approach to address this need. Utilizing two months of data collected from a MEMS accelerometer array on submarine cables, our approach integrates Variational Modal Decomposition (VMD) to isolate key temporal patterns from raw sensor measurement data, and Principal Component Analysis (PCA) to optimize feature selection. These refined features with meteorological and calendar information are fed into an interpretable machine learning model, Temporal Fusion Transformer (TFT), for sea level prediction. The TFT provides interpretable outputs, including the importance ranking of input variables and attention distribution of different time steps. Our hybrid VMD-PCA-TFT model achieved a root mean square error of 1.57 cm and a coefficient of determination (R²) of 0.98 compared to tide gauge records, outperforming existing models. These results demonstrate that MEMS-based ocean measurement systems can match the accuracy of traditional methods at a fraction of the cost. Moreover, the TFT’s interpretable analysis reveals that the VMD-PCA features accounted for more than 80% predictive power, provide transparent insights into prediction mechanisms. This dual advantage of cost-effectiveness and interoperability of our framework could potentially promote widespread deployment of ocean monitoring systems and advance prediction capabilities for climate change adaptation.

For centuries, the relation between mind and body has intrigued philosophers and scientists. How body affects the mind, and mind affects the body? Since mid-19th century, scientists searched for the “mind” in the anatomy of brain, just as they explored the “body” with Da Vinci’s artistic renditions of muscles. Muscles are considered as contractile force actuators, and neurons as information processors. Here, we will discuss our findings, revealed by a novel MEMS sensor, that neurons are also contractile, both in vivo and in vitro. This contractility leads to tension in axons, a long cable-like structure that connects neurons with each other. We measure the magnitude and time evolution of tension in axons using specially designed MEMS force sensors. We then correlate the tension with neuronal function (firing pattern). We find that without this tension neurons cannot function. Furthermore, we have preliminary evidence of increased neuronal contractility with physical exercise, revealing the first possible link between mind and body. Understanding the critical role of neuron mechanics on neuron function may lead to new therapies against mental diseases including anxiety, depression, dementia, and Alzheimer’s disease.

This article presents a fully integrated portable microplastic (MP) detection system-on-chip (SoC) that combines high-sensitivity radio frequency (RF) micro electromechanical systems (MEMS) sensors with narrow-band notch-tracking algorithms for dielectric discrimination. Conventional MP detection systems suffer from the low transmit power (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TX</sub>), restricted receiver dynamic range (DR), and the need for separate two-chip configurations, increasing complexity and cost. To overcome these challenges, the proposed 180-nm CMOS MP detection SoC monolithically integrates the sensor driver and readout functionalities, eliminating the need for multiple chips and reducing power consumption. The MP detection SoC, originally developed for a 1.2 GHz notch RF MEMS sensor, was also evaluated with a 2.84 GHz sensor, demonstrating its scalability for high-frequency MP detection. The system tracks the resonance points of the RF MEMS sensor to monitor transmission coefficient (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">21</sub>) variations, enabling quantitative analysis of MPs such as polyethylene (PE), polypropylene (PP), polyethylene terephthalate (PET), polystyrene (PS), and polymethyl methacrylate (PMMA). By correlating notch frequency shifts with variations in relative permittivity (ε<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub>) and loss tangent [tan(δ)], it performs stepwise MP detection and enables quantitative analysis of MPs. The system was validated using real-world MPs, including 3D printer filament (PLA), aged water pipelines, and laundry wastewater, showing results consistent with thermogravimetric analysis-Fourier transform-infrared (TGA-FT-IR) spectroscopy, with error rates ranging from 10% to 23%. These results highlight a compact, portable, and reliable solution for on-site MP detection under realistic environmental conditions.

Fault diagnosis of ball screws in servo mechanisms using deeply integrated MEMS sensors and convolutional neural networks

An Improved Instantaneous Wavelet Bispectrum Feature for gear fault diagnostics using a Wireless MEMS sensor

Accelerographs are essential instruments for quantifying strong ground motion, serving as the foundation of modern earthquake engineering. In Peru, the first accelerographic station was installed in Lima in 1944; since then, various institutions have promoted the expansion of the national network. However, this network's spatial coverage and instrumentation remain insufficient to properly characterize strong motion and support seismic risk reduction policies. In this context, the KUYUY accelerograph is presented as a low-cost, low-noise device equipped with real-time telemetry and high-performance MEMS sensors. Its interoperability with the Intelligent Automatic Processing System (SIPA) enables real-time monitoring and automated signal analysis for seismic microzonation studies and rapid damage assessment, contributing to seismic risk reduction in Peru. The validation process included static gravity calibration, field comparison with a reference accelerograph, and an initial deployment in Lima and Yurimaguas. The results demonstrate the proposed accelerograph's linear response, temporal stability, and amplitude consistency with respect to high-end instruments, with differences below 5-10%.

This paper shows an overview of understanding the wind turbines, their historical evaluation and technological advancements in wind turbine design and how they work, capturing the progress from early wooden structures of the 19&amp;lt;sup&amp;gt;th&amp;lt;/sup&amp;gt; century to contemporary high-capacity machines. Highlighted are significant milestones, including Blyth&amp;apos;s first electric wind turbine and Brush&amp;apos;s improved designs, followed by Poul la Cour&amp;apos;s innovative aerodynamic concepts and substantial contributions made during the interwar years. The paper also shows wind turbines classification based on different concepts and their main components explaining their function and their design mechanism as rotor, blades &amp; gearbox and how they work together to convert wind energy into electrical energy. Due to increasing interest in offshore turbines, wind energy looks like to have a particularly potential future. Even with improvements, maximizing turbine performance, reducing environmental effects, and integrating wind energy into the electrical grid are still difficult tasks with many challenges. Recent studies on complex aerodynamic systems and structural health monitoring reflect ongoing efforts to extend turbine lifetime and efficiency where the paper highlights two different studies MEMS sensors and SHM system. In order to address the present issues with wind energy use and to pursue sustainable, renewable, cost-effective energy solutions for the future, the paper&amp;apos;s conclusion highlights the need for ongoing research and development. Future developments in smart grid and energy storage technologies will also be essential to improving offshore wind farms&amp;apos; dependability and efficiency. Through encouraging cooperation among scientists, engineers, and representatives, the shift.

The aim of this study was to conduct an advanced analysis of the MEMS sensor, including both experimental tests and numerical simulations, in order to determine its mechanical properties and operational dynamics in detail. It is challenging to find publications in the literature that are not based on theoretical assumptions or general manufacturer data, which do not reflect the actual microstructural characteristics of the sensor. This study uses a numerical model developed in MATLAB/Simulink, which allows the experimentally determined material characteristics to be combined with predictive dynamic modelling. The model takes into account key mechanical parameters such as stiffness, damping and response to dynamic loads, and the built-in optimisation algorithm allows the structural parameters of the MEMS accelerometer to be estimated directly from experimental data. In addition, SEM microscopic studies and EDS chemical composition analysis provided detailed information on the sensor’s microstructure, allowing its impact on mechanical properties and dynamic parameters to be assessed. The integration of advanced experimental methods with numerical modelling has resulted in a model whose response closely matches the measurement results, which is an important step towards further research on design optimisation and improving the reliability of MEMS sensors in diverse operating conditions.

Addressing the challenges of poor real-time data processing and high bandwidth consumption inherent in traditional cloud computing for coal mining machinery condition monitoring, alongside the limited generalization capability of existing fault diagnosis models, this paper proposes a distributed intelligent monitoring and diagnostic framework integrating edge computing, wireless sensor networks (WSN), and deep learning-based transfer learning techniques. Initially, a multi-source data acquisition system grounded on WSN is established, utilizing the STM32F405 microcontroller, ADXL1005 MEMS sensor, and nRF24L01+ modules to enable high-precision vibration data collection and robust transmission. Subsequently, an edge computing terminal powered by the RK3588 processor is designed, featuring heterogeneous communication and multi-source data aggregation capabilities, effectively reducing data transmission latency. Finally, the fault diagnosis model is refined through transfer learning to filter effective deep features and minimize distribution discrepancies between source and target domains. Experimental results demonstrate that the system achieves 16-bit data acquisition precision, wireless transmission success rates exceeding 98%, and a 12.3% improvement in fault diagnosis accuracy compared to conventional deep learning methods, thereby fulfilling the real-time monitoring and fault diagnosis requirements of equipment operating in the complex environments of coal mines.

Abstract With the widespread application of MEMS sensors in harsh environments, there is an increasingly high demand for their high-temperature resistance. Due to the exceptional mechanical, chemical, and electrical properties of third-generation semiconductor silicon carbide (SiC), pressure sensors utilizing this material are capable of operating in high-temperature environments. However, significant challenges remain in achieving high-temperature interconnects for SiC substrate-based pressure sensors. Among these challenges, wire bonding stands out as one of the most critical interconnect technologies in semiconductor packaging. Its quality directly impacts the long-term reliability of the sensors. Therefore, achieving stable wire bonding under elevated temperatures is crucial for the application of sensors in extreme thermal environments. This study employs a Cr/Al 2 O 3 composite layered structure to achieve high-quality deposition of Pt electrodes on SiC substrates. Through precise control of the bonding process parameters (bonding voltage: 1.6 V, bonding time: 7 ms), a high-strength wire bond with a maximum tensile strength of 390.4 mN was achieved, exhibiting excellent connection stability. Furthermore, the performance of Pt wire bonds was investigated after annealing at 600 °C, 900 °C, and 1200 °C for one hour, including their surface morphology, microstructure, mechanical strength, and electrical properties. Results show that even after annealing at 1200 °C, the bonded interface retained high tensile strength, meeting application standards, and exhibited stable electrical conductivity. This study provides a key technological basis for packaging and interconnection of high-temperature sensors in aerospace applications, which is of great significance for achieving highly reliable packaging of high-temperature sensor devices.