Mechanical Sensors Research Articles

Microscale chemiresistive hydrogen sensors: Current status and recent developments

Hydrogen is known for its efficient combustion, abundant natural availability, and environmentally friendly characteristics. It is recognized as a promising energy source for the future and is already utilized in various industries, including petrochemicals, electronics, food processing, aerospace, and new energy vehicles. However, challenges arise in the storage and use of hydrogen owing to its tendency to leak, its potential for explosion within a specific concentration range of 4%–75%, and itslow ignition energy requirements. Consequently, there is a demand for hydrogen sensors capable of quickly and accurately detecting low levels of hydrogen leaks. Microelectromechanical systems-based chemiresistive hydrogen sensors offer advantages such as low cost, compact size, low energy consumption, and superior sensing performance, making them a major focus of recent research. This article provides a comprehensive overview and comparison of the sensing principles of various hydrogen sensors, including chemiresistive sensors, electrochemical sensors, thermocatalytic sensors, acoustic sensors, and mechanical sensors. Micro-chemiresistive hydrogen sensors exhibit high sensitivity, low cost, and ease of integration, making them highly promising for practical applications. In response to the challenges encountered in practical applications of chemiresistive hydrogen sensors, such as high operating temperatures and high power consumption, this review explores emerging trends in chemiresistive hydrogen sensor technology from the perspectives of novel materials and activation methods. Finally, it discusses the applications and potential further developments of chemiresistive hydrogen sensors.

Designing High-Sensitivity Mechanochromic Luminescent Materials Through Friction-Induced Crystallization Strategy.

Despite recent significant breakthroughs in novel mechanochromic luminescent (ML) materials, developing a high-sensitivity ML material is still challengeable. Herein, a "friction-induced crystallization" strategy is proposed to realize highly sensitive transformations of luminescent signal, through an integration of polymeric chains and an aggregation-sensitive luminescent core, which act as mechanical sensors and fluochromic actuators, respectively. The coupling of these two components enables the material to crystallize in response to shear friction, thereby exhibiting blue-shift fluorescence due to a more restricted relaxation pathway. This study underscores a high-sensitivity ML material based on the precise regulation of molecular-scale motions, and also expands the scope and potential of ML materials toward user-friendly, interactive wearable devices.

Vibrational squeezing via spin inversion pulses

Magnetic resonance force microscopy (MRFM) describes a range of approaches to detect nuclear spins with mechanical sensors. MRFM has the potential to enable magnetic resonance imaging with near-atomic spatial resolution, opening up exciting possibilities in solid state and biological research. In many cases, the spin-mechanics coupling in MRFM is engineered with the help of periodic radio frequency pulses. In this paper, we report that such pulses can result in unwanted parametric amplification of the mechanical vibrations, causing misinterpretation of the measured signal. We show how the parametric effect can be canceled by auxiliary radio frequency pulses or by appropriate post-correction after careful calibration. Future MRFM measurements may even make use of the parametric amplification to reduce the impact of amplifier noise.

Recent Patents on Piano Tuning System

Piano tuning plays a very important role in piano playing and music education. Regular tuning can prolong the service life of the piano, improve the sound saturation, and make the sound more concentrated and full. Automated mechanical equipment, controllers, sensors, and other equipment are used to automatically tune pianos to solve the problems of human error, overtone resonance interference, string breakage, etc., occurring in manual tuning. To solve the above problems, based on the synthesis of related patents and research results, the patents on automatic tuning system and methods for modulating piano are introduced in detail. An automatic tuning system can replace manual tuning, simplify the complex process of tuning, and consider the problem of overtone interference caused by the vibration of the strings to improve the accuracy of tuning; it can, at the same time, perform real-time monitoring of the string tension, protecting the strings from breaking due to tuning. The automatic tuning system has a simple structure and a high degree of automation; its method is highly accurate and it can replace professional tuning technicians, being highly innovative and widely practical.

Exploring the interplay between zinc‐induced protein dyshomeostasis and mitochondrial dysfunction using viscosity‐sensitive sensor

AbstractMitochondria are crucial sites for protein quality control within cells. When mitochondrial stress is triggered by protein misfolding, it can accelerate abnormal protein aggregation, potentially inducing various diseases. This study developed a cascade‐responsive sensor, named AggHX, to monitor the microenvironment of protein aggregation induced by zinc (II) ions and the accompanying mitochondrial dysfunction. The AggHX consists of two key components: (1) A Zn2+ recognition group for triggering a fluorescent enhance response, and (2) a near‐infrared BODIPY scaffold that detects viscosity changes in cell aggregation via HaloTag. This sensor's mechanism of action is elucidated through photochemical and biochemical characterizations. To further investigate the relationship between protein aggregation and mitochondrial homeostasis, we employ fluorescence lifetime imaging microscopy to assess viscosity changes in protein aggregates under intracellular Zn2+ stress. This research provides insights into the dynamic behavior and spatial distribution of protein aggregates and mitochondria, contributing to a deeper understanding of their physiological roles in cellular processes and potential implications in disease pathology.

Tumor cell-intrinsic Piezo2 drives radioresistance by impairing CD8+ T cell stemness maintenance.

Changes in mechanosensitive ion channels following radiation have seldom been linked to therapeutic sensitivity or specific factors involved in antitumor immunity. Here, in this study, we found that the mechanical force sensor, Piezo2, was significantly upregulated in tumor cells after radiation, and Piezo2 knockout in tumor cells enhanced tumor growth suppression by radiotherapy. Specifically, loss of Piezo2 in tumor cells induced their IL-15 expression via unleashing JAK2/STAT1/IRF-1 axis after radiation. This increase in IL-15 activates IL-15Rα on tumor-infiltrating CD8+ T cells, thereby leading to their augmented effector and stem cell-like properties, along with reduced terminal exhausted feature. Importantly, Piezo2 expression was negatively correlated with CD8 infiltration, as well as with radiosensitivity of patients with rectum adenocarcinoma receiving radiotherapy treatment. Together, our findings reveal that tumor cell-intrinsic Piezo2 induces radioresistance by dampening the IRF-1/IL-15 axis, thus leading to impaired CD8+ T cell-dependent antitumor responses, providing insights into the further development of combination strategies to treat radioresistant cancers.

Methodology for assessing health risk behaviours among undergraduates in Sri Lanka: a literature review

It is imperative to learn more about the distinctive Health-Risk-Behaviours (HRB) that are culturally acceptable in the lower-resource settings. Therefore, the objective of this review is to identify tools measuring HRBs among undergraduates in Sri Lankan context. A literature review was conducted both manually and electronically through Google Scholar and Hinari and PubMed databases. Self-administered Questionnaires were used in National Surveys and individual studies. Youth-Risk Behaviour-Surveillance-System was the most commonly used comprehensive HRB assessment tool. The National College Health Risk Behaviour Survey, National College Health Assessment were other widely used instruments. There are few validated tools and multiple short scales or items combined like Health-Promotion-Lifestyle[1]Profile, Health-Behaviour-Questionnaire or Inventory, DOSPERT scale, Adolescent-Health-Attitude and Behaviour[1]Survey, Healthy-Lifestyle-Scale, Health-Behaviour-Scale and Health-Behaviour-Checklist. Objective methods for assessing high-risk behaviors (HRBs) include measures of personality and aggression, which have been used to critically quantify violence and accidental injury, as well as biochemical tests for alcohol, nicotine, and other drug use, mechanical or computer sensors, and biological tests such as urinary cultures for sexual risk behaviours. These tools had focused only on specific aspects of HRBs among undergraduates in Western countries and were not comprehensive assessments of HRBs of undergraduates in lower-resource settings. It was evident that there has been a deficit of choices for the adaptation of an HRB tool for undergraduates in lower-resource-settings and a new tool to be developed for Sri Lankan context.

Sowing Depth Monitoring System for High-Speed Precision Planters Based on Multi-Sensor Data Fusion

High-speed precision planters are subject to high-speed (12~16 km/h) operation due to terrain undulation caused by mechanical vibration and sensor measurement errors caused by the sowing depth monitoring system’s accuracy reduction problems. Thus, this study investigates multi-sensor data fusion technology based on the sowing depth monitoring systems of high-speed precision planters. Firstly, a sowing depth monitoring model comprising laser, ultrasonic, and angle sensors as the multi-sensor monitoring unit is established. Secondly, these three single sensors are filtered using the Kalman filter. Finally, a multi-sensor data fusion algorithm for optimising four key parameters in the extended Kalman filter (EKF) using an improved sparrow search algorithm (ISSA) is proposed. Subsequently, the filtered data from the three single sensors are integrated to address the issues of mechanical vibration interference and sensor measurement errors. In order to ascertain the superiority of the ISSA-EKF, the ISSA-EKF and SSA-EKF are simulated, and their values are compared with the original monitoring value of the sensor and the filtered sowing depth value. The simulation test demonstrates that the ISSA-EKF-based sowing depth monitoring algorithm for high-speed precision planters, with a mean absolute error (MAE) of 0.083 cm, root mean square error (RMSE) of 0.103 cm, and correlation coefficient (R) of 0.979 achieves high-precision monitoring. This is evidenced by a significant improvement in accuracy when compared with the original monitoring value of the sensor, the filtered value, and the SSA-EKF. The results of a field test demonstrate that the ISSA-EKF-based sowing depth monitoring system for high-speed precision planters enhances the precision and reliability of the monitoring system when compared with the three single-sensor monitoring values. The average MAE and RMSE are reduced by 0.071 cm and 0.075 cm, respectively, while the average R is improved by 0.036. This study offers a theoretical foundation for the advancement of sowing depth monitoring systems for high-speed precision planters.

Design and fabrication of an accelerometer structure using glass fiber reinforced epoxy resin for seismic P-wave early warning

Earthquake early warning represents a vital and effective strategy for mitigating earthquake-related losses. Among the existing sensors for earthquake early warning, MEMS accelerometers involve complex manufacturing process, leading to high research and development costs and long cycles. Traditional mechanical sensors are bulky, costly, hence not suitable for large-scale, high-density monitoring network. In this study, a low-cost accelerometer using glass fiber reinforced epoxy resin is proposed for longitudinal seismic waves (P-waves) early warning. Both theorical and finite element analysis are carried out to define and optimize the initial structure of the accelerometer. A pre-deformation structure is implemented into the spring suspension system to offset the influence of the sensor’s gravity, so that a self-balanced state in the direction of gravity is achieved. The simulation results show that the accelerator can achieve an eigenfrequency of 12.67 Hz, a Q-value of 49.2, and mechanical thermal noise of 1.72×10-9g/Hz. Based on the simulation results, the accelerometer’s structure is processed using a computer numerical control (CNC) milling machine, and tested by collecting the voltage signals when the accelerometer’s structure undergoes an impact in the direction of the gravity. The measured eigenfrequency is around 10 Hz, and the signal attenuation rate closely aligns with the simulation results. The accelerometer structure has dimensions within 6 cm and a relatively simplified manufacturing process, making it a cost-effective solution for the development of large-scale, high-density earthquake early warning systems.

Active flux based adaptive and non-adaptive observer for sensorless interior permanent magnet synchronous machine drive

The use of the interior permanent magnet synchronous machine (IPMSM) drive has profoundly increased in a large number of applications due to numerous advantages. Owing to the disadvantages of mechanical sensors, sensorless control techniques are employed to enhance the performance of the IPMSM drive by removing the effect of noise and gain drift due to the sensor, increasing reliability, cost saving, and reducing overall size. This article presents the comparative analysis between the adaptive observer and non-adaptive extended electromotive force (EEMF) observer based on the active flux concept in a stationary reference frame (α–β). Moreover, the effect of slot harmonics and non-sinusoidal distribution of rotor flux is present in the three-phase IPMSM, this problem is considered as the control system disturbances in this article. Due to the non-sinusoidal distribution of flux and slot harmonics, the observer structure in the rotating reference frame (d–q) fails to estimate at the low-speed operation range. Comparative analysis between adaptive and non-adaptive observer structures is provided for a wide speed range. The effectiveness of the observer structures is examined using the classical field-oriented control scheme. In the end, simulation and experimental results are demonstrated to validate the performance of the sensorless control scheme using the adaptive and non-adaptive observer structures for the three-phase IPMSM drive setup.

Sensor‐Embedded Muscle for Closed‐Loop Controllable Actuation in Proprioceptive Biohybrid Robots

Biohybrid robots are soft robots that exploit unique characteristics of biological cells and tissues for motion generation. Skeletal muscle tissue‐based bioactuators respond to externally applied stimuli, such as electrical fields. However, current bioactuation systems rely on open‐loop control strategies that lack knowledge of the actuator's state. The regulation of output force and position of biohybrid robots requires self‐sensing control systems that combine bioactuators with sensors and control paradigms. Herein, a soft, fiber‐shaped mechanical sensor based on a piezoresistive composite is proposed that efficiently integrates with engineered skeletal muscle tissue and senses its contracting states in a cell culture environment in the presence of applied electrical fields. After testing the sensor's insulation and biocompatibility, its sensitivity for typical strains (<1%) is characterized, and its ability is proven to detect motions from contractile skeletal muscle tissue constructs. Finally, it is shown that the sensor response can feed an autonomous control system, thus demonstrating the first proprioceptive biohybrid robot that senses and responds to its contraction state. In addition to inspiring implantable systems, biomedical models, and other bioelectronic devices, the proposed technology will confer biohybrids with decisional autonomy, thus driving the paradigm shift between bioactuators and intelligent biohybrid robots.

Study On the Mechanical Device, Control System, and Sensor of Biaxial Actuated Artificial Anal Sphincter Actuator for Treating Fecal Incontinence

Abstract Artificial anal sphincter, Actuator, Mechanical device, Control system, Pressure sensor

Moisture-wicking Janus-structure electronic knitted fabric for multimodal wearable mechanical sensing

Multimodal mechanical sensors that can recognize bending direction and exhibit excellent wearable performance are crucial for the fields of physical exercise and healthcare. This study fabricated a knitted multimodal sensor with porous and wetting gradients using hydrophobic core-spun yarns and hydrophilic CNT/WPU conductive yarns in plain stitch and partial tuck stitch. The electronic fabric demonstrated reliable strain sensing unaffected by compression disturbances and displayed a selective response to directional bending. The sensor exhibits a 50% sensing range with a GFmax of −4.41 during strain deformation detection. In terms of bending detection, one side achieves a 10% sensing range (−51.68°) with a GFmax of −6.89, while the other side achieves a 20% sensing range (73.72°) with a GFmax of 8.64. Encouragingly, deep learning incorporated with the sensor achieves an impressive recognition rate of 98.16% for mixed signals with different force levels. Furthermore, it possesses excellent wearable properties such as breathability, moisture permeability, perspiration remove, quick-drying and washability, suitable for applications like activity monitoring, medical rehabilitation evaluation, and gesture recognition. This work presents a promising pathway for the advancement of next-generation multifunctional electronic textiles.

Wearable photonic crystal double network hydrogel sensor based on structural color analysis

Abstract This paper introduces a high-toughness photonic crystal (PhCs) dual-network hydrogel sensor designed for mechanical sensing, which enables quantitative analysis of structural color using the HSB color space. The hydrogel achieves a maximum tensile strain of 250% under a tensile stress of 3.5 MPa, thanks to its dual-network structure comprising a covalently cross-linked polyacrylamide (PAM) network and an ionically cross-linked sodium alginate (SA) network. At an 80% tensile strain, the PAM-SA photonic crystal hydrogel displays a blue shift in the bandgap wavelength of over 130 nm and demonstrates a sensitivity of 1.69 nm/%. The analysis of the force-induced color change in the PAM-SA photonic crystal hydrogel utilized both RGB and HSB color spaces. In the HSB color space, the hue component (H) exhibited a strong linear correlation with strain (R2>0.95), indicating the feasibility of quantitative structural color analysis using HSB. As a wearable sensor, the PAM-SA photonic crystal hydrogel precisely detects human motion via bandgap displacement (R2=0.989) and structural color change (R2=0.978). The PAM-SA PhCs hydrogel, featuring easily accessible color information and high sensitivity, has broad potential applications in wearable devices and mechanical sensors.

Development of an Underwater Adaptive Penetration System for In Situ Monitoring of Marine Engineering Geology.

In recent years, offshore wind farms have frequently encountered engineering geological disasters such as seabed liquefaction and scouring. Consequently, in situ monitoring has become essential for the safe siting, construction, and operation of these installations. Current technologies are hampered by limitations in single-parameter monitoring and insufficient probe-penetration depth, hindering comprehensive multi-parameter dynamic monitoring of seabed sediments. To address these challenges, we propose a foldable multi-sensor probe and establish an underwater adaptive continuous penetration system capable of concurrently measuring seabed elevation changes and sediment pore water pressure profiles. The reliability of the equipment design is confirmed through static analysis of the frame structure and sealed cabin. Furthermore, laboratory tests validate the stability and accuracy of the electrical and mechanical sensor measurements. Preliminary tests conducted in a harbor environment demonstrate the system's effectiveness.

Essential Roles of PIEZO1 in Mammalian Cardiovascular System: From Development to Diseases.

Mechanical force is the basis of cardiovascular development, homeostasis, and diseases. The perception and response of mechanical force by the cardiovascular system are crucial. However, the molecular mechanisms mediating mechanotransduction in the cardiovascular system are not yet understood. PIEZO1, a novel transmembrane mechanosensitive cation channel known for its regulation of touch sensation, has been found to be widely expressed in the mammalian cardiovascular system. In this review, we elucidate the role and mechanism of PIEZO1 as a mechanical sensor in cardiovascular development, homeostasis, and disease processes, including embryo survival, angiogenesis, cardiac development repair, vascular inflammation, lymphangiogenesis, blood pressure regulation, cardiac hypertrophy, cardiac fibrosis, ventricular remodeling, and heart failure. We further summarize chemical molecules targeting PIEZO1 for potential translational applications. Finally, we address the controversies surrounding emergent concepts and challenges in future applications.

Online Handwriting Recognition Method with a Non-Inertial Reference Frame Based on the Measurement of Linear Accelerations and Differential Geometry: An Alternative to Quaternions.

This work describes a mathematical model for handwriting devices without a specific reference surface (SRS). The research was carried out on two hypotheses: the first considers possible circular segments that could be made during execution for the reconstruction of the trace, and the second is the combination of lines and circles. The proposed system has no flat reference surface, since the sensor is inside the pencil that describes the trace, not on the surface as in tablets or cell phones. An inertial sensor was used for the measurements, in this case, a commercial Micro-Electro Mechanical sensor of linear acceleration. The tracking device is an IMU sensor and a processing card that allows inertial measurements of the pen during on-the-fly tracing. It is essential to highlight that the system has a non-inertial reference frame. Comparing the two proposed models shows that it is possible to construct shapes from curved lines and that the patterns obtained are similar to what is recognized; this method provides an alternative to quaternion calculus for poorly specified orientation problems.

Research on the Measurement and Estimation Method of Wheel Resistance on a Soil Runway

In view of the fact that there is no suitable measurement method for tire driving resistance during the operation of a transporter on soil pavement, this paper proposes a simple measurement and estimation method for driving resistance on a soil runway based on the Bekker settlement model. In this paper, using the driving resistance equation based on the Bekker model, a simplified equation of driving resistance related to mass is proposed. Using the principle of kinematics, a simple test device for driving resistance was developed, and the test data of the test device were used to determine the parameters of the driving resistance estimation equation of the road surface. The driving resistance of the wheel running state was tested by the simulated aircraft load loading vehicle through the mechanical sensor, and the consistency between the theoretical value and the actual value of the driving resistance value under 8T, 10T, and 12T loads was verified. This research shows that the test method and estimation method have low cost and good accuracy and are suitable for wide promotion. The results of this paper provide a new idea and method for estimating the driving resistance of a wheel when a transporter runs on a soil road and also provide a reference for designing the length of a soil runway.

Damage-related imbalance identification for UAV composite propeller blades based on bidirectional temporal convolutional network and a flexible sensing system

Abstract Damage to the composite propeller blades could lead to rotational imbalance, which seriously affects the operational safety of unmanned aerial vehicles (UAVs), therefore, a novel method combining the Teager energy operator (TEO) and bidirectional temporal convolutional network (BiTCN) is proposed for detecting, localizing, and quantifying the damage-related imbalance in the blades. A flexible sensing system that contains Micro electro mechanical sensor accelerometers, signal conditioning, and wireless transmission is integrated with the composite propeller for in-situ signal acquisition of the propeller blades. TEO is applied to demodulate and enhance the pulse compositions in vibration signals and singular value decomposition (SVD) is employed to suppress random noise, resulting in denoised Teager energy spectrums for model input. Temporal convolutional network (TCN) has been widely used in sequence signal modeling because the causal dilated convolution could learn the context information of sequence signals while maintaining the advantages of parallel computing. To fully extract the signal features, BiTCN models are established to learn both the forward and backward signal features. Experimental verification results show that the proposed method detects the existence of imbalance with 100% accuracy, and the accuracies of localization and quantization are 99.65% and 98.61%, respectively, which are much higher than those of the models with the original signal as input. In addition, compared with the other four different algorithms, BiTCN is superior in terms of convergence speed and prediction accuracy.

Edge Computing and Fault Diagnosis of Rotating Machinery Based on MobileNet in Wireless Sensor Networks for Mechanical Vibration.

With the rapid development of the Industrial Internet of Things in rotating machinery, the amount of data sampled by mechanical vibration wireless sensor networks (MvWSNs) has increased significantly, straining bandwidth capacity. Concurrently, the safety requirements for rotating machinery have escalated, necessitating enhanced real-time data processing capabilities. Conventional methods, reliant on experiential approaches, have proven inefficient in meeting these evolving challenges. To this end, a fault detection method for rotating machinery based on mobileNet in MvWSNs is proposed to address these intractable issues. The small and light deep learning model is helpful to realize nearly real-time sensing and fault detection, lightening the communication pressure of MvWSNs. The well-trained deep learning is implanted on the MvWSNs sensor node, an edge computing platform developed via embedded STM32 microcontrollers (STMicroelectronics International NV, Geneva, Switzerland). Data acquisition, data processing, and data classification are all executed on the computing- and energy-constrained sensor node. The experimental results demonstrate that the proposed fault detection method can achieve about 0.99 for the DDS dataset and an accuracy of 0.98 in the MvWSNs sensor node. Furthermore, the final transmission data size is only 0.1% compared to the original data size. It is also a time-saving method that can be accomplished within 135 ms while the raw data will take about 1000 ms to transmit to the monitoring center when there are four sensor nodes in the network. Thus, the proposed edge computing method shows good application prospects in fault detection and control of rotating machinery with high time sensitivity.