Pressure Sensor Research Articles

Correlation of residual stress on piezoresistive properties of boron-doped diamond films

To investigate the influence of residual stress on the piezoresistive behavior of boron-doped diamond (BDD) films, BDD films with varying doping concentrations were synthesized using a hot-filament chemical vapor deposition (HFCVD) system on silicon and diamond substrates. The relationship between the surface morphology, structural composition, resistivity, and piezoresistive properties of BDD electrodes was examined, along with an in-depth analysis of the impact of boron doping level on these properties. The microstructure and bonding state of the BDD films were characterized using scanning electron microscopy (SEM), atomic force microscopy (AFM), and Raman spectroscopy. The piezoresistive properties of the BDD films were evaluated employing a cantilever beam bending method. Our findings demonstrate that the level of boron doping not only affects resistivity but also directly influences residual stress in BDD films, both factors having an impact on their piezoresistive properties. Despite significantly larger grain sizes observed in BDD films grown on diamond substrates compared to those grown on silicon substrates at identical boron doping concentrations, they exhibit consistent variations in residual stress and gauge factor (GF) values. With increasing doping levels, the absolute residual stress decreases initially before increasing again; moreover, at 2000 ppm doping level, it transitions from compressive to tensile stress. The GF value is closely associated with both the magnitude and type of residual stress. By utilizing a doping concentration of 2000 ppm for growth on diamond substrates, we achieved a peak GF value of 257 for BDD films which highlights their promising potential for pressure sensor applications.

Wood derived conductive aerogel with ultrahigh specific surface area and exceptional mechanical flexibility for pressure sensing

Conductive nanocellulose aerogels possess durability and sensitivity by using as piezoresistive sensors. Those aerogels are mostly prepared via a “bottom-up” strategy by using nanocellulose or dissolved and regenerated cellulose as raw materials. However, there is a pressing need to further enhance their sustainability and economic viability. To alleviate these issues, cellulose aerogel can alternatively be made via a “top-down” technique that entails the direct removal of contaminants from naturally porous biomass. Herein, conductive aerogels with exceptional mechanical flexibility and ultrahigh specific surface area were created. Delignified wood was partially dissolved and regenerated to created nanocellulose fibrils with spider web structure inside the pores of wood. After modification with silane coupling agent and Carbon Nanotubes (CNTs), flexible conductive wood aerogel (C-WA) were obtained. The specific surface area of C-WA was as high as 333.82 m2/g, which was 45 times of that of wood. C-WA could maintain its good resilience even after 1000 cycles, showing high compression resilience and excellent fatigue resistance (stress retention rate was 87.22 %, height retention rate was 99.5 %). The utilization of C-WA as a pressure sensor demonstrated remarkable attributes, including ultra-fast response and stability within the designated range. Furthermore, it exhibited exceptional sensing performance even after enduring 2000 stable cycles. The system is capable of real-time, accurate, and efficient detection of human motion signals, showcasing immense potential for future applications in wearable electronic products.

Effect of boron doping levels on the piezoresistive properties of boron-doped diamond films prepared by HFCVD

The exceptional piezoresistive properties of boron-doped diamond (BDD) films render them highly promising for pressure sensor applications. These properties are closely associated with the doping concentration, crystal structure, and substrate material. In this study, BDD films with varying boron concentrations (B/C=500-8000 ppm) were fabricated on silicon and diamond substrates using hot-filament chemical vapor deposition (HFCVD) equipment. The relationship between surface grain characteristics, grain boundaries, and gauge factor (GF) of BDD films deposited on different substrates was systematically examined. The concentration of boron doping significantly influenced the surface morphology, electrical resistivity, and GF of the BDD films. The grain size of BDD films initially increased as the boron doping concentration was raised from 500 ppm to 2000 ppm; however, it subsequently decreased when the boron doping content exceeded 2000 ppm. Moreover, we found that the piezoresistive impact of a BDD film is strongly correlated to its grain size; larger grain sizes result in higher GF values. Notably, compared to those on silicon substrates, the BDD films deposited on diamond substrates exhibited superior piezoresistive characteristics. Specifically at a doping concentration of 2000 ppm, a GF as high as 284 was achieved for the BDD film on a diamond substrate compared to only 140 for that on a silicon substrate.

254 - The development and validation of a Portable Urinary Flowmeter Based on Flexible Pressure Sensor and Micro-rotor

254 - The development and validation of a Portable Urinary Flowmeter Based on Flexible Pressure Sensor and Micro-rotor

A triboelectric flexible pressure sensors based on synergistic enhancement of material and structure for robot behavior perception

A triboelectric flexible pressure sensors based on synergistic enhancement of material and structure for robot behavior perception

Accuracy assessment of recent global ocean tide models in coastal waters of the European North West Shelf

The accuracy of global ocean tide models is assessed in coastal waters of the European North West Shelf to ascertain where higher resolution local (forecast) models are most needed for geophysical and navigational applications, and which global models are most suitable for providing boundary conditions for regional and local tide models. Five recent global ocean tide models (FES2014b, EOT20, TPXO9-atlas-v5, GOT4.10c, and DTU16) are considered, with the models first compared by interpolating them onto common grids and computing the mean absolute deviation at each grid point. Coastline tide gauge and offshore bottom pressure sensor data were collated from several sources to give a total of 279 observation sites for evaluating model accuracy, including observational values from 137 locations that have not previously been released and have therefore not been assimilated into any of the global models tested. The residual errors between each model’s predicted phasor and the corresponding observed phasor were calculated at each observation location, and quantified using the root mean square (RMS) and median absolute residual (MAR) for the eight tidal constituents M2, S2, N2, O1, K1, K2, P1, and Q1. To avoid RMS values being biased by observation point density, a Voronoi-weighted RMS based on the water area of the Voronoi polygon about each observation location was also developed and used. Four zones were defined based on ocean depth to gauge model performance, and model inaccuracy is again demonstrated in near-shore regions. Seven further zones were defined based on geographical areas, which reveals inhomogeneity among the global models. The smallest overall root sum square (RSS) RMS error across all eight constituents arises with FES2014b, although TPXO9-atlas-v5 has the best performance when using the MAR and Voronoi-weighted RMS metrics. Using only the 137 observation sites that have not been assimilated by any model and therefore provide an independent accuracy assessment, FES2014b exhibits the smallest errors at the coastline, with an RSS RMS of 24.46 cm. All models exhibit larger errors with the 137 independent observation sites than with all 279 observation sites, with an average overall increase in RSS RMS error of 12%, and an increase of 30% for coastline tide gauges, highlighting the need for local model development in these areas.

A predictive walking energy model based on gait phase with suspended backpack

Walking with heavy loads is a common task in military affairs and daily life. Considering that the shoulder and leg muscles fatigue will be caused during walking, which will affect the walking endurance and physical health. However, the suspended backpack is found to improve the energy efficiency of walking with a load. In this study, A lightweight suspended backpack is designed and proposing a model for estimating the metabolic cost of a suspended backpack based on gait phase. In this study, four inertial measurement units (IMUs) are fixed on the thigh and shank, six flexible pressure sensors are mounted on the soles of the feet and shoulders, respectively. The gait is defined as four successive phases. For each phase, the muscle tension is solved based on the muscle moment balance theory. Based on the phase segmentation method, the ECCF index is calculated by adding the gait phase constraint and backpack data calculation into the energy prediction model, and the relatively accurate data is obtained. In addition, In order to study the effects of the suspended backpack with different parameters on the cost metabolism, gait phase and biomechanics, the subjects need to carry the same load of 16.5 kg to walk 400 m at the different speeds, respectively. A group of seven healthy subjects in the same walking condition need to conduct two experiments: suspended backpack work (SB) and ordinary backpack (OB). The experimental results show that the suspended backpack can reduce plantar pressure and shoulder pressure in the SB condition. And at the speed of 5.0 km/h, ground reaction force (GRF) and shoulder reaction force (SRF) were reduced by 11.59 % and 13.22 % in the SB condition compared to the OB condition, respectively.

Estimation of gas hold-up in bubble columns using wall pressure fluctuations and machine learning

We present a novel approach to estimate gas hold-up in bubble columns using wall pressure fluctuations – without requiring knowledge of operating flow regime, column configuration or physical properties. The approach uses machine learning and pressure fluctuations acquired from a wall mounted pressure sensor. The approach is validated by carrying out experiments with different physical properties of liquid (deionised water, deionised water – alcohol [ethanol, propanol and butanol] mixture up to 2 % alcohol, deionised water – glycerine (30 %), tap water). The majority of the data was obtained with 0.1 m diameter bubble column. Data with tap water in this column was obtained for four different spargers. Data with tap water was also obtained for 0.15 m and 0.33 m diameter bubble columns. Gas velocity was varied up to 0.1 m/s. The covered physical properties, sparger configurations, column diameter and gas velocities span different flow regimes. An artificial neural network (ANN) based model was developed to relate characteristics of wall pressure fluctuations and superficial gas velocity to the gas hold-up. The approach was validated by comparing predicted results with the experimental data unseen by the ANN model. The approach demonstrates the feasibility of using wall pressure fluctuations with machine learning to estimate key characteristics without prior knowledge of column configuration, flow regimes or physical properties.

Reduced Graphene Oxide-Based Flexible Pressure Sensors With Multiple Structural Designs

Reduced Graphene Oxide-Based Flexible Pressure Sensors With Multiple Structural Designs

Influence of tip air injection on the unsteady blade force under circumferential pressure distortion

In order to further understand the impact of tip air injection on compressor stability under circumferential pressure distortion, a series of experimental studies on tip air injection under uniform and circumferential distortion were conducted on a low-speed single rotor axial flow compressor. The unsteady measurements were carried out by use of microsensors and strain gauges bonded on the rotor blade surfaces, and a collection of dynamic pressure sensors installed on the casing wall. Results indicate that with the increase in the injected momentum ratio, the load at the leading edge near the rotor blade tip obviously decreases, thereby delaying the stall. By comparing the pressure distribution on the rotor blade surface under different injected momentum ratios, tip air injection can effectively suppress flow separation at the blade tip to enhance flow capability. In addition, it can also weaken the fluctuation of tip leakage flow and reduce the dynamic stress similar to natural vibration near the blade root both in stationary and rotating coordinate systems. Under the condition of inlet circumferential distortion, tip air injection can significantly reduce the load at the distorted region and suppress the low-frequency disturbances measured on the rotor blade surface caused by inlet distortion. In addition, the dynamic stress near the blade root can also be effectively reduced by tip air injection. It implies that tip air injection can both extend the stall margin and reduce blade vibration when suffering from circumferential distortion; thus, the compressor can safely operate.

Ultra-thin and sensitive pressure sensor based on MXene/PVDF-HFP composite fiber TENG for self-diagnosis of ligament injuries

Early screening of anterior cruciate ligament (ACL) injuries using wearable devices enables timely intervention and reduces the risk of chronic complications. However, the subtle biomechanical signals of the ACL pose challenges to the baseline stability, sensitivity and skin adherence of wearable devices. Here, we present a TENG based on MXene/PVDF-HFP composite fibers for ACL self-diagnosis, leveraging the inherent advantages of TENG in baseline stability. The fiber-network structure prepared by electrospinning ensures that the TENG is ultra-thin and flexible to wear. The addition of MXene significantly increases the relative permittivity of the electrode materials, and re-modulates the crystalline phase (α and β). Consequently, the peak-to-peak open circuit voltage of the TENG reaches 160 V under a pressure of 10 N. It can respond to pressures as low as 0.01 N, allowing it to distinguish subtle relative displacements between the tibia and the knee. Integrated with a mobile app, a self-diagnostic system capable of detecting varying degrees of ACL injury has been developed. We expect this approach to facilitate the early detection and timely treatment of ACL injuries.

HCNT/AgNPs/PVA/PAM hydrogel-based flexible pressure sensor for physiological monitoring

HCNT/AgNPs/PVA/PAM hydrogel-based flexible pressure sensor for physiological monitoring

Development of the neutral gas diagnostic system for neutral pressure measurements and gas analysis on the SPARC tokamak.

A suite of plasma diagnostics will be installed on the SPARC tokamak to allow for real-time plasma control, an investigation of high-field tokamak physics, and to de-risk the design of ARC, a compact fusion power plant with the aim to supply electricity to the grid. Among these diagnostics is the neutral gas diagnostics system (NTGS), a set of pressure sensors and gas analyzers used to monitor neutral pressure and gas composition for plasma control, optimization of wall conditioning, and helium ash removal, among other measurement functions linked to operational and scientific research needs. While reliable measurements of neutral pressure and gas composition have been fielded on existing magnetic-confinement fusion devices, SPARC represents a step increase in challenge due to its larger power density, higher field, high vacuum vessel bake temperatures, and higher neutron flux environment, as well as a step decrease in the accessibility for maintenance of in-vessel sensors. Multiple sensor types will be employed to have defense-in-depth and mitigate common failure modes. The NTGS system is currently progressing through final design, working to close out decisions using prototyping and analysis, and then moving on to procuring sensors for assembly and installation on SPARC. This paper outlines the current status of the system design and the diagnostic requirements that motivate neutral gas measurements on SPARC, as well as highlights the planned prototyping activities.

A fault identification method for Fuel cell engine air supply subsystem pressure sensor based on Random forest

A fault identification method for Fuel cell engine air supply subsystem pressure sensor based on Random forest

Calibration modeling of drilling fluid rheological parameters in variable temperature environment based on support vector machine.

Traditional measurement of drilling fluid rheological parameters suffers from significant lag due to the inability of the instruments to promptly capture real-time parameters of the drilling fluid. These measurement models are typically constructed based on fixed temperature conditions and empirical formulas, rendering them inadequate for complex temperature gradient environments. Consequently, this limitation results in increased prediction errors, severely compromising the precise monitoring of drilling fluid performance. Aiming at the problems of low accuracy and poor stability of drilling fluid measurements under variable temperature conditions, a support vector machine-based calibration model for drilling fluid rheological parameters in a variable temperature environment is proposed in this paper. First, the measurement principle of the double-tube differential pressure rheology real-time measurement device is analyzed. The relationship between shear stress and shear rate is then established using differential pressure sensor and flow rate data. Utilizing the gray wolf optimization algorithm to optimize the kernel function weights and parameters, an SVM-based calibration model for predicting drilling fluid rheology correction parameters is constructed. Finally, a real-time monitoring platform for drilling fluid is developed. Experimental results show that the maximum relative errors for the predictions of apparent viscosity, plastic viscosity, and yield point are within ±5%, with coefficients of determination (R2) all greater than 0.95. These results validate the effectiveness of the proposed method in accurately monitoring the rheological performance of drilling fluids.

Relationship Between Resultant Force Vector Acting on Human Organs From Food Bolus and the Bolus Configuration During Swallowing Using Numerical Swallowing Simulation With Moving Particle SimulationMethod.

This study investigates the forces exerted on organs during swallowing, specifically focusing on identifying forces other than those resulting from direct organ contact. Using a swallowing simulator based on the moving particle method, we simulated the swallowing process of healthy individuals upon the ingestion of thickened foods, which were simulated as shear-thinning flow without yield stress. We extracted the resultant force vectors acting on the organs and shape of the bolus at each time interval. The simulation results confirmed that the bolus originates from tongue movement and is transferred between the oral cavity and pharynx, with each organ's coordinated movements with the tongue occurring at their respective positions, as indicated by the balance of the resultant force vectors. Utilizing the information about the resultant force vectors obtained through simulations, we calculated the physical parameters of impulse, energy, and power. The variations in these physical parameters were aligned with the behaviors of both the biological system and the food bolus during swallowing. The force values calculated from the simulations closely approximate the theoretical values. Furthermore, the forces calculated from the simulations were relatively smaller than the force values derived from pressure information, such as that from high-resolution manometry and tongue pressure sensors. This difference can be attributed to the simulations extracting only the forces exerted on the organ by the food bolus. Force information on organs has the potential to provide a new interpretation of conventional mechanical indicators such as manometry and tongue pressure sensors.

Ultra-low cost and high-performance paper-based flexible pressure sensor for artificial intelligent E-skin

E-skin driven by artificial intelligence algorithms enables the innovation of future technologies such as IoT, intelligent manufacturing, health monitoring and human–machine interfaces for VR/AR. Although many flexible pressure sensors have been reported, the development of sensors that combine low-cost and high performance remains a huge challenge. Herein, we propose an all-paper-based pressure sensor with water-soluble graphite functional layer and carbon interdigital electrodes obtained by friction coating and screen printing, respectively. The sensor boasts a rectangular design measuring 1 cm × 2.15 cm, with a remarkably thin profile of only 71 μm and an ultra-light weight of 6 mg, and the cost of a single sensor is extremely low. Specifically, the sensor achieved a lower detection limit of 0.98 Pa and an upper detection limit of 2000 kPa, which represents a new benchmark among paper-based pressure sensors. It demonstrated high sensitivity across various pressure ranges: 319.94 kPa−1 (0–1 kPa), 97.63 kPa−1 (1–40 kPa), 18.75 kPa−1 (40–200 kPa), and 2.72 kPa−1 (200–2000 kPa). The sensor also exhibited a rapid response and recovery time of 8 ms and 9 ms, respectively, along with excellent durability (>20,000 fatigue tests, 10,000 cycles at 2 kPa and 10,000 cycles at 100 kPa) and high pressure stability. Furthermore, the eco-friendly sensor can identify eight surface textures with a recognition accuracy of 98.33% after training with a 2D convolutional neural network (2D CNN). Finally, a portable wireless wearable device with dimensions of 38 mm × 26 mm × 14 mm and a weight of only 11.265 g has been developed for pressure recognition, breathing, pulse, and heartbeat detection.

Operando Stack Pressure Measurement of LFP/Graphite and LMFP/Graphite Cells to aid in State of Charge Prediction

Abstract One of the biggest issues facing electric vehicles with LiFePO4 (LFP) batteries is state of charge management. Over much of the state of charge window, the voltage of both LFP and LiMnxFe1-xPO4 (LMFP) batteries does not vary. Therefore, state of charge management for LFP batteries relies on coulomb counting, which requires frequent charging to 100% to remain accurate. We propose to use the signal from pressure sensors placed between prismatic cells in battery modules to help determine the state of charge of LFP and LMFP batteries. Using LFP and LMFP pouch cells to demonstrate this principle, the pressure vs. time profiles of cells constrained in fixed volumes reveal distinct and repeatable profiles, which can be used to determine the state of charge in combination with voltage monitoring. Additionally, a simulated LFP battery pack was constructed and the pressure vs. time profile was monitored in the presence of compliant material under low initial force loadings to demonstrate how this could work in an EV battery.

A Pressure Sensor Based on the Interaction between a Hard Magnet Magnetorheological Elastomer and a Hall Effect Structure

In this article, we report a novel pressure sensing method based on the Hall effect and a hard magnet magnetorheological elastomer (hmMRE). The elastic property of the MRE under pressure was used to generate spatial variation in the magnetic flux density around the MRE, and the variation was detected by the Hall effect device underneath. As the first development in this kind of pressure sensing mechanism, we conducted research for the following three purposes: (1) to verify the Hall effect on the output signal, (2) to understand the sensor output variations under different modes of operation, and (3) to utilize the mechanism as a pressure sensor. We characterized the sensor with its operation parameters, such as signal polarity switching depending on wiring directions, signal amplitude, and offset shift depending on the input voltage. Based on the analyses, we concluded that the Hall voltage represents the pressure applied on the hmMRE, and the new pressure sensing mechanism was devised successfully.

THERMAL TECHNOLOGICAL CONDITION OF IVG.1M RESEARCH REACTOR CORE UNDER VARIOUS OPERATING MODES

The relevance of the study is related to the determination of the thermal characteristics of the IVG.1M research reactor core with the low enriched uranium fuel under the nominal and design operating modes. The thermal technological condition of the IVG.1M research reactor during the start-up are determined by the readings of the temperature, pressure and coolant flow sensors of the information and measuring system. The indirect methods including the computer simulating ones are used to determine the temperature of the core structural materials and the distribution of the coolant temperature by the height of the fuel assembly. The research has been carried out using the method of the finite element analysis using the ANSYS Fluent software package. The study goal was to verify the adequacy of the calculation methodology and obtain the calculated data on the temperature distribution in the fuel assembly in the reactor power range from the nominal to design one. The article presents a description of the IVG.1M reactor, the research methodology, computer model, simulation results and the comparison of the calculated data with the experimental ones. The study scientific novelty consists in determining the temperature conditions of the fuel rods during the reactor operation at various levels of the design capacity with a conservative approach to the cooling conditions. The significance of the research results lies in the fact that a computer model can be used to determine the characteristics of the IVG.1M reactor core under the reactor various operating modes and to analyze the thermohydraulic processes in the fuel assembly.