Principle Of Sensor Research Articles

The Role of Silver Nanoparticles in Electrochemical Sensors for Aquatic Environmental Analysis

With rapidly increasing environmental pollution, there is an urgent need for the development of fast, low-cost, and effective sensing devices for the detection of various organic and inorganic substances. Silver nanoparticles (AgNPs) are well known for their superior optoelectronic and physicochemical properties, and have, therefore, attracted a great deal of interest in the sensor arena. The introduction of AgNPs onto the surface of two-dimensional (2D) structures, incorporation into conductive polymers, or within three-dimensional (3D) nanohybrid architectures is a common strategy to fabricate novel platforms with improved chemical and physical properties for analyte sensing. In the first section of this review, the main wet chemical reduction approaches for the successful synthesis of functional AgNPs for electrochemical sensing applications are discussed. Then, a brief section on the sensing principles of voltammetric and amperometric sensors is given. The current utilization of silver nanoparticles and silver-based composite nanomaterials for the fabrication of voltammetric and amperometric sensors as novel platforms for the detection of environmental pollutants in water matrices is summarized. Finally, the current challenges and future directions for the nanosilver-based electrochemical sensing of environmental pollutants are outlined.

Cernox thermometer under hydrostatic pressure for enhanced temperature accuracy

Sensors that can be used in a pressurized environment are few. Thus, it is generally considered that the accuracy of temperature measurements decreases in experiments carried out under pressure. Based on a commercially available cryogenic temperature sensor, Cernox from Lake Shore Cryotronics, Inc., we developed and tested an enclosure that enables Cernox to be directly used inside a cylindrical pressure cell, next to the specimen under investigation. To isolate the Cernox thermometer from the pressurized medium inside the pressure cell, we combined the principles of an encapsulated Pt sensor and a cylindrical clamped piston cell. The encapsulated Cernox allows for precise measurement and control of the specimen’s temperature. It is also beneficial for accurately determining small changes in physical properties, such as temperature, or measuring the amount of hysteresis in the first-order phase transition. It would also be useful for accurately controlling the sample’s temperature when the sample approaches the transition temperature from one side.

Two-Way Generation of High-Resolution EO and SAR Images via Dual Distortion-Adaptive GANs

Synthetic aperture radar (SAR) provides an all-weather and all-time imaging platform, which is more reliable than electro-optical (EO) remote sensing imagery under extreme weather/lighting conditions. While many large-scale EO-based remote sensing datasets have been released for computer vision tasks, there are few publicly available SAR image datasets due to the high costs associated with acquisition and labeling. Recent works have applied deep learning methods for image translation between SAR and EO. However, the effectiveness of those techniques on high-resolution images has been hindered by a common limitation. Non-linear geometric distortions, induced by different imaging principles of optical and radar sensors, have caused insufficient pixel-wise correspondence between an EO-SAR patch pair. Such a phenomenon is not prominent in low-resolution EO-SAR datasets, e.g., SEN1-2, one of the most frequently used datasets, and thus has been seldom discussed. To address this issue, a new dataset SN6-SAROPT with sub-meter resolution is introduced, and a novel image translation algorithm designed to tackle geometric distortions adaptively is proposed in this paper. Extensive experiments have been conducted to evaluate the proposed algorithm, and the results have validated its superiority over other methods for both SAR to EO (S2E) and EO to SAR (E2S) tasks, especially for urban areas in high-resolution images.

Reshaping healthcare with wearable biosensors

Wearable health sensors could monitor the wearer's health and surrounding environment in real-time. With the development of sensor and operating system hardware technology, the functions of wearable devices have been gradually enriched with more diversified forms and more accurate physiological indicators. These sensors are moving towards high precision, continuity, and comfort, making great contributions to improving personalized health care. At the same time, in the context of the rapid development of the Internet of Things, the ubiquitous regulatory capabilities have been released. Some sensor chips are equipped with data readout and signal conditioning circuits, and a wireless communication module for transmitting data to computer equipment. At the same time, for data analysis of wearable health sensors, most companies use artificial neural networks (ANN). In addition, artificial neural networks could help users effectively get relevant health feedback. Through the physiological response of the human body, various sensors worn could effectively transmit data to the control unit, which analyzes the data and provides feedback of the health value to the user through the computer. This is the working principle of wearable sensors for health. This article focuses on wearable biosensors used for healthcare monitoring in different situations, as well as the development, technology, business, ethics, and future of wearable sensors for health monitoring.

Wear debris measurement in lubricating oil based on inductive method: A review

Almost all of the wear debris generated during the operation of the machine is suspended in the circulating lubricating oil. The analysis of the wear debris in the lubricating oil can effectively monitor the wear state of the machine and provide early warning of failures. An overview on inductive sensors for measuring wear debris in lubrication is introduced. To begin with, the significance of analyzing the wear debris in lubricating oil is explained and the working principle of the inductive wear sensors is illustrated. Furthermore, the development of inductive wear sensors and the key limitations are summarized. Finally, some rest factors affecting the sensor and the processing method of the induction signal aliasing are discussed, and the future development trend is prospected. It is pointed out that developing high sensitivity wear debris inductive sensors, increasing sensor throughput, and solving the problem of aliasing of detection signals are the following issues that should be further studied in the future.

GAS SENSORS BASED ON METAL OXIDE NANOPARTICLES AND THEIR APPLICATION FOR ENVIRONMENTALLY HAZARDOUS GASES DETECTION A MINI-REVIEW

Hazardous gases have adverse effects on living organisms and the environment. They can beclassified into two categories, i.e. toxic gases (e.g. H2S, SO2, CO, NO2, NO and NH3) and greenhousegases (e.g. N2O, CH4 and CO2). Moreover, their presence in confined areas may lead to fireaccidents, cause serious health problems or even death. Therefore, monitoring of these substanceswith gas sensors allows assessing the quality of the atmosphere, helps avoiding accidents and saveslives. Metal oxide semiconductor gas sensors (MOS) are one of the most popular choices for theseapplications owing to their numerous advantages, i.e. high sensitivity, long lifetime and shortresponse time. However, these devices have their limitations as well. They exhibit baseline drift,sensor poisoning and poor selectivity. Although much has been done in order to deal with thoseproblems, the improvement of MOS sensors continues to attract researchers attention.The strict control of gas sensing materials preparation is one of the approaches that helps to improveMOS sensors performance. Nanomaterials have been found to be more suitable candidates for gasdetection than materials designed at microscale. Moreover, it was found that the regular and orderedmorphology of metal oxide nanostructures, their loading with noble metals, or the formation ofheterojunctionscan exert additional influence on the properties of these nanostructures andimprove their gas sensing performance, which will be described in the following sections of thispaper. Following a discussion of the operation principle of MOS sensors, a comprehensive review ofthe synthesis and application of metal oxide nanoparticles in the construction of the MOS sensorsdedicated for environmentally hazardous gases is presented. The paper discusses also present issuesand future research directions concerning application of nanotechnology for gas sensing.

In Situ Continuous Measurement of Salinity in Estuarine and Coastal Sediments by All-Solid Potentiometric Sensors

Salinity is crucial for understanding the environmental and ecological processes in estuarine and coastal sediments. In situ measurements in sediments are scarce due to the low water content and particulate adsorption. Here, a new potentiometric sensor principle is proposed for the real-time in situ measurement of salinity in sediments. The sensor system is based on paper sampling and two all-solid electrodes, a cation-selective electrode (copper hexacyanoferrate, CuHCF) and an anion-selective electrode (Ag/AgCl). The spontaneous aqueous electrolyte extraction and redox reaction can produce a Nernstian response on both electrodes that is directly related to salinity. This potentiometric sensor allows for salinity acquisition in a wide salinity range (1-50 ppt), with high resolution (<1 ppt), and at a low water content (<30%), and it has been applied for the in situ measurement of salinity and the interpretation of cycling processes of metals in estuarine and coastal sediments. Moreover, the sensor coupled to a wireless monitoring system exhibited remote-sensing capability and successfully captured the salinity dynamic processes of the overlying water and pore water during the tidal period. This sensor with its low cost, versatility, and applicability represents a valuable tool to advance the comprehension of salinity and the salinity-driven dissolved-matter variations in estuarine and coastal sediments.

FBG Sensor for Rotation Speed Measurement With Large Sensing Distance Based on Terfenol-D

The rotation speed of revolved mechanisms is one of the critical parameters for characterizing mechanical health conditions, fault diagnosis, and maintenance strategy. This article studies a fiber Bragg grating (FBG) sensor for rotation speed measurement based on Terfenol-D, which generates a rotating magnetic field through a magnet fixed on a rotating structure. Terfenol-D material undergoes magnetostriction with the rotating magnetic field. The FBG sensor fixed on Terfenol-D monitors the rotation speed of the rotating mechanism by inducing the strain of Terfenol-D. The sensor principle is theoretically analyzed in this article, and finite-element simulations are performed on the space magnetic field distribution of the sensor. According to the results, the variation law of the central wavelength of the sensor grating is qualitatively derived. Then, the orthogonal tests are performed with the sensing distance from 2 to 10 cm and the rotation speed from 1 to 50 r/s. The experimental results are compatible with the theoretical analysis. The results show that the sensor can accurately measure the rotation speed at a sensing distance of 10 cm, which makes the sensor have a more flexible installation position. The sensor has good operation performance within experimental conditions.

Research and Practice on Slope Monitoring of Power Grid Construction Based on IoT Technology

In order to solve the shortcomings of traditional slope monitoring technology in automation, timeliness, and intelligence, this paper studies a slope monitoring system for high slopes in power grid construction based on power Internet of Things technology with characteristics of comprehensive perception, reliable delivery, and convenient application. By applying high-precision sensors, the plane and elevation monitoring accuracy of the system can reach a millimeter level. The layout principle of digital slope monitoring sensors is summarized and proposed according to traditional slope monitoring data in a power grid construction project in order to solve the relationship between actual slope monitoring demand and equipment investment. The slope monitoring system is applied in the three-level slope of a pilot project, and this technology realizes real-time judgment, efficient data transmission, and processing, making the obtained slope stability information more comprehensive, dynamic, and reliable.

Study on automatic water level detection process using ultrasonic sensor

Currently, we live in a world where all fields are developing very rapidly. In particular, the field of water management is developing day by day, and the water supply system is being intelligent automated. Nevertheless, mechanical means and human labor are used to monitor the water level in many water management facilities of our region. This situation causes many problems in the automatic management of the system. In this article, we will consider the process of automating the measurement and control of water level, that is, an automated system that helps to know when the water in the tank is full or empty. In the automation of this system, an ultrasonic sensor that meets the requirements of the time was used. The working principle of ultrasonic sensors is based on the movement of ultrasonic waves over time. The analysis of the results of this experiment showed that the water level measurement and control system did not make more than 1.5% error in different values of the water level. This situation showed that the developed system can be effectively used in all water level control facilities.

Discussion on the Range Configuration Strategy of Tension Sensor of Icing Monitoring Terminals of High-Voltage Transmission Line

Online monitoring of transmission line icing is an important means of anti-icing in the power grid. The accuracy mainly depends on the accuracy of the terminal tension sensor, which is one of the main sources of monitoring errors from large range configuration of tension sensor. This paper analyzes the working principle and characteristics of tension sensors and the load calculation method of different types of insulator hanging points to determine the output error of over-range configuration tension sensors. It finally proposes a principle and a calculation method for tension sensors’ range configuration, which is verified to be reliable by experiments. The results can provide a basis for the selection of tension sensors for icing-monitoring terminals and reduce the error caused by temperature drift and zero drift of tension sensors.

Multimodal Sensing in Stroke Motor Rehabilitation

AbstractApplying sensors in biomedical institutions and home‐based stroke rehabilitation is now a global research focus. In this review paper, the relationship between stroke diseases’ physiology mechanism and diverse sensors’ functionalities is detailed explained. The review starts by interpreting how stroke influences motion abilities and then introduces broadly adopted physical training methods. After, the working principles of sensors and their use to objectively provide patients’ body information for stroke rehabilitation status are discussed. The content of the paper aims to not only review the state‐of‐the‐art works of developing sensors for assisting in evaluating stroke patients’ status but also bridging the gap between medical staff and engineers.

Advances in silicon-based in-sensor computing for neuromorphic vision sensors

With the development of the Internet of Things (IoT), intelligent security systems, assisted driving and artificial intelligence, traditional machine vision sensors no longer meet the needs of low power consumption, high sampling rate, high dynamic range and low data redundancy, therefore, the emergence of new vision sensors is urgently needed. Drawing on the information extraction and encoding methods of biological retinas, in-sensor computing photoelectric detection systems have been developed. It has a more efficient sampling model and data transfer mode than frame-based and separated sensory-computing systems. This paper provides an overview of several major silicon-based in-sensor computing neuromorphic vision sensors. Using bio-vision mechanisms as an entry point, the architecture and working principles of these neuromorphic optoelectronic sensors are focused on, their application scenarios, advantages and limitations are compared, and the challenges and possible directions for development are analysed.

Real-time and accurate monocular 3D sensor using the reference plane calibration and an optimized SGM based on opencl acceleration

Speckle projection profilometry (SPP), as a promising structured light projection technique, can achieve global unambiguous 3D measurement by projecting a single random speckle pattern. In addition, the projected speckle pattern is usually etched into the microstructure of highly integrated Vertical-Cavity Surface-Emitting Laser (VCSEL), which makes the hardware system compact enough to be mounted on mobile devices such as robots. However, since the stereo matching algorithm used in SPP involves high computational overhead, it usually runs in real-time on specially customized hardware platforms such as ASIC/FPGA, rather than general-purpose mobile platforms. In this paper, we propose a real-time and accurate 3D measurement method using a monocular 3D sensor based on the infrared speckle projection. Similar to Kinect v1, our sensor mainly consists of an IR dot projector and one IR camera for projecting and capturing speckle images synchronously. Low-cost and high-quality speckle projection is achieved by customizing the projection pattern of VCSEL and using the beam copy function of Diffractive Optical Elements (DOE). Based on the 3D imaging principle of monocular 3D sensors, a reference plane calibration method is proposed to obtain a high-quality reference speckle image for improving the monocular matching accuracy. Then, benefited from the local memory mechanism and multiple operating synchronizations on the OpenCL environment, an optimized semi-global matching (SGM) algorithm using GPU is presented to achieve efficient and accurate depth reconstruction dynamically. Within the measurement range of 0.8m (length) ×0.5m (width) ×1m (depth), the proposed method can achieve real-time and single-shot 3D imaging with an accuracy of 1.277 mm at 75 FPS on GTX 1060 and 15 FPS on ARM Mail G52(mobile platform).

Generalizability of Soft Sensors for Bioprocesses through Similarity Analysis and Phase-Dependent Recalibration.

A soft sensor concept is typically developed and calibrated for individual bioprocesses in a time-consuming manual procedure. Following that, the prediction performance of these soft sensors degrades over time, due to changes in raw materials, biological variability, and modified process strategies. Through automatic adaptation and recalibration, adaptive soft sensor concepts have the potential to generalize soft sensor principles and make them applicable across bioprocesses. In this study, a new generalized adaptation algorithm for soft sensors is developed to provide phase-dependent recalibration of soft sensors based on multiway principal component analysis, a similarity analysis, and robust, generalist phase detection in multiphase bioprocesses. This generalist soft sensor concept was evaluated in two multiphase bioprocesses with various target values, media, and microorganisms. Consequently, the soft sensor concept was tested for biomass prediction in a Pichia pastoris process, and biomass and protein prediction in a Bacillus subtilis process, where the process characteristics (cultivation media and cultivation strategy) were varied. High prediction performance was demonstrated for P. pastoris processes (relative error = 6.9%) as well as B. subtilis processes in two different media during batch and fed-batch phases (relative errors in optimized high-performance medium: biomass prediction = 12.2%, protein prediction = 7.2%; relative errors in standard medium: biomass prediction = 12.8%, protein prediction = 8.8%).

The Principle and Design of CMOS Active Pixel Sensor

In today's increasingly prosperous global semiconductor development, CMOS image sensors have always been in the spotlight. The many advantages of CMOS such as lower production costs, smaller sensor sizes and lower production costs have allowed them to be used in a wide variety of devices. In this paper, the working principle of the 3-T active pixel sensor and its current applications are introduced, and the architecture and working mode of APS itself are also highlighted. The simulation software coolspice is then used to simulate the output values and timing of the circuit. In this paper, nine inputs of the same value and six pulse voltages are used to control the output sequence. The paper also describes the key technical problems of the APS itself and the solutions to these problems.

Conjugated Polymer-Based Nanocomposites for Pressure Sensors.

Flexible sensors are the essential foundations of pressure sensing, microcomputer sensing systems, and wearable devices. The flexible tactile sensor can sense stimuli by converting external forces into electrical signals. The electrical signals are transmitted to a computer processing system for analysis, realizing real-time health monitoring and human motion detection. According to the working mechanism, tactile sensors are mainly divided into four types-piezoresistive, capacitive, piezoelectric, and triboelectric tactile sensors. Conventional silicon-based tactile sensors are often inadequate for flexible electronics due to their limited mechanical flexibility. In comparison, polymeric nanocomposites are flexible and stretchable, which makes them excellent candidates for flexible and wearable tactile sensors. Among the promising polymers, conjugated polymers (CPs), due to their unique chemical structures and electronic properties that contribute to their high electrical and mechanical conductivity, show great potential for flexible sensors and wearable devices. In this paper, we first introduce the parameters of pressure sensors. Then, we describe the operating principles of resistive, capacitive, piezoelectric, and triboelectric sensors, and review the pressure sensors based on conjugated polymer nanocomposites that were reported in recent years. After that, we introduce the performance characteristics of flexible sensors, regarding their applications in healthcare, human motion monitoring, electronic skin, wearable devices, and artificial intelligence. In addition, we summarize and compare the performances of conjugated polymer nanocomposite-based pressure sensors that were reported in recent years. Finally, we summarize the challenges and future directions of conjugated polymer nanocomposite-based sensors.

Transverse vibration modes analysis and acoustic response in optical fibers

Fiber optic sensors are often used as acoustic sensors to detect sound waves because of their apparent advantages, such as anti-electromagnetic interference and strong adaptation to the environment. The transverse vibration mode of the fiber caused by the acoustic wave can be obtained, and the principle of the optical fiber sensor to detect the acoustic wave signal was explored by using a simple model. It is found that the acoustic wave can effectively cause the change in birefringence of the fiber only when the number of azimuthal modes is 2, and the acoustic wave was detected by using a fiber sensor. It is found, by analyzing the detection mechanism, that the spectral width is proportional to the acoustic impedance of the surrounding medium, and the acoustic interaction between the TR22 mode and the surrounding medium is much weaker than that of the TR21 mode. This provides a theoretical basis for the detection of acoustic signals by fiber optic sensors.

Operating principles of zero-bias retinomorphic sensors

Zero bias retinomorphic sensors (ZBRSs) are a new type of optical sensor which produce a signal in response to changes in light intensity, but not to constant illumination. For this reason, they are hoped to enable much faster identification of moving objects than conventional sensing strategies. While recent proof-of-principle demonstrations are significant, there does not yet exist a robust quantitative model for their behaviour, which represents an impediment for effective progress to be made in this field. Here I report a mathematical framework to quantify and predict the behaviour of ZRBSs. A simple device-level model and a more detailed carrier-dynamics model are derived. Both models are tested computationally, yielding equivalent behaviour consistent with experimental observations. A figure of merit, , was identified which is hoped to enable facile comparison of devices between different research groups. This work is hoped to serve as the foundation for a consistent description of ZBRSs.

Cost-effective selective hydrogen sensor based on the combination of catalytic spillover effect and impedance measurement

Selective measurement of hydrogen in the natural gas grid as large transport and storage infrastructure is a crucial requirement for establishing a hydrogen economy. Cost-effective sensors must face mainly two critical conditions: (i) absence of oxygen contents capable for ensuring the sensor function and (ii) presence of a large surplus of another reducing gas, methane. The developed Pt/TiO2 sensor is based on the combination of the catalytic spillover effect and an impedance measurement. It allows hydrogen detection in a wide concentration range and it is specific for hydrogen while other gases such as methane or carbon dioxide behave like inert gases. The sensor curve with decreasing relative sensitivity for higher hydrogen concentrations and the temperature dependence of the sensor effect are in agreement with the spillover-based sensor principle, where hydrogen is activated on Pt and then spills over onto the TiO2 where it acts as electron donor.