Microelectromechanical Sensors Research Articles

Characterization of Sand and Dust Pollution Degradation Based on Sensitive Structure of Microelectromechanical System Flow Sensor

The effect of sand and dust pollution on the sensitive structures of flow sensors in microelectromechanical systems (MEMS) is a hot issue in current MEMS reliability research. However, previous studies on sand and dust contamination have only searched for sensor accuracy degradation due to heat conduction in sand and dust cover and have yet to search for other failure-inducing factors. This paper aims to discover the other inducing factors for the accuracy failure of MEMS flow sensors under sand and dust pollution by using a combined model simulation and sample test method. The accuracy of a flow sensor is mainly reflected by the size of its thermistor, so in this study, the output value of the thermistor value was chosen as an electrical characterization parameter to verify the change in the sensor’s accuracy side by side. The results show that after excluding the influence of heat conduction, when sand particles fall on the device, the mutual friction between the sand particles will produce an electrostatic current; through the principle of electrostatic dissipation into the thermistor, the principle of measurement leads to the resistance value becoming smaller, and when the sand dust is stationary for some time, the resistance value returns to the expected level. This finding provides theoretical guidance for finding failure-inducing factors in MEMS failure modes.

Wireless Gas Sensor Based on the Mesoporous ZnO-SnO2 Heterostructure Enables Ultrasensitive and Rapid Detection of 3-Methylbutyraldehyde.

Achieving ultrasensitive and rapid detection of 3-methylbutyraldehyde is crucial for monitoring chemical intermediate leakage in pharmaceutical and chemical industries as well as diagnosing ventilator-associated pneumonia by monitoring exhaled gas. However, developing a sensitive and rapid method for detecting 3-methylbutyraldehyde poses challenges. Herein, a wireless chemiresistive gas sensor based on a mesoporous ZnO-SnO2 heterostructure is fabricated to enable the ultrasensitive and rapid detection of 3-methylbutyraldehyde for the first time. The mesoporous ZnO-SnO2 heterostructure exhibits a uniform spherical shape (∼79 nm in diameter), a high specific surface area (54.8 m2 g-1), a small crystal size (∼4 nm), and a large pore size (6.7 nm). The gas sensor demonstrates high response (18.98@20 ppm), short response/recovery times (13/13 s), and a low detection limit (0.48 ppm) toward 3-methylbutyraldehyde. Furthermore, a real-time monitoring system is developed utilizing microelectromechanical systems gas sensors. The modification of amorphous ZnO on the mesoporous SnO2 pore wall can effectively increase the chemisorbed oxygen content and the thickness of the electron depletion layer at the gas-solid interface, which facilitates the interface redox reaction and enhances the sensing performance. This work presents an initial example of semiconductor metal oxide gas sensors for efficient detection of 3-methylbutyraldehyde that holds great potential for ensuring safety during chemical production and disease diagnosis.

Research on the equivalent impact test method for different pulse widths based on micro-electro-mechanical system devices

The impact pulse width generated by kinetic weapons during launch differs significantly from that produced by conventional laboratory overload impact methods. Consequently, there is an urgent need to develop a method for evaluating the equivalent impact between overload environments characterized by different pulse widths. In the Hopkinson Bar overload test environment, this study presents a method for performing equivalent impact tests based on the Sum of Effective Impact Energy for micro-electromechanical system acceleration sensors, in accordance with the impact dynamics theory and the law of energy variation. It establishes an equivalent impact model for inertial loads with different pulse widths. The dynamic response of the accelerometer to various impact loads was evaluated through the Hopkinson Bar overload test and live firing test, with an error margin of less than 10 %. Timely guidance can be provided for selecting overload parameters in dynamic impact testing, thereby advancing the engineering process of simulating extreme overload environments.

Smartphone-Based Recognition of Heart Failure by Means of Microelectromechanical Sensors

BackgroundHeart failure (HF) is the leading cause of hospitalization in individuals over 65 years of age. Identifying noninvasive methods to detect HF may address the epidemic of HF. Seismocardiography which measures cardiac vibrations transmitted to the chest wall has recently emerged as a promising technology to detect HF. ObjectivesIn this multicenter study, the authors examined whether seismocardiography using commercially available smartphones can differentiate control subjects from patients with stage C HF. MethodsBoth inpatients and outpatients with HF were enrolled from Finland and the United States. Inpatients with HF were assessed within 2 days of admission, and outpatients were assessed in the ambulatory setting. In a prespecified pooled data analysis, algorithms were derived using logistic regression and then validated using a bootstrap aggregation method. ResultsA total of 217 participants with HF (174 inpatients and 172 outpatients) and 786 control subjects from cardiovascular clinics were enrolled. The mean age of participants with acute HF was 64 ± 13 years, 64.9% were male, left ventricular ejection fraction was 39 ± 15%, and median N-terminal pro–B-type natriuretic peptide was 5,778 ng/L (Q1-Q3: 1,933-6,703). The majority (74%) of participants with HF had reduced EF, and 38% had atrial fibrillation. Across both HF cohorts, the algorithms had an area under the receiver operating characteristic curve of 0.95 with a sensitivity of 85%, specificity of 90%, and accuracy of 89% for the detection of HF, with a decision threshold of 0.5. The positive and negative likelihood ratios were 8.50 and 0.17, respectively. The accuracy of the algorithms was not significantly different in subgroups based on age, sex, body mass index, and atrial fibrillation. ConclusionsSmartphone-based assessment of cardiac function using seismocardiography is feasible and differentiates patients with HF from control subjects with high diagnostic accuracy. (Recognition of Heart Failure With Micro Electro-mechanical Sensors FI [NCT04444583]; Recognition of Heart Failure With Micro Electro-mechanical Sensors [NCT04378179]; Detection of Coronary Artery Disease With Micro Electro-mechanical Sensors [NCT04290091])

Wireless Blackbox for Cars Using Sensors and GPS Module

Abstract: The main purpose of this wireless black box project is to develop a vehicle black box system that can be installed into any vehicle all over the world. This paradigm is often designed with minimum range of circuits. Wireless black box is basically a device that will indicate all the parameters of a vehicle crash and will also store and display its parameters such as temperature, location, vibration, alcohol limit etc. At the time of accident, the message will be sent from the system built inside the car to the registered mobile numbers such as emergency numbers of police stations, hospitals, family members, owner etc. We have used various types of sensors like temperature sensor, which is used to measure temperature. Vibration sensor measures vibrations felt by the car during accident. Alcohol sensor is located on the steering wheel which will indicate whether the driver is drunk. Micro-electromechanical system sensor is used to indicate tilt during the accident. GSM module, GPS module are some of the devices used in this project which helps in accomplishing the output.

Exploring the potential of smartphone MEMS sensors for cost-effective rotating machinery speed estimation

PurposeThe purpose of this study is to explore the use of smartphone-embedded microelectro-mechanical sensors (MEMS) for accurately estimating rotating machinery speed, crucial for various condition monitoring tasks. Rotating machinery (RM) serves a crucial role in diverse applications, necessitating accurate speed estimation essential for condition monitoring (CM) tasks such as vibration analysis, efficiency evaluation and predictive assessment.Design/methodology/approachThis research explores the utilization of MEMS embedded in smartphones to economically estimate RM speed. A series of experiments were conducted across three test setups, comparing smartphone-based speed estimation to traditional methods. Rigorous testing spanned various dimensions, including scenarios of limited data availability, diverse speed applications and different smartphone placements on RM surfaces.FindingsThe methodology demonstrated exceptional performance across low and high-speed contexts. Smartphones-MEMS accurately estimated speed regardless of their placement on surfaces like metal and fiber, presenting promising outcomes with a mere 6 RPM maximum error. Statistical analysis, using a two-sample t-test, compared smartphone-derived speed outcomes with those from a tachometer and high-quality (HQ) data acquisition system.Research limitations/implicationsThe research limitations include the need for further investigation into smartphone sensor calibration and accuracy in extremely high-speed scenarios. Future research could focus on refining these aspects.Social implicationsThe societal impact is substantial, offering cost-effective CM across various industries and encouraging further exploration of MEMS-based vibration monitoring.Originality/valueThis research showcases an innovative approach using smartphone-embedded MEMS for RM speed estimation. The study’s multidimensional testing highlights its originality in addressing scenarios with limited data and varied speed applications.

Improving the underwater navigation performance of an IMU with acoustic long baseline calibration

Underwater acoustic Long-Baseline System (LBL) is an important technique for submarine positioning and navigation. However, the high cost of the seafloor equipment and complex construction of a seafloor network restrict the distribution of the LBL within a small area, making an underwater vehicle difficult for long-distance and high-precision acoustic-based or inertial-based navigation. We therefore propose an acoustic LBL-based Inertial Measurement Unit (IMU) calibration algorithm. When the underwater vehicle can receive the acoustic signal from a seafloor beacon, the IMU is precisely calibrated to reduce the cumulative error of Strapdown Inertial Navigation System (SINS). In this way, the IMU is expected to maintain a certain degree of accuracy by relying solely on SINS when the vehicle reaches out the range of the LBL network and cannot receive the acoustic signal. We present the acoustic LBL-based IMU online calibration model and analyze the factors that affect the accuracy of IMU calibration. The results fulfill the expectation that the gyroscope bias and accelerometer bias are the main error sources that affect the divergence of SINS position errors, and the track line of the underwater vehicle directly affects the accuracy of the calibration results. In addition, we deduce that an optimal calibration trajectory needs to consider the effects of the three-dimensional observability and position dilution of precision. In the experiment, we compare the effects of seven calibration trajectories: straight and diamond-shaped with and without the change of depth, and three sets of curves with the change of depth: circular, S-shaped, and figure-eight. Among them, we find that the figure-eight is the optimal trajectory for acoustic LBL-based IMU online calibration. We take the maintenance period during which the accumulated SINS Three Dimensional (3D) position errors are below 1 km to evaluate the calibration performance. The filed experimental results show that for the Micro-electromechanical Systems-grade IMU sensor, the maintenance period for the IMU calibrated with the proposed algorithm can be increased by 121% and 38.9% compared to the IMU without calibration and with the laboratory default parameter calibration, indicating the effectiveness of the proposed calibration algorithm.

Solar PV tracking system using arithmetic optimization with dual axis and sensor

Presently, PV system is considered the best renewable energy source for electricity production. In order to maximize energy output in photovoltaic systems, a system for tracking the sun's position and adjusting panel positions was created. Despite the fact that several models for tracking solar radiation have been suggested to improve energy production, it faces challenges in continuous tracking and power consumption. This paper proposes a novel sensor-based solar tracking system with numerical optimization to increase photovoltaic systems' energy output. The initial model was for a two-axis tracking system based on sensors. Solar panel and sun positions are detected by this system using ultraviolet and microelectromechanical sun sensors. To improve tracking movements and photovoltaic energy production, we recommend using solar sensors to construct a novel two-axis solar tracking device. This technology benefits from increased solar radiation and solar energy harvesting capabilities. The main disadvantage of dual-axis active solar tracking systems is that the drive mechanism frequently uses up the output power of the solar panels. As a result, the net power gain of the solar panel is less than its maximum. Further, a control system was developed with proportional integral derivative (PID) controller and arithmetic optimization (AO) to adjust the panel position relative to suns movement. The AO technique tunes the PID controller gains until it reaches its desired level to enhance the production of power. With the use of sensors, the tracking system can use mathematical optimization algorithms and the sun's position to minimize power consumption while still maintaining optimal solar panel positioning. The developed model was designed in the MATLAB software and the outcomes are analyzed. The paper also presents an energy analysis of the system, which evaluates the energy input, output and overall system efficiency. We recommend using solar sensors to create a unique dual-axis solar tracking system. This technology benefits from increased solar radiation and solar energy harvesting capabilities.

Modeling the mechanical response of microelectromechanical system (MEMS)-based sensors to volatile alcohol vapors: A finite element analysis

The development of microelectromechanical system-based sensors, such as microcantilever sensors, has garnered considerable interest across various fields. Notably, there is a significant focus on the detection of volatile alcohol vapors, which holds promise in mitigating breath-related illnesses. This study employed finite element analysis to simulate the deflection of a silica-based microcantilever coated with polymethyl methacrylate in response to different volatile alcohol vapors (VAVs), such as methanol, ethanol, and isopropanol. A Multiphysics framework was used to analyze the time-dependent response of a microcantilever to different concentrations of VAVs incorporating fluid–structure interaction. This complex model integrated the aspects of laminar flow, solid mechanics, and transport of dilute species. Significant agreement has been achieved between finite element analysis-simulated results and the experimental findings that we had previously documented. This alignment revealed consistent trends, with methanol exhibiting higher levels than ethanol, followed by isopropanol, further validating the robustness and reliability of the sensor system in VAV detection.

Black Phosphorus-Tungsten Oxide Sandwich-like Nanostructures for Highly Selective NO2 Detection.

Modern chemical production processes often emit complex mixtures of gases, including hazardous pollutants such as NO2. Although widely used, gas sensors based on metal oxide semiconductors such as WO3 respond to a wide range of interfering gases other than NO2. Consequently, developing WO3 gas sensors with high NO2 selectivity is challenging. In this study, a simple one-step hydrothermal method was used to prepare WO3 nanorods modified with black phosphorus (BP) flakes as sensitive materials for NO2 sensing, and BP-WO3-based micro-electromechanical system gas sensors were fabricated. The characterization of the as-prepared BP-WO3 composite through X-ray diffraction scanning electron microscopy and X-ray photoelectron spectroscopy confirmed the successful formation of the sandwich-like nanostructures. The result of gas-sensing tests with 2-14 ppm NO2 indicated that the sensor response was 1.25-2.21 with response-recovery times of 36 and 36 s, respectively, at 190 °C. In contrast to pure WO3, which exhibited a response of 1.07-2.2 to 0.3-5 ppm H2S at 160 °C, BP-WO3 showed almost no response to H2S. Thus, compared with pure WO3, BP-WO3 exhibited significantly improved NO2 selectivity. Overall, the BP-WO3 composite with sandwich-like nanostructures is a promising material for developing highly selective NO2 sensors for practical applications.

An innovative approach to continuously monitor rail track surface displacement during freezing-thawing seasons

Large differential displacement as a result of freeze–thaw cycles (FTC) is a major issue affecting the safety and performance of railway tracks constructed in cold regions. Current track monitoring methods in winter are mainly based on visual inspection and manual measurements. The frequency of FTC is expected to increase under future climate and thus it is essential for railway operators to employ new methods of inspection that allow continuous and remote monitoring of railway tracks during the freeze–thaw period. While sensors are commonly used to measure localized track displacement, the advantages and limitations of using instruments that are capable of collecting data over a segment of track is not well understood. This paper investigates the use of a micro electro-mechanical sensor (MEMS) based instrument for monitoring surface displacement of railway tracks during FTC. The ShapeArray™ instrument, in this case the SAAX inclinometer, is a MEMS based sensor that was selected due to its ability to collect data over a segment of track (up to 150 m). Two sets of SAAX inclinometers were installed on a 50-metre section of track to measure data during two freezing-thawing periods. Analysis of the field data suggests that the main limitation of using MEMS-based sensors is their sensitivity to fluctuations of ambient temperature which may be confused with their responses to actual track displacement. A data analysis technique has been developed to remove the unwanted variations so that the instrument can be effectively used for monitoring track displacement during FTC. The comparison of the processed measurements with data collected from a digital level suggested that it provides a reasonably accurate measure of track surface displacement. Overall, the limited investigations in this project suggests that using MEMS-based sensor along with proper data processing techniques can provide useful information about the differential displacement over a segment of track, a key parameter for the safety of railway operation. The measurements can also be transferred through the cloud, enabling track engineers to remotely monitor track condition in near-real-time without the need to access the track and interrupt trains operations.

Triaxial piezoresistive force sensor probe with high sensitivity and stiffness using 3D notch structure

This paper proposes a microelectromechanical system triaxial piezoresistive force sensor probe with high sensitivity and stiffness. The sensor probe is composed of a cantilever and four supporting beams. Two of the four beams had horizontal notch parts at the root, and sidewall-doped piezoresistors were utilised to detect the in-plane deformation. There was a vertical notch at the root of the remaining two beams and a surface-doping piezoresistor in one beam to detect the out-of-plane deformation. Thus, the proposed sensor probe measures the three directional forces applied to the cantilever tip with high sensitivity and stiffness owing to the corresponding piezoresistive notch structures. We demonstrate a fabrication process that forms the notch structure and the surface and sidewall doping methods. Our fabricated device was confirmed capable of measuring triaxial forces with a force resolution at the sub-micro-Newton level.

Research on a capacitive MEMS pressure sensor based on through glass via

AbstractThe paper reports the fabrication and characterization of a capacitive Microelectro Mechanical Systems pressure sensor based on through glass vias (TGV), which had a large dynamic range by operating in touch mode. The substrate with TGV offers the advantage of elimination of parasitic capacitances owing to the superior insulation property of glass. The fabrication process was introduced. In particular, anodic bonding was applied in high vacuum to realize the hermetic package to obtain large operating range and high sensitivity. The performance of the sensor with a sensitive diaphragm of 700 μm × 1400 μm was characterized in the pressure range from −80 to 180 kPa. The sensitivity was determined as 0.072 pF/kPa over the range of 160 kPa with a good linearity of 97%.

Directional Resonant MEMS Acoustic Sensor and Associated Acoustic Vector Sensor.

This paper reports on the design, modeling, analysis, and evaluation of a micro-electromechanical systems acoustic sensor and the novel design of an acoustic vector sensor array (AVS) which utilized this acoustic sensor. This research builds upon previous work conducted to develop a small, lightweight, portable system for the detection and location of quiet or distant acoustic sources of interest. This study also reports on the underwater operation of this sensor and AVS. Studies were conducted in the lab and in the field utilizing multiple acoustic sources (e.g., generated tones, gun shots, drones). The sensor operates at resonance, providing for high acoustic sensitivity and a high signal-to-noise ratio (SNR). The sensor demonstrated a maximum SNR of 88 dB with an associated sensitivity of -84.6 dB re 1 V/μPa (59 V/Pa). The sensor design can be adjusted to set a specified resonant frequency to align with a known acoustic signature of interest. The AVS demonstrated an unambiguous, 360-degree, in-plane, azimuthal coverage and was able to provide an acoustic direction of arrival to an average error of within 3.5° during field experiments. The results of this research demonstrate the potential usefulness of this sensor and AVS design for specific applications.

Coherent Flow Structures Linked to the Impulse Criterion for Incipient Motion of Coarse Sediment

Incipient motion has been a topic of investigation by researchers, engineers and scientists for more than a century. The main approach for studying sediment entrainment has been the static approach that uses temporal and spatial averaged flow parameters like bed shear stress and stream power to link them indirectly to sediment entrainment. Recent research outputs have shed light on the important role of turbulent fluctuations in the sediment transport process. It is suggested that the approach of using temporal and spatial averaged parameters fails to account for the dynamic and probabilistic nature of the entrainment process, as inherited by flow turbulence. This has led to the introduction of the only dynamic criteria in the literature for studying sediment entrainment, namely the impulse and energy criteria. These criteria take into account both the magnitude and duration of the turbulent flow event used for assessing the conditions that can result in sediment entrainment. In light of this, this work aims to assess whether there is a trend in terms of the type of flow structures that occur in sequence before and after the occurrences of the flow impulses that have resulted in the coarse particle’s entrainment. To achieve this, we conducted a well-controlled laboratory experiment to investigate the incipient motion of a 7 cm diameter instrumented particle. Five runs of the experiment were performed at flowrates close to the threshold of motion. The instrumented particle was equipped with micro-electro-mechanical sensors (MEMS) to accurately measure its inertial dynamics and detect motion. The sensors recorded entrainment events, and these events were stochastically linked to the impulses occurring for the tested flow conditions. Quadrant analysis was used to investigate the type of flow structures that occurred before, during and after the occurrence of quadrant events with an impulse above the critical impulse. The findings herein associate coarse particle entrainments with energetic impulses linked primarily to sweep events (Q4) and secondarily, sequence of sweeps (Q4) and ejections (Q1).

An Improved GPS/INS Integration Based on EKF and AI During GPS Outages

Inertial navigation system (INS) is often integrated with satellite navigation systems to achieve the required precision at high-speed applications. In global navigation system (GPS)/INS integration systems, GPS outages are unavoidable and a severe challenge. Moreover, because of the usage of low-cost microelectromechanical sensors (MEMS) with noisy outputs, the INS will get diverged during GPS outages, and that is why navigation precision severely decreases in commercial applications. In this paper, we improve GPS/INS integration system during GPS outages using extended Kalman filter (EKF) and artificial intelligence (AI) together. In this integration algorithm, the AI receives the angular rates and specific forces from the inertial measurement unit (IMU) and velocity from the INS at [Formula: see text] and [Formula: see text]. Therefore, the AI has positioning and timing data of the INS. While the GPS signals are available, the output of the AI is compared with the GPS increment; so that the AI is trained. During GPS outages, the AI will practically play the GPS role. Thus, it can prevent the divergence of the GPS/INS integration system in GPS-denied environments. Furthermore, we utilize neural networks (NNs) as an AI module in five different types: multi-layer perceptron (MLP) NN, radial basis function (RBF) NN, wavelet NN, support vector regression (SVR) and adaptive neuro-fuzzy inference system (ANFIS). To evaluate the proposed approach, we utilize a real dataset that has been gathered by a mini-airplane. The results demonstrate that the proposed approach outperforms the INS and GPS/INS integration systems with the EKF during GPS outages. Meanwhile, the ANFIS also reached more than 47.77% precision compared to the traditional method.

Miniaturization and modeling of a folded beam-based microelectromechanical intraocular pressure sensor

This paper describes the design and detailed analysis of a microelectromechanical system (MEMS) capacitive pressure sensor consisting of nested folded beam suspensions and variously shaped diaphragms that can be used as a part of an LC tank implant circuit for biomedical pressure sensing applications, designed specifically for continuous intraocular pressure (IOP) monitoring in glaucoma patients. The pressure sensor has been designed to measure pressures within the IOP sensor range of 0–8000 Pa. This paper aims to reduce the size of the sensor in comparison to previous research. Significant benefits of miniaturization include requiring less energy, being lightweight, being inexpensive, and having minimal impact on the objects being detected. The MEMS implantable pressure sensor for continuous monitoring of IOP would prevent the progression of the disease, and these tiny devices could alleviate patients’ suffering. Microbeams are likely the most widely utilized structural element in MEMS. This design adds nested folded beam suspensions to the diaphragm, which reduces the diaphragm’s residual tension and rigidity; as a result, the device’s dimension is smaller than the diaphragm without flexible beams (previous work). Due to the reduced rigidity, nested folded beam suspensions can also enhance sensitivity. Finally, the central deflection of the diaphragm will be determined under uniform external pressure. Using COMSOL/Multiphysics and SolidWorks, the design and simulation of pressure sensors have been accomplished. The outcomes reveal that the capacitive pressure sensor with nested folded beam suspensions and the rectangular diaphragm is better suited for measuring IOP, and its dimensions are also smaller than those of previous works ().

Design and Measurement of Microelectromechanical Three-Axis Magnetic Field Sensors Based on the CMOS Technique.

The design, fabrication, and measurement of a microelectromechanical system (MEMS) three-axis magnetic field sensor (MFS) based on the commercial complementary metal oxide semiconductor (CMOS) process are investigated. The MFS is a magnetic transistor type. The performance of the MFS was analyzed employing the semiconductor simulation software, Sentaurus TCAD. In order to decrease the cross-sensitivity of the three-axis MFS, the structure of the MFS is planed to accommodate two independent sensing components, a z-MFS utilized to sense magnetic field (M-F) in the z-direction and a y/x-MFS composed of a y-MFS and a x-MFS to be utilized to sense M-F in the y- and x-directions. The z-MFS incorporates four additional collectors to increase its sensitivity. The commercial 1P6M 0.18 μm CMOS process of the Taiwan Semiconductor Manufacturing Company (TSMC) is utilized to manufacture the MFS. Experiments depict that the MFS has a low cross-sensitivity of less than 3%. The sensitivities of z-, y-, and x-MFS are 237 mV/T, 485 mV/T, and 484 mV/T, respectively.

A Wide-Dynamic Range (-40 °C to 180 °C) Active Wireless MEMS Temperature Sensor With 7m °C Sensitivity

We demonstrate a highly-accurate active wireless microelectromechanical (MEMS) temperature sensor with the largest reported dynamic range (-40°C to 180°C) among active MEMS temperature sensors. The sensor uses a 27.2 MHz high quality factor (Q > 3,200) AlN-on-Si thin-film piezoelectric-on-silicon (TPoS) MEMS resonator to enable highly accurate temperature sensing, with minimum sensitivity (S), better than 0.011 °C across this wide temperature range. The wireless sensor uses an innovative frequency shift keying (FSK) 900 MHz direct up-conversion transmitter. The transmitter uses frequency multipliers for up-conversion, obviating the need for a temperature-stable frequency reference clock, thereby preserving the temperature coefficient of frequency (TCF) of the resonator, and consequently the sensitivity of the sensor. The experimental results show a very high correlation (<0.03% error) between the TCF of the MEMS oscillator and that of the transmitter output signal, demonstrating the viability of the proposed direct up-conversion technique. A detailed analysis of the Allan variance for MEMS temperature sensors which can be used to design for a given minimum sensitivity, is also presented in the paper.

Small Size and Low‐Cost TENG‐Based Self‐Powered Vibration Measuring and Alerting System

AbstractVibration measurement systems containing sensors, signal conditioning, and data acquisition devices, are important for monitoring motors, gearboxes, turbines, etc. Microelectromechanical and piezoelectric sensors are predominantly used for vibration measurements. However, they are not cost‐effective, flexible in design, and incapable of self‐powering. Recently, triboelectric nano‐generator (TENG)‐based vibration sensors have been considered as a possible alternative to resolve this problem, and tremendous progress has been achieved. Previous work on TENG‐based sensors is limited to optimizing the sensor design, while the signal conditioning and data acquisition of TENG signal still need investigation for actual applications. This work develops a TENG‐based vibration measurement device and self‐powered alerting system that is integrated with the signal condition and data acquisition systems. The experimental results show that the proposed measurement system successfully measures signals within the range of 0–1800 Hz frequency. Meanwhile, the TENG generates a high output, up to 80 V and 0.55 µA from small size TENG area (3.6 cm2). The signal is adequate to harvest energy for self‐powering to drive alerting components (harvest 320 mJ in 36 h, which drives alarming for duration of 1.5 s). The proposed device is cost‐effective (30 $), small (105 cm3), and consumes less power (0.18 W) in comparison to commercial devices.