Nitrogen-Doped Carbon Quantum Dots Activated Dandelion-Like Hierarchical WO3 for Highly Sensitive and Selective MEMS Sensors in Diabetes Detection.

High sensitivity, low concentration, and excellent selectivity are pronounced primary challenges for semiconductor gas sensors to monitor acetone from exhaled breath. In this study, nitrogen-doped carbon quantum dots (N-CQDs) with high reactivity were used to activate dandelion-like hierarchical tungsten oxide (WO3) microspheres to construct an efficient and stable acetone gas sensor. Benefiting from the synergistic effect of both the abundant active sites provided by the unique dandelion-like hierarchical structure and the high reaction potential generated by the sensitization of the N-CQDs, the resulting 16 wt % N-CQDs/WO3 sensor shows an ultrahigh response value (Ra/Rg = 74@1 ppm acetone), low detection limit (0.05 ppm), outstanding selectivity, and reliable stability to acetone at the optimum working temperature of 210 °C. Noteworthy that the N-CQDs facilitate the carrier migration and intensify the reaction between acetone and WO3 during the sensing process. Considering the above advantages, N-CQDs as a sensitizer to achieve excellent gas-sensitive properties of WO3 are a promising new strategy for achieving accurate acetone detection in real time and facilitating the development of portable human-exhaled gas sensors.

Inhibiting Emulative Oxygen Adsorption via Introducing Pt-Segregated Sites into the Pd Surface for Enhanced H2 Sensing in Air.

Pd-modified metal sulfide gas sensors exhibit excellent hydrogen (H2) sensing activity through spillover effects. However, the emulative oxygen adsorption often occupies an exposed Pd surface and thus limits the effective Pd-H interaction, impeding the H2 sensing performance in air. Herein, we develop an edge-rich Pt-shell/Pd-core structure to adjust the selective adsorption between oxygen and hydrogen for effective H2 sensing in an air atmosphere. Detailedly, through accurately regulating the rate of Pt deposition onto the icosahedron Pd surface, an edge-rich Pt-shell/Pd-core structure can be first achieved. It has been found that marginal Pt aggregations can segregate the oxygen molecules around the Pt species and induce easier Pt-O bonding, further guiding accessible Pd surfaces for effective Pd-H interactions, which can be verified by 1H ssNMR, in-situ Raman, ex-situ XPS, and density functional theory analyses. The final ZnS/PdPt sensor exhibits an ultrasensitive response (8608 to 4% H2) and a wide detected range (0.5 ppm-4%) in air, exceeding most reported hydrogen sensors.

Replicating Spectral Baseline for Unambiguous Frequency Locking in Resonant Sensors.

Electrothermal piezoresistive resonant cantilever sensors have been fabricated with embedded actuating (heating resistor) and sensing (piezo resistors) parts, with the latter configured in a Wheatstone bridge circuit. Due to the close spacing between these two elements, a direct thermal parasitic effect on the resonant sensor during the actuating-sensing process leads to asymmetric amplitude and reversing phase spectral responses. Such a condition affects the precise determination of the cantilever's resonant frequency, f0. Moreover, in the context of phase-locked loop-based (PLL) resonance tracking, a reversing phase spectral response hinders the resonance locking due to its ambiguity. In this work, a replica of the baseline spectral was applied to remove the thermal parasitic effect on the resonance spectra of the cantilever sensor, and its capability was simulated through mathematical analysis. This replica spectral was subtracted from the parasitized spectral using a particular calculation, resulting in optimized spectral responses. An assessment using cigarette smoke particles performed a desired spectral shifting into symmetrical amplitude shapes and monotonic phase transitions, subsequently allowing for real-time PLL-based frequency tracking.

The Revolution in Breast Cancer Diagnostics: From Visual Inspection of Histopathology Slides to Using Desktop Tissue Analysers for Automated Nanomechanical Profiling of Tumours.

We aim to develop new portable desktop tissue analysers (DTAs) to provide fast, low-cost, and precise test results for fast nanomechanical profiling of tumours. This paper will explain the reasoning for choosing indentation-type atomic force microscopy (IT-AFM) to reveal the functional details of cancer. Determining the subtype, cancer stage, and prognosis will be possible, which aids in choosing the best treatment. DTAs are based on fast IT-AFM at the size of a small box that can be made for a low budget compared to other clinical imaging tools. The DTAs can work in remote areas and all parts of the world. There are a number of direct benefits: First, it is no longer needed to wait a week for the pathology report as the test will only take 10 min. Second, it avoids the complicated steps of making histopathology slides and saves costs of labour. Third, computers and robots are more consistent, more reliable, and more economical than human workers which may result in fewer diagnostic errors. Fourth, the IT-AFM analysis is capable of distinguishing between various cancer subtypes. Fifth, the IT-AFM analysis could reveal new insights about why immunotherapy fails. Sixth, IT-AFM may provide new insights into the neoadjuvant treatment response. Seventh, the healthcare system saves money by reducing diagnostic backlogs. Eighth, the results are stored on a central server and can be accessed to develop strategies to prevent cancer. To bring the IT-AFM technology from the bench to the operation theatre, a fast IT-AFM sensor needs to be developed and integrated into the DTAs.

MIMU Optimal Redundant Structure and Signal Fusion Algorithm Based on a Non-Orthogonal MEMS Inertial Sensor Array.

A micro-inertial measurement unit (MIMU) is usually used to sense the angular rate and acceleration of the flight carrier. In this study, multiple MEMS gyroscopes were used to form a spatial non-orthogonal array to construct a redundant MIMU system, and an optimal Kalman filter (KF) algorithm was established by a steady-state KF gain to combine array signals to improve the MIMU's accuracy. The noise correlation was used to optimize the geometric layout of the non-orthogonal array and reveal the mechanisms of influence of correlation and geometric layout on MIMU's performance improvement. Additionally, two different conical configuration structures of a non-orthogonal array for 4,5,6,8-gyro were designed and analyzed. Finally, a redundant 4-MIMU system was designed to verify the proposed structure and KF algorithm. The results demonstrate that the input signal rate can be accurately estimated and that the gyro's error can also be effectively reduced through fusion of non-orthogonal array. The results for the 4-MIMU system illustrate that the gyro's ARW and RRW noise can be decreased by factors of about 3.5 and 2.5, respectively. In particular, the estimated errors (1σ) on the axes of Xb, Yb and Zb were 4.9, 4.6 and 2.9 times lower than that of the single gyroscope.

Research on Pedestrian Indoor Positioning Based on Two-Step Robust Adaptive Cubature Kalman Filter with Smartphone MEMS Sensors.

With the development of location-based service (LBS), indoor positioning based on pedestrian dead reckoning (PDR) has become a hot research topic. Smartphones are becoming more popular for indoor positioning. This paper proposes a two-step robust-adaptive-cubature Kalman filter (RACKF) algorithm based on smartphone micro-electro-mechanical-system (MEMS) sensor fusion for indoor positioning. To estimate pedestrian heading, a quaternion-based robust-adaptive-cubature Kalman filter algorithm is proposed. Firstly, the model noise parameters are adaptively corrected based on the fading-memory-weighting method and the limited-memory-weighting method. The memory window of the limited-memory-weighting algorithm is modified based on the characteristics of pedestrian walking. Secondly, an adaptive factor is constructed based on the partial state inconsistency to overcome filtering-model deviation and abnormal disturbances. Finally, to identify and control the measurement outliers, the robust factor based on maximum-likelihood estimation is introduced into the filtering to enhance the robustness of heading estimation and support more robust dynamic-position estimation. In addition, based on the accelerometer information, a nonlinear model is constructed and the empirical model is used to estimate the step length. Combining heading and step length, the two-step robust-adaptive-cubature Kalman filter is proposed to improve the pedestrian-dead-reckoning method, which enhances the adaptability and robustness of the algorithm and further improves the accuracy of the plane-position solution. The adaptive factor based on the prediction residual and the robust factor based on the maximum-likelihood estimation are introduced into the filter to improve the adaptability and robustness of the filter, reduce the positioning error, and improve the accuracy of the pedestrian-dead-reckoning method. Three different smartphones are used to validate the proposed algorithm in an indoor environment. Additionally, the experimental results confirm the algorithm's effectiveness. From the results of the three smartphones, the root mean square error (RMSE) of the indoor-positioning results obtained by the proposed method is about 1.3-1.7 m.

Wearable Multi-Channel Pulse Signal Acquisition System Based on Flexible MEMS Sensor Arrays with TSV Structure.

Micro-electro-mechanical system (MEMS) pressure sensors play a significant role in pulse wave acquisition. However, existing MEMS pulse pressure sensors bound with a flexible substrate by gold wire are vulnerable to crush fractures, leading to sensor failure. Additionally, establishing an effective mapping between the array sensor signal and pulse width remains a challenge. To solve the above problems, we propose a 24-channel pulse signal acquisition system based on a novel MEMS pressure sensor with a through-silicon-via (TSV) structure, which connects directly to a flexible substrate without gold wire bonding. Firstly, based on the MEMS sensor, we designed a 24-channel pressure sensor flexible array to collect the pulse waves and static pressure. Secondly, we developed a customized pulse preprocessing chip to process the signals. Finally, we built an algorithm to reconstruct the three-dimensional pulse wave from the array signal and calculate the pulse width. The experiments verify the high sensitivity and effectiveness of the sensor array. In particular, the measurement results of pulse width are highly positively correlated with those obtained via infrared images. The small-size sensor and custom-designed acquisition chip meet the needs of wearability and portability, meaning that it has significant research value and commercial prospects.

Highly Reliable Multicomponent MEMS Sensor for Predictive Maintenance Management of Rolling Bearings.

In the field of vibration monitoring and control, the use of low-cost multicomponent MEMS-based accelerometer sensors is nowadays increasingly widespread. Such sensors allow implementing lightweight monitoring systems with low management costs, low power consumption and a small size. However, for the monitoring systems to provide trustworthy and meaningful data, the high accuracy and reliability of sensors are essential requirements. Consequently, a metrological approach to the calibration of multi-component accelerometer sensors, including appropriate uncertainty evaluations, are necessary to guarantee traceability and reliability in the frequency domain of data provided, which nowadays is not fully available. In addition, recently developed metrological characterizations at the microscale level allow to provide detailed and accurate quantification of the enhanced technical performance and the responsiveness of these sensors. In this paper, a dynamic calibration procedure is applied to provide the sensitivity parameters of a low-cost, multicomponent MEMS sensor accelerometer prototype (MDUT), designed, developed and realized at the University of Siena, conceived for rolling bearings vibration monitoring in a broad frequency domain (from 10 Hz up to 25 kHz). The calibration and the metrological characterization of the MDUT are carried out by comparison to a reference standard transducer, at the Primary Vibration Laboratory of the National Institute of Metrological Research (INRiM).

Anomaly Detection and Inter-Sensor Transfer Learning on Smart Manufacturing Datasets.

Smart manufacturing systems are considered the next generation of manufacturing applications. One important goal of the smart manufacturing system is to rapidly detect and anticipate failures to reduce maintenance cost and minimize machine downtime. This often boils down to detecting anomalies within the sensor data acquired from the system which has different characteristics with respect to the operating point of the environment or machines, such as, the RPM of the motor. In this paper, we analyze four datasets from sensors deployed in manufacturing testbeds. We detect the level of defect for each sensor data leveraging deep learning techniques. We also evaluate the performance of several traditional and ML-based forecasting models for predicting the time series of sensor data. We show that careful selection of training data by aggregating multiple predictive RPM values is beneficial. Then, considering the sparse data from one kind of sensor, we perform transfer learning from a high data rate sensor to perform defect type classification. We release our manufacturing database corpus (4 datasets) and codes for anomaly detection and defect type classification for the community to build on it. Taken together, we show that predictive failure classification can be achieved, paving the way for predictive maintenance.

A High-Precision Bandgap Reference with Chopper Stabilization and V-Curve Compensation Technique.

The MEMS sensor converts the physical signal of nature into an electrical signal. The output signal of the MEMS sensor is so weak and basically in the low-frequency band that the MEMS sensor interface circuit has a rigorous requirement for the noise/offset and temperature coefficient, especially in the bandgap reference block. However, the traditional amplifier has low-frequency noise and offset voltage, which will decrease the precision of the bandgap reference. In order to satisfy the need of the MEMS sensor interface circuit, a high-precision and low-noise bandgap reference is proposed in this paper. A novel operational amplifier with a chopper-stabilization technique is adopted to reduce offset and low-frequency noise. At the same time, the V-curve compensation circuit is used to realize the second-order curvature compensation. The circuit is implemented under the 0.18 µm standard of the CMOS process. The test result shows that the temperature coefficient of the bandgap is 2.31 ppm/°C in the range of -40-140 °C, while the output voltage noise is only 616 nV/sqrt(Hz)@1 Hz and the power-supply rejection ratio is 73 dB@10 kHz. The linear adjustment rate is 0.33 mV/V for supply voltages of 1.2-1.8 V at room temperature, the power consumption is only 107 µW at 1.8 V power supply voltage, and the chip active area is 0.21 × 0.28 mm2.

Comparative Study of Gravimetric Humidity Sensor Platforms Based on CMUT and QCM.

Humidity sensors with comprehensive performance are of great interest for industrial and environmental applications. Most sensors, however, have to compromise on at least one characteristic such as sensitivity, response speed, and linearity. This paper reports a gravimetric humidity sensor based on a capacitive micromachined ultrasonic transducer (CMUT) with exceptional all-around performance, and presents a side-by-side comparative investigation of two types of gravimetric humidity sensors for a better understanding of their characteristics and sensing mechanisms. For these purposes, a circular CMUT and a quartz crystal microbalance (QCM) with a resonance frequency of 10 MHz were designed and fabricated. Poly(vinyl alcohol) (PVA) was employed as the humidity sensing layer for its hydrophilicity and ease of film formation. The electrical properties of the sensors, including the electrical input impedances and quality factors, were characterized by a vector network analyzer. The relative humidity (RH) sensing performance of the sensors was evaluated and compared from RH levels of 11% to 97%. Both sensors exhibited good repeatability and low hysteresis. The unique microscale resonant structure of the CMUT humidity sensor contributed to a high sensitivity of 2.01 kHz/%RH, short response and recovery times of 8 s and 3 s, respectively, and excellent linearity (R2 = 0.973), which were far superior to their QCM counterparts. The underlying mechanism was revealed and discussed.

Reliability Evaluation Based on Mathematical Degradation Model for Vacuum Packaged MEMS Sensor.

Vacuum packaging is used extensively in MEMS sensors for improving performance. However, the vacuum in the MEMS chamber gradually degenerates over time, which adversely affects the long-term performance of the MEMS sensor. A mathematical model for vacuum degradation is presented in this article for evaluating the degradation of vacuum packaged MEMS sensors, and a temperature-accelerated test of MEMS gyroscope with different vacuums is performed. A mathematical degradation model is developed to fit the parameters of the degradation of Q-factor over time at three different temperatures. The results indicate that the outgassing rate at 85 °C is the highest, which is 0.0531 cm2/s; the outgassing rate at 105 °C is the lowest, which is 0.0109 cm2/s; and the outgassing rate at 125 °C is in the middle, which is 0.0373 cm2/s. Due to the different mechanisms by which gas was released, the rate of degradation did not follow this rule. It will also be possible to predict the long-term reliability of vacuum packaged MEMS sensors at room temperature based on this model.

Dual Band MEMS Directional Acoustic Sensor for Near Resonance Operation.

In this paper, we report on the design and characterization of a microelectromechanical systems (MEMS) directional sensor inspired by the tympana configuration of the parasitic fly Ormia ochracea. The sensor is meant to be operated at resonance and act as a natural filter for the undesirable frequency bands. By means of breaking the symmetry of a pair of coupled bridged membranes, two independent bending vibrational modes can be excited. The electronic output, obtained by the transduction of the vibration to differential capacitance and then voltage through charge amplifiers, can be manipulated to tailor the frequency response of the sensor. Four different frequency characteristics were demonstrated. The sensor exhibits, at resonance, mechanical sensitivity around 6 µm/Pa and electrical sensitivity around 13 V/Pa. The noise was thoroughly characterized, and it was found that the sensor die, rather than the fundamental vibration, induces the predominant part of the noise. The computed average signal-to-noise (SNR) ratio in the pass band is about 91 dB. This result, in combination with an accurate dipole-like directional response, indicates that this type of directional sensor can be designed to exhibit high SNR and selectable frequency responses demanded by different applications.

Research on the Application of MEMS Intelligent Sensor in Abnormal Monitoring of Metro Tunnel by Simplified Model Tests.

The current monitoring methods for tunnel structure deformation mainly focus on laser distance measurement, fiber Bragg grating, photogrammetry, electronic total station, hydrostatic leveling and so on. Compared with traditional monitoring methods, MEMS sensors have the advantages of small size, low cost, low energy consumption and high accuracy. In this paper, MEMS sensors are used for the continuous real-time intelligent monitoring of model tunnels, and the multi-point deployment of MEMS sensors is set up for the tunnel structure monitoring with the indicators of acceleration and inclination. The results demonstrated that ß-sample interpolation of the angles of the MEMS measurement points, and then integration of the overall displacements can better reflect the form of uneven settlement of the tunnel. For tunnel models with uneven settlement as the main deformation, the angle interpolation method allows the MEMS sensor to measure the vertical displacement more accurately and to determine the load mode to a certain extent. However, for tunnel models with global settlement as the main deformation, the results vary considerably from reality, as only the uneven part of the settlement can be measured using the angular interpolation method.

Research on Random Drift Model Identification and Error Compensation Method of MEMS Sensor Based on EEMD-GRNN.

Random drift error is one of the important factors of MEMS (micro-electro-mechanical-system) sensor output error. Identifying and compensating sensor output error is an important means to improve sensor accuracy. In order to reduce the impact of white noise on neural network modeling, the ensemble empirical mode decomposition (EEMD) method was used to separate white noise from the original signal. The drift signal after noise removal is modeled by GRNN (general regression neural network). In order to achieve a better modeling effect, cross-validation and parameter optimization algorithms were designed to obtain the optimal GRNN model. The algorithm is used to model and compensate errors for the generated random drift signal. The results show that the mean value of original signal decreases from 0.1130 m/s2 to -1.2646 × 10-7 m/s2, while the variance decreases from 0.0133 m/s2 to 1.0975 × 10-5 m/s2. In addition, the displacement test was carried out by MEMS acceleration sensor. Experimental results show that the displacement measurement accuracy is improved from 95.64% to 98.00% by compensating the output error of MEMS sensor. By comparing the GA-BP (genetic algorithm-back propagation) neural network and the polynomial fitting method, the EEMD-GRNN method proposed in this paper can effectively identify and compensate for complex nonlinear drift signals.

[Portable Monitoring Device for Motor Rehabilitation Equipment with Rotor Shaft].

A novel and portable device is proposed to monitor motor rehabilitation equipment, which can be mounted on most equipment with rotor shaft. The software of the device, whose main functions include equipment configuration, monitoring and statistical computation, is developed based on available sensor. The data collected by the device serve both department managers to learn the efficiency of the equipment, and physicians and therapists to understand the physical conditions of the patients who perform training exercises with the monitored rehabilitation equipment. About 2000 hours' monitoring has been carried out, and the experimental result indicates that the monitoring device is applicable to many types of motor rehabilitation equipment and achieves good monitoring accuracy. The data aggregated by the device can be used to evaluate the motor functions of the patients and make rehabilitation training plan. Besides, it is agreed by physicians and therapists that the device is easy-to-use, robust and has good real-time performance. The monitoring device thus holds the promise of boosting the development of digitalized rehabilitation medicine.

Thermal Conductivity Gas Sensor with Enhanced Flow-Rate Independence.

In this article, novel thermal gas sensors with newly designed diffusion gas channels are proposed to reduce the flow-rate disturbance. Simulation studies suggest that by lowering the gas flow velocity near the hot film, the maximum normalized temperature changes caused by flow-rate variations in the two new designs (Type-H and Type-U) are decreased to only 1.22% and 0.02%, which is much smaller than in the traditional straight design (Type-I) of 20.16%. Experiment results are in agreement with the simulations that the maximum normalized flow-rate interferences in Type-H and Type-U are only 1.51% and 1.65%, compared to 24.91% in Type-I. As the introduced CO2 flow varied from 1 to 20 sccm, the normalized output deviations in Type-H and Type-U are 0.38% and 0.02%, respectively, which are 2 and 3 orders of magnitude lower than in Type-I of 10.20%. In addition, the recovery time is almost the same in all these sensors. These results indicate that the principle of decreasing the flow velocity near the hot film caused by the two novel diffusion designs can enhance the flow-rate independence and improve the accuracy of the thermal conductivity as well as the gas detection.

Dual-Resonator-Based (DRB) and Multiple-Resonator-Based (MRB) MEMS Sensors: A Review.

Single-resonator-based (SRB) sensors have thrived in many sensing applications. However, they cannot meet the high-sensitivity requirement of future high-end markets such as ultra-small mass sensors and ultra-low accelerometers, and are vulnerable to environmental influences. It is fortunate that the integration of dual or multiple resonators into a sensor has become an effective way to solve such issues. Studies have shown that dual-resonator-based (DRB) and multiple-resonator-based (MRB) MEMS sensors have the ability to reject environmental influences, and their sensitivity is tens or hundreds of times that of SRB sensors. Hence, it is worth understanding the state-of-the-art technology behind DRB and MRB MEMS sensors to promote their application in future high-end markets.

Mechanical Energy Sensing and Harvesting in Micromachined Polymer-Based Piezoelectric Transducers for Fully Implanted Hearing Systems: A Review.

The paper presents a comprehensive review of mechanical energy harvesters and microphone sensors for totally implanted hearing systems. The studies on hearing mechanisms, hearing losses and hearing solutions are first introduced to bring to light the necessity of creating and integrating the in vivo energy harvester and implantable microphone into a single chip. The in vivo energy harvester can continuously harness energy from the biomechanical motion of the internal organs. The implantable microphone executes mechanoelectrical transduction, and an array of such structures can filter sound frequency directly without an analogue-to-digital converter. The revision of the available transduction mechanisms, device configuration structures and piezoelectric material characteristics reveals the advantage of adopting the polymer-based piezoelectric transducers. A dual function of sensing the sound signal and simultaneously harvesting vibration energy to power up its system can be attained from a single transducer. Advanced process technology incorporates polymers into piezoelectric materials, initiating the invention of a self-powered and flexible transducer that is compatible with the human body, magnetic resonance imaging system (MRI) and the standard complementary metal-oxide-semiconductor (CMOS) processes. The polymer-based piezoelectric is a promising material that satisfies many of the requirements for obtaining high performance implantable microphones and in vivo piezoelectric energy harvesters.

Miniaturized Hybrid Frequency Reader for Contactless Measurement Scenarios Using Resonant Surface Acoustic Wave Sensors.

Due to higher automation and predictive maintenance, it becomes more and more important to acquire as many data as possible during industrial processes. However, many scenarios require remote sensing since either moving parts would result in wear and tear of cables or harsh environments prevent a wired connection. In the last few years, resonant surface acoustic wave (SAW) sensors have promised the possibility to be interrogable wirelessly which showed very good results in first studies. Therefore, the sensor's resonance frequency shifts due to a changed measurand and thus has to be determined. However, up to now frequency reader systems showed several drawbacks like high costs or insufficient accuracy that blocked the way for a widespread usage of this approach in the mass market. Hence, this article presents a miniaturized and low cost six-port based frequency reader for SAW resonators in the 2.45 GHz ISM band that does not require an external calculation unit. It is shown that it can be either used to evaluate the scenario or measure the frequency directly with an amplitude or phase measurement, respectively. The performance of the system, including the hardware and embedded software, is finally shown by wired and contactless torque measurements.