Electric Sensor Research Articles

Laboratory evaluation of open source and commercial electrical conductivity sensor precision and accuracy: How do they compare?

Variation in the electrical conductivity (EC) of water can reveal environmental disturbance and natural dynamics, including factors such as anthropogenic salinization. Broader application of open source (OS) EC sensors could provide an inexpensive method to measure water quality. While studies show that other water quality parameters can be robustly measured with sensors, a similar effort is needed to evaluate the performance of OS EC sensors. To address this need, we evaluated the accuracy (mean error, %) and precision (sample standard deviation) of OS EC sensors in the laboratory via comparison to EC calibration standards using three different OS and OS/commercial-hybrid (OS/C) EC sensors and data logger configurations and two commercial (C) EC sensors and data logger configurations. We also evaluated the effect of cable length (7.5 m and 30 m) and sensor calibration on OS sensor accuracy and precision. We found a significant difference between OS sensor mean accuracy (3.08%) and all other sensors combined (9.23%). Our study also found that EC sensor precision decreased across all sensor configurations with increasing calibration standard EC. There was also a significant difference between OS sensor mean precision (2.85 μS/cm) and the mean precision of all other sensors combined (9.12 μS/cm). Cable length did not affect OS sensor precision. Furthermore, our results suggest that future research should include evaluating how performance is impacted by combining OS sensors with commercial data loggers as this study found significantly decreased performance in OS/commercial-hybrid sensor configurations. To increase confidence in the reliability of OS sensor data, more studies such as ours are needed to further quantify OS sensor performance in terms of accuracy and precision across different settings and OS sensor and data collection platform configurations.

Emerging nanomaterials to enhance electrochemical impedance spectroscopy for biomedical applications

Over the last few decades, electrical impedance-based sensors have been investigated for clinical translation to detect changes in tissue conductivities, including cardiac output and pulmonary function. Recently, electrochemical impedance spectroscopy (EIS) provides metabolic measurements that occur at the electrode-tissue interface, and the 3-D EIS can be reconstructed to generate electrical impedance tomography (EIT) for detecting the impedimetric properties of the vascular wall or fatty liver disease. In both EIS and EIT applications, the electrochemical properties of the interface electrodes are essential to address the signal-to-noise ratio or sensitivity of measurements in the biological environment. To enhance the conductive properties, we will survey a series of carbon-based nanomaterials as the emerging candidates for coating the electrodes of bioimpedance sensors. In this review, we will provide a theoretical background on impedance-based measurements and highlight the current state of EIS and EIT, including their applications for cancer screening and detection of vulnerable atherosclerotic plaques. Next, we will focus on the strengths of different nanomaterials when used as an electrode coating to optimize charge transfer across the electric double layers and to enhance measurement sensitivity. We will also identify some unmet clinical needs, such as the ability to adapt to different hemodynamic conditions and blood vessel geometries, that can be realized by the novel biomaterials for the future EIS-based sensors.

Designing a PID Control Scheme for Direct Current Motors without Using Electrical Sensors

Designing a PID Control Scheme for Direct Current Motors without Using Electrical Sensors

Rapid Escherichia coli Cloned DNA Detection in Serum Using an Electrical Double Layer-Gated Field-Effect Transistor-Based DNA Sensor.

In this study, a rapid diagnosis platform was developed for the detection of Escherichia coli O157:H7. An electrical double layer (EDL)-gated field-effect transistor-based biosensor (BioFET) as a point-of-care testing device is demonstrated with its high sensitivity, portability, high selectivity, quick response, and ease of use. The specially designed ssDNA probe was immobilized on the extended gate electrode to bind the target complementary DNA segment of E. coli, resulting in a sharp drain current change within minutes. The limit of detection for target DNA is validated to a concentration of 1 fM in buffer solution and serum. Meanwhile, the results of a Kelvin probe force microscope were shown to have reduced surface potential of the DNA immobilized sensors before and after the cDNA detection, which is consistent with the decreased drain current of the BioFET. A 1.2 kb E. coli duplex DNA synthesized in plasmid was sonicated and detected in serum samples with the sensor array. Gel electrophoresis was used to confirm the efficiency of sonication by elucidating the length of DNA. Those results show that the EDL-gated BioFET system is a promising platform for rapid identification of pathogens for future clinical needs.

High electrical self-healing flexible strain sensor based on MWCNT- polydimethylsiloxane elastomer with high gauge factor and wide measurement range

Flexible strain sensors are widely used in wearable electronic devices, sports health detection and electronic skin. These sensors have the advantages of large strain range, full flexibility and biocompatibility. However, flexible strain sensors will inevitably suffer mechanical damage during operation, resulting in degraded sensor performance and shortened life. In this paper, a high electrical self-healing flexible strain sensor (ESFSS) based on multi-walled carbon nanotubes and polydimethylsiloxane (MWCNT-PDMS) elastomer is proposed. The ESFSS is designed by the modified self-healing PDMS elastomer (SPE) that gives the ESFSS electrical self-healing ability. Moreover, the conductive filler (MWCNT) is introduced to the sensor to endow the ESFSS with high sensitivity and a wide measurement range. The matrix materials of the ESFSS are characterized, the electrical healing mechanism and sensing mechanism of the ESFSS are analyzed and the high electrical self-healing performance of the ESFSS is demonstrated with an electrical healing efficiency (EFE) of 97.8%. In addition, the proposed ESFSS also has high gauge factor (GF) values of 57.894 (0–85.36%), 993.088 (85.36%–106.64%) and 5536.346 (106.64%–136.32%) and a wide measurement range (0–136.32%). The ESFSS has a promising application in human motion detection.

DELAMINATION DETECTION IN THIN-WALLED COMPOSITE STRUCTURES USING ACOUSTIC PITCH-CATCH TECHNIQUE WITH FIBER BRAGG GRATING SENSORS

Structural health monitoring (SHM) of a composite structure is essential in maintaining the integrity of the structure. Over the years, various studies have reported on the use of conventional electrical sensors in analysing acoustic wave propagation for delamination detection. However, electrical sensors are associated with drawbacks such as high signal attenuation, are prone to electromagnetic interference (EMI) and are not suitable for harsh environments. Therefore, this paper reported on the use of fiber Bragg grating (FBG) sensors for delamination detection. Two composite structures with delamination sizes of 10 cm × 2 cm and 10 cm × 6 cm were fabricated. Two FBGs were bonded before and after the delamination. In addition, three trials of impacts were induced at the centre of the structure. Multiple signal parameters were obtained and analysed, which were the time delay, amplitude difference and velocity difference. The experimental results revealed that the time delay, amplitude and velocity analysis varied for both the delamination sizes with an average percentage of 42.36%, 97.09% and 42.39%, respectively. Therefore, it was confirmed that the increase in delamination size resulted in a longer time delay, higher signal amplitude attenuation and slower wave propagation.

Viticultural Manipulation and New Technologies to Address Environmental Challenges Caused by Climate Change

Climate change is a critical challenge for the global grape and wine industry, as it can disrupt grapevine growth, production, and wine quality. Climate change could influence the cost-effectiveness and growth of the wine industry in different wine regions since grapevine development is deeply dependent on weather (short-term) and climate (long-term) conditions. Innovation and new technologies are needed to meet the challenge. This review article addresses the impact of climate change on grapevines, such as vine phenology, pest and disease pressure, crop load, and grape and wine composition. It also reviews recent advances in the areas of viticultural manipulation and relevant technologies to potentially reduce the impact of climate change and help growers improve grape quality. Remote sensing is used for vineyard microclimate monitoring; thermal sensors combined with UAVs, aircraft, or satellites are used for water management; soil electrical conductivity sensors have been developed for soil mapping. Viticultural manipulations, such as regulated deficit irrigation for water use efficiency and berry-ripening delay for growing quality fruit, are also discussed. The review assesses future directions for further technological development, such as soil and vine water monitoring devises, precision viticulture, and artificial intelligence in vineyards.

Development and characterization of silane crosslinked cellulose/graphene oxide conductive hydrophobic membrane

The development of environmentally friendly, multifunctional conductive membranes is an ideal candidate for future new electronic components. Some cellulose-based electronic sensors have been studied, such as pressure sensors, hydrothermal sensors, flexible sensors, optoelectronic devices, and electrochemical energy storage, etc. However, there are very few studies on cellulose-based conductive materials in Joule heating. In this study, we adopted a simple and environmentally friendly silane crosslinking method to crosslink cellulose and graphene oxide (GO) together. The cellulose/GO hybrid membrane were characterized by FTIR, Raman, SEM, AFM, TGA, surface wettability, self-cleaning, surface resistance, Joule heating and other tests. Results showed that the covalent bond between cellulose and GO was formed by vinyltrimethoxysilane (VTMS), and GO was successfully cross-linked on the surface of the cellulose membrane. The cellulose/GO hybrid membrane has good thermal stability, strong hydrophobicity and self-cleaning properties. When the GO concentration was 3 w/w%, the Young's modulus of the film reached the maximum (47.38% higher than that of the original sample). In addition, it also exhibited extremely low surface resistivity (720.69 Ω), controllable Joule heating capability, extremely fast thermal response (heating process and cooling process within 5 s) and good electrothermal stability. It showed great potential in multi-functional electronic products such as electric heating electronic devices, electric heating sensors, and smart clothing in the future.

Integrated Filter Design for Analog Field Mill Sensor Interface

The design process of an integrated bandpass filter targeted for the noise filtering stage of the synchronous demodulation unit of an electric field mill sensor interface is presented. The purpose of this study of filter integration techniques is to avoid the challenging and, in some cases, impossible passive element integration process and to incorporate the final filter design in an entirely integrated field mill sensing system with superior performance and an optimized silicon-to-cost ratio. Four different CMOS filter implementations in the 0.18 μm process of XFAB, using OTA (Operational Transconductance Amplifier)-based configurations for passive element replacement in cascaded filter topologies and leapfrog techniques, are compared in terms of noise performance, total harmonic distortion, dynamic range, and power consumption, as well as in terms of integrability, silicon area, and performance degradation at process corners/mismatches. The optimum filter design performance-wise and process-wise is included in the final design of the integrated analog readout of a field mill sensor, and post-layout simulation results of the total circuit are presented.

Micro- and nanostructure of additively manufactured, in-situ alloyed, magnetic spinodal Fe54Cr31Co15

Exploring the chemical micro- and nanostructure of metal alloys is essential to understand their physical properties, such as magnetism or hardness. Additively manufactured (AM) materials, e.g. via laser powder bed fusion (LPBF) followed by various heat treatments, can raise further questions concerning the printed material. For the in-situ alloyed, spinodal Fe54Cr31Co15 system, the macroscopic magnetic behaviour is greatly influenced by subsequent homogenisation and heat treatment steps. Here we show that the decomposition takes place on the nanometre scale, resulting in ferromagnetic FeCo-rich particles embedded in a Cr-rich matrix. By studying phenomena like chemical homogeneity, grain structure, and texture of the in-situ alloyed material at different scales, we reveal correlations between the heat treatment and the resulting nanostructure and its ferromagnetic properties. We found that the isothermal heating conditions determine the degree of phase segregation and that a homogenization step can be omitted for additively manufactured, in-situ alloyed FeCrCo alloys. The approach thereby offers insight and a path for also tailoring specific manufacturing parameters to provide the right quality printed materials with desired functionalities. For example, magnetic FeCrCo alloys are often used in electric motors or magnetic sensors, and the flexibility of the presented approach can lead to optimal use of the material.

Assimilating Low‐Cost High‐Frequency Sensor Data in Watershed Water Quality Modeling: A Bayesian Approach

AbstractUncertainty reduction in watershed water quality (WWQ) modeling remains a major challenge. One important reason is the lack of sufficient available water quality observations because traditional laboratory analysis of water samples has high labor, financial and time costs. Low‐cost high‐frequency water quality data from in‐situ sensors provide an opportunity to solve this problem. However, long‐term sensing in complex natural environments usually suffers more significant errors. This study aimed to develop a novel method to utilize in‐situ sensor data in WWQ modeling, namely, the Bayesian calibration using multisource observations (BCMSO), which can simultaneously assimilate laboratory‐based observations and in‐situ sensor data. Both synthetic and real‐world cases of nitrate modeling were used to demonstrate the methodology, and the Soil and Water Assessment Tool was employed as the WWQ model. The results indicated that direct assimilation of sensor data using traditional Bayesian calibration generated obvious deviations in parameter inference and model simulation, which could consequently bias future predictions and affect management decision correctness. However, after proper treatment of errors in sensor data, the BCMSO method could extract meaningful information from sensor data and prevent negative impacts of errors. The modeling uncertainty was also greatly reduced. In the real‐world case, with 1 yr of subhourly electrical conductivity sensor data incorporated, the modeling uncertainty of nitrate concentration and management cost of controlling nitrate pollution were reduced by 70%. The BCMSO method provides a flexible framework to accommodate nonconventional observations in environmental modeling and can be easily extended to other modeling fields.

New smart actuator based on shape memory alloys for avoiding overheating and preventing fire in electronic devices

In the past few years, fire incidents in electronic systems have become frequent and caused many losses of life and property. Consequently, various studies have been conducted in order to establish effective ways to avoid fire caused by electrical devices. In light of the fact that electrical sensors frequently get damaged, it is crucial to develop a smart switch that can detect and control off electrical current during an unexpected heat increase or fire incident. In this study, a numerical study that aims to develop a smart actuators combined with electrical devices in order to protect electronic system from fire accidents has been conducted. The proposed smart actuator is based on the shape memory alloy (SMA), which has a robust thermomechanical coupling that reacts to the temperature variation and generates an important mechanical loading. Using the SMA actuator between an electrical power source and the device will avoid any incident related to the temperature increase. Therefore, mathematical modelling has been conducted to adjust the smart actuator with any electrical device, with rules that will be developed to increase the actual detection rate. The simulation results of the proposed fire detection system has been compared with several current methods. The results show that the proposed engineering is very promising and can respond to a temperature change in 0.2 s and generate a total deformation of about 8%. The results show that the suggested method has a higher fire detection rate that can reach 90% for detection.

Monitoring of Temperature on the Compost Pile in the Mechanical Composting Facility using Electric Sensors and Modeling Compost Maturity

Monitoring of Temperature on the Compost Pile in the Mechanical Composting Facility using Electric Sensors and Modeling Compost Maturity

A wearable dual-mode strain sensing yarn: Based on the conductive carbon composites and mechanoluminescent layer with core-sheath structures

Yarn or fiber has the advantages of flexible processing, comfortable wearing, good service life, high feasibility of mass production. In recent years, the wearable strain sensor based on yarn or fiber has attracted wide attention. However, most of the previous research work is based on a single electrical signal or energy conversion sensor, which greatly reduces the application range of strain sensor. Therefore, multi-mode sensing under stress application conditions is particularly important promoting the development of new materials. By combining resistive stress sensing with mechanical luminescence sensing, the two-mode strain sensing yarn is prepared and applied in wearable applications. The results show that the tensile strength of core-shell yarn can reach 180% and the surface brightness can reach the visual level. When the carbon concentration in the core yarn is 40%, the corresponding range is small and the resistance increases from 3 MΩ to 5MΩ. The doped luminescent powder has a strong luminescent peak at 578 nm and a small luminescent peak at 493 nm under the action of stress. These properties solve the problem of non-stretching of conductive yarns and can better reflect the magnitude of stress. It also has good application prospects in motion detection, health care, human-computer interaction, artificial intelligence and so on.

Performance of SiQDs and H_Si nanopillars/etched Si electrical sensor synthesized by aggressive laser-assisted etching method: comparative study

Performance of SiQDs and H_Si nanopillars/etched Si electrical sensor synthesized by aggressive laser-assisted etching method: comparative study

A Real-Time Data Monitoring Framework for Predictive Maintenance Based on the Internet of Things

The Internet of Things (IoT) is a platform that manages daily life tasks to establish an interaction between things and humans. One of its applications, the smart office that uses the Internet to monitor electrical appliances and sensor data using an automation system, is presented in this study. Some of the limitations of the existing office automation system are an unfriendly user interface, lack of IoT technology, high cost, or restricted range of wireless transmission. Therefore, this paper presents the design and fabrication of an IoT-based office automation system with a user-friendly smartphone interface. Also, real-time data monitoring is conducted for the predictive maintenance of sensor nodes. This model uses an Arduino Mega 2560 Rev3 microcontroller connected to different appliances and sensors. The data collected from different sensors and appliances are sent to the cloud and accessible to the user on their smartphone despite their location. A sensor fault prediction model based on a machine learning algorithm is proposed in this paper, where the k-nearest neighbors model achieved better performance with 99.63% accuracy, 99.59% F1-score, and 99.67% recall. The performance of both models, i.e., k-nearest neighbors and naive Bayes, was evaluated using different performance metrics such as precision, recall, F1-score, and accuracy. It is a reliable, continuous, and stable automation system that provides safety and convenience to smart office employees and improves their work efficiency while saving resources.

Performance model evaluation of turbine flow meter in vertical gas-liquid two-phase flows

Aiming at the need for flow measurement of gas-liquid flows in domestic gas well production, this paper proposes a measurement method based on the combination of the turbine flow meter (TFM) and a rotating electric field conductance sensor (REFCS). In experiments, the REFCS is used for the measurement of the gas holdup. To verify the applicability of the TFM models investigated in the previous study, for the modeling part, the mass, momentum and torque models are evaluated in vertical upward gas-liquid two-phase flows. In our model test, the meter factor model of TFM considers the effects of the slip ratio between the gas and liquid phases and flow patterns. In particular, the gas holdup involved in calculating the slip ratio in the model evaluation is obtained from the REFCS measurements. Model test results show the torque model has better volumetric flow rate prediction accuracy than the mass and momentum models. In the present study, the ranges of the liquid and gas phases are Qw = 2–30 m3/d and Qg = 1–16 m3/d, it was found that the average absolute deviation (AAD) in the predicted volume flow rate is equal to 1.23 m3/d and the average absolute percentage deviation (AAPD) is equal to 7.69%. The evaluated results presented in this paper will allow better estimates of the volumetric flow rates of gas-liquid flows based on the combined TMF and REFCS measurements during the monitoring of gas well production.

Magneto electric sensor based on cantilever coated galfenol/ AlN structure

Ultrasensitive magnetic field sensors based on magnetoelectric (ME) structures have many applications in bio-magnetic sensors and magnetoencephalography (MEG) scanners. Enhance the sensitivity, limit of detection and main frequency of these sensors need simulation process by accurate methods such as finite element method (FEM). To get good sensitivity and efficient benefit, we use a cantilever-type composite structures including galfenol alloy as a magnetostrictive layer and AlN as a piezoelectric layer. Galfenol is an alloy of iron and gallium and its Fe0.83Ga0.17 structure has a high magnetostriction coefficient and can be used as a thin film. This simulated sensor can measure an AC magnetic field with field resolution in the order of 1pT. This sensor has the best performance in the bias DC magnetic field of 2.3 mT and resonance frequency of 2521.8 Hz and has a magnetoelectric coefficient of 4865 (V/cm.Oe).

Humidity characteristics of a weak fiber Bragg grating array coated with polyimide

Real-time and online large-scale temperature and humidity monitoring is urgently needed in industrial and agricultural production, food and drug storage, and some engineering applications. Since it is difficult to use electrical sensors in building large-scale distributed sensing networks and they have poor stability in some complex and harsh environments, a polyimide-coated (PI-wFBG) weak fiber Bragg grating array based on a drawing tower integrated preparation method is proposed for large-scale distributed humidity sensors. An approximately 4 km-length PI-wFBG array with a coating thickness of 12 μm and grating spacing of 2 m was prepared, and six consecutive sensors were randomly selected for the determination of the relative humidity sensitivity and verification of the stability, repeatability and accuracy of the method. The experimental results showed that the relative humidity sensitivity of the prepared PI-wFBG array humidity sensor was concentrated at 1.4 ± 0.1 pm/%RH (the RH ranged from 21 %RH to 90 %RH) and the average sensitivity consistency deviation was within 3 %; the short-term stability fluctuation was within ± 2.5 %RH and the repeatability and accuracy deviations were ≤ 1.5 %RH and ≤ 2 %RH, respectively. Furthermore, the PI-wFBG array prepared by the drawing tower online method had features of adjustable grating spacing and customizable length, enabling it to be more adaptable and flexible in large-scale application scenarios.

Nucleotide detection mechanism and comparison based on low-dimensional materials: A review.

The recent pandemic has led to the fabrication of new nucleic acid sensors that can detect infinitesimal limits immediately and effectively. Therefore, various techniques have been demonstrated using low-dimensional materials that exhibit ultrahigh detection and accuracy. Numerous detection approaches have been reported, and new methods for impulse sensing are being explored. All ongoing research converges at one unique point, that is, an impetus: the enhanced limit of detection of sensors. There are several reviews on the detection of viruses and other proteins related to disease control point of care; however, to the best of our knowledge, none summarizes the various nucleotide sensors and describes their limits of detection and mechanisms. To understand the far-reaching impact of this discipline, we briefly discussed conventional and nanomaterial-based sensors, and then proposed the feature prospects of these devices. Two types of sensing mechanisms were further divided into their sub-branches: polymerase chain reaction and photospectrometric-based sensors. The nanomaterial-based sensor was further subdivided into optical and electrical sensors. The optical sensors included fluorescence (FL), surface plasmon resonance (SPR), colorimetric, and surface-enhanced Raman scattering (SERS), while electrical sensors included electrochemical luminescence (ECL), microfluidic chip, and field-effect transistor (FET). A synopsis of sensing materials, mechanisms, detection limits, and ranges has been provided. The sensing mechanism and materials used were discussed for each category in terms of length, collectively forming a fusing platform to highlight the ultrahigh detection technique of nucleotide sensors. We discussed potential trends in improving the fabrication of nucleotide nanosensors based on low-dimensional materials. In this area, particular aspects, including sensitivity, detection mechanism, stability, and challenges, were addressed. The optimization of the sensing performance and selection of the best sensor were concluded. Recent trends in the atomic-scale simulation of the development of Deoxyribonucleic acid (DNA) sensors using 2D materials were highlighted. A critical overview of the challenges and opportunities of deoxyribonucleic acid sensors was explored, and progress made in deoxyribonucleic acid detection over the past decade with a family of deoxyribonucleic acid sensors was described. Areas in which further research is needed were included in the future scope.