Accurate Temperature Sensor Research Articles

A ±0.15 °C (3σ) Inaccuracy CMOS Smart Temperature Sensor from −40 °C to 125 °C with a 10 ms Conversion Time-Leveraging an Adaptative Decimation Filter in 65 nm CMOS Technology

This paper presents the design and implementation of a highly accurate smart temperature sensor designed in 65 nm CMOS technology. The sensor exhibits a ±0.15 °C (3σ) error across a wide temperature range from −40 °C to 125 °C, catering to diverse application needs. Leveraging advanced CMOS technology, the sensor employs an adaptive decimation filter that allows us to control the conversion time, ensuring that the accuracy of the conversion is maintained even in challenging conditions. The proposed sensor architecture integrates advanced techniques for temperature sensing for improved accuracy and reliability. Through meticulous circuit design and the usage of dynamic element matching, chopping, and calibration/trimming, the sensor demonstrates exceptional performance characteristics, making it suitable for various industrial, automotive, and consumer electronics applications demanding high precision temperature monitoring.

Analysis of the instability of the characteristics of a standard platinum resistance thermometer in the interval between verifications and a method for reducing the measurement error

The standard platinum resistance thermometer is currently the most accurate contact temperature sensor. One of the most important characteristics of its error is the instability of resistance during the interval between calibrations. In this work, the task was set to analyze the data from periodic calibration of 27 thermometers performed at the D. I. Mendeleev Institute for metrology for several years. Based on the analysis, it was concluded that the instability of the resistance of thermometers at the triple point of water for most thermometers exceeds ±0.002 °C, sometimes reaching several hundredths of a degree. The work analyzes the reasons for the instability of resistance, as well as the instability of the relative resistance of thermometers. Based on data analysis of the thermometer error at the temperatures between calibration points and graphs of changes in the interpolation function during the interval between the calibrations in the entire measurement range, it was shown that the error associated with the instability of the thermometer can be reduced from hundredths to thousandths of a degree when used as a measured parameter relative resistance of the thermometer. It is proposed to include in the regulatory documents on the verification methods of Standard platinum resistance thermometers an additional periodic calibration of the thermometers at the triple point of water.

Analytical Analysis of Factors Affecting the Accuracy of a Dual-Heat Flux Core Body Temperature Sensor.

Non-invasive core body temperature (CBT) measurements using temperature and heat-flux have become popular in health, sports, work safety, and general well-being applications. This research aimed to evaluate two commonly used sensor designs: those that combine heat flux and temperature sensors, and those with four temperature sensors. We used analytical methods, particularly uncertainty analysis calculus and Monte Carlo simulations, to analyse measurement accuracy, which depends on the accuracy of the temperature and flux sensors, mechanical construction parameters (such as heat transfer coefficient), ambient air temperature, and CBT values. The results show the relationship between the accuracy of each measurement method variant and various sensor parameters, indicating their suitability for different scenarios. All measurement variants showed unstable behaviour around the point where ambient temperature equals CBT. The ratio of the heat transfer coefficients of the dual-heat flux (DHF) sensor's channels impacts the CBT estimation uncertainty. An analysis of the individual components of uncertainty in CBT estimates reveals that the accuracy of temperature sensors significantly impacts the overall uncertainty of the CBT measurement. We also calculated the theoretical limits of measurement uncertainty, which varied depending on the method variant and could be as low as 0.05 °C.

High precision temperature measurement for cryogenic temperature sensors based on deep learning technology

Nonlinear error compensation is a significant factor that affects the measurement accuracy of temperature sensors. Cryogenic temperature sensors require a precise calibration technique to achieve accurate temperature measurements. BP (Back Propagation) neural networks are not suitable for sensor temperature prediction due to slow convergence and poor learning ability. In this study, a Chebyshev polynomial-based BiLSTM (Bi-directional Long Short-Term Memory) algorithm (C-BiLSTM) is proposed to improve the accuracy of measurement. Firstly, we demonstrate vacuum packaged temperature sensors based on zirconium oxynitride (ZrOxNy) thin films with ultra-high sensitivity at cryogenic temperatures. Secondly, a dataset consisting of sensor resistance and temperature values was obtained from five above-mentioned sensors, which contains 29 calibration points in the temperature range of 16 K-300 K measured by the Calibration System. Then, the dataset was divided into two temperature ranges (16 K-54.358 K and 40 K-300 K). In the range 16–54.358 K, 14 set-points are selected as training set and 3 set-points as testing set. In the range 40–300 K, 12 set-points are selected as training set and 2 set-points as testing set. Thirdly, a neural network model was built and trained using the TensorFlow framework. By comparing C-BiLSTM we proposed with the BP neural network and BiLSTM, the results show that the C-BiLSTM model converges faster and greatly improves the prediction accuracy after adding Chebyshev polynomial features. The fitting error and prediction error are less than 1 mK in the temperature range of 16 K-40 K. They can also keep less than 10 mK even at the wide temperature range of 40 K-300 K, which is a significant improvement respect to improving the accuracy of temperature measurement.

Reliable Temperature Measurement in Radiation-Intensive Environments

Radiation-resistant temperature sensors are vital for ensuring reliability in radiation-intensive environments, where the highly energetic and penetrating nature of radiation can significantly impact electronic devices and sensors. In such environments, like those near intense radiation sources or in challenging radiation-rich settings, such as space, gamma radiation can lead to erroneous measurements or equipment failures. Radiation-resistant sensors play a crucial role in maintaining measurement accuracy as they are designed to minimize interference caused by radiation, protecting electronic components and providing precise and reliable temperature readings. Their resilience to radiation-induced effects ensures data durability, reducing the need for frequent replacements, and enhancing the overall reliability of measurements in these demanding conditions. In this paper, we present and analyze two different configurations, aiming to address the challenges posed by radiation in sensitive environments. By exploring these novel approaches, we seek to enhance the robustness and accuracy of temperature sensors in radiation-intensive settings, enabling reliable data collection and facilitating successful operations in challenging radiation-rich conditions. The comparative analysis of these configurations will shed light on their performance and effectiveness in mitigating radiation-induced effects, thereby contributing to the advancement of radiation-resistant temperature sensing technologies.

Validation of Temperature Sensors to Monitor Thermoplastic Splint Wear in Hand Surgery Patients.

The aim of this study was to validate the use of temperature sensors to accurately measure thermoplastic volar forearm splint wear in a healthy cohort of volunteers using 5- and 15-minute temperature measurement intervals. A prospective diagnostic study was performed to evaluate the diagnostic accuracy of temperature sensors in monitoring splint wear in 8 healthy volunteers between December 2022 and June 2023. Temperature sensors were molded into thermoplastic volar forearm splints. Volunteers who were familiar with the study aims were asked to keep an exact log of the time spent wearing the splint ("actual wear time"). Sensors recorded temperatures every 5 or 15 minutes, and separate algorithms were developed to determine the sensor-detected wear time compared with the actual wear time as the gold standard. The algorithms were then externally validated with the total population. The 5-minute and 15-minute algorithms demonstrated excellent sensitivity (99.1% vs 96.6%), specificity (99.9% vs 99.9%), positive (99.4% vs 99.5%) and negative (99.9% vs 99.3%) predictive value, and diagnostic accuracy (99.8% vs 99.3%), respectively. The 5-minute algorithm recorded 99.5% of the total splint hours, whereas the 15-minute algorithm recorded 96.1%. There was no significant difference between the actual time per wear session (5.4 ± 2.7 hours) and the time estimated by the 5-minute algorithm (5.4 ± 2.6 hours; P = .40), but there was a significant difference for the 15-minute algorithm (5.2 ± 2.6 hours; P < .001). Temperature sensors can be used to accurately monitor thermoplastic volar forearm splint wear. Diagnostic II.

MICROBIOLOGICAL HAZARDS IN MASS CATERING AREAS ON SELECTED EXAMPLES

Mass catering establishments involve a number of threats related to the health safety of the foodproduced there. The most important microbiological threats include: viruses, prions, bacteria,moulds as well as their toxins and parasites. Those biological hazards pose a hazard for peopleoccupied in the food production process and also for consumers. The required microbiologicalquality of meals served in catering facilities is very important given the risk of potential collectivefood poisoning. The aim of the article is to assess the microbiological cleanliness of surfaces,staff hands and technological equipment used in food production, taking into account selectedmicroclimate parameters. Three mass catering establishments were studied, one open and twoclosed. Microbiological purity tests of surfaces intended for food production were carried out usingthe 3M™ Clean Trace™ NGi luminometer and the compact AXIOMET AX-DT100 microclimaterecorder, equipped with an accurate air temperature and humidity sensor. The obtained researchresults were then compared with reference values and a plan was proposed to improve hygienicconditions in the catering establishments in question. A review of the results of studies carriedout on microbiological cleanliness in selected mass catering establishments showed acceptableexceedances of the reference contamination limits.

Text-to-speech on health monitoring bracelet for the visually impaired

Text-to-speech (TTS) is a technology that converts text into sound using a phonetization system and is especially useful to be applied to blind aids who need information in the form of sound because of their limitations. To help the visually impaired know their health conditions where the visually impaired feel limited to going out during the pandemic. For this reason, it is necessary to make an aid that can read text-based data such as body temperature, heart rate per minute, and oxygen levels into a voice that can be heard by the blind, the method used in this study is finite state automata (FSA) which is used to split Indonesian words into words according to its syllable patterns and facilitate the pronunciation process which is included in the blind aids so that it is expected to help the visually impaired to be able to find out their health condition. In this study, the test was carried out using the confusion matrix method and the results obtained were 100% accurate, 99.71% accuracy of temperature sensor, 98% accuracy of heart rate, and 95% accuracy of oxygen saturation.

Machine-Learning-Based Calibration of Temperature Sensors.

Temperature sensors are widely used in industrial production and scientific research, and accurate temperature measurement is crucial for ensuring the quality and safety of production processes. To improve the accuracy and stability of temperature sensors, this paper proposed using an artificial neural network (ANN) model for calibration and explored the feasibility and effectiveness of using ANNs to calibrate temperature sensors. The experiment collected multiple sets of temperature data from standard temperature sensors in different environments and compared the calibration results of the ANN model, linear regression, and polynomial regression. The experimental results show that calibration using the ANN improved the accuracy of the temperature sensors. Compared with traditional linear regression and polynomial regression, the ANN model produced more accurate calibration. However, overfitting may occur due to a small sample size or a large amount of noise. Therefore, the key to improving calibration using the ANN model is to design reasonable training samples and adjust the model parameters. The results of this study are important for practical applications and provide reliable technical support for industrial production and scientific research.

Land cover aware temperature correction of bicycle transects: A case study of mapping the air temperature in two Belgian cities

A low-threshold methodology for mapping the nocturnal air temperature in an urbanized region based on bicycle measurements and a number of in-situ weather stations is presented. Firstly, two corrections are applied on the bike measurements: a thermal inertia correction to account for the finite reaction speed of the temperature sensor and a temperature decline correction to offset the background temperature evolution during the mobile measurements. The latter includes for the first time land cover information to account for its impact on the nocturnal cooling rate. Secondly, a linear statistical model is used to produce regional maps of the air temperature based on the relationship between the corrected temperature and land cover around the bicycle transect. Applying this methodology on the Belgian cities of Antwerp and Ghent demonstrates that this low-threshold approach allows for a comprehensive picture of the air temperature and that it has advantages compared to more traditional decline corrections. Further improvements could be made by employing more accurate temperature sensors, applying more complex statistical models and performing multiple measurement campaigns per evening.

Luminescence Temperature Sensing and First Principles Calculation of Photoelectric Properties in C12A7 Co-Doped Eu3+ Ions.

Developing high-resolution, high-accuracy fluorescent thermometers is challenging. In this study, the optical properties and thermal sensing of Yb-, Tm-, and Eu-co-doped C12A7 (C12A7:Yb/Eu/Tm), with flower-like structure upconversion microparticles, were studied. Eu3+ doping induced an approximately 6-fold change in the upconversion luminescence (UCL) output in comparison with C12A7:Yb/Tm microparticles. The maximum relative temperature sensitivity (S) of C12A7:Yb/Eu/Tm reached 3.0% K-1, representing an approximately 5-fold difference compared with the value of C12A7:Yb/Tm. In particular, the multicolor upconversion emission of C12A7:Yb/Eu/Tm can easily change from blue to white UCL with increasing temperature. Moreover, the band structure, total density, and optical coefficient of C12A7:Yb/Eu/Tm were investigated via density functional theory. The total density of O atoms increased in comparison with the total density of pure C12A7, indicating that substitution of Ca2+ by Yb/Eu/Tm produced positive vacancies on the cage structure. The optical coefficient of C12A7 was improved by the Yb/Eu/Tm dopant. The thermally regulated multicolor characteristics and thermally coupled energy levels of Tm3+ provide "dual adjustment temperature sensing", which is a promising strategy for realizing accurate and effective temperature sensors.

Employee Presence Using Body Temperature Detection and Face Recognition

Employee performance can be measured by their presence or attendance, which applies to both civil servants and non-civil servants. Because the attendance system still uses the manual technique, it is considered inefficient due to the potential for data fraud and attendance problems. In addition, the government is adopting precautions against viruses in office buildings to maintain business continuity while the pandemic is being addressed. The goal of this study was to employ a facial recognition system and temperature measurement to lower the danger of COVID 19 transmission while also minimizing paper use by using a facial recognition system as a substitute for presences. It has so far permitted the digitization of formerly manual sights. The OpenCV library allows computers to detect faces using the haar cascade classifier approach and Python as a programming language. A Logitech C930e webcam with a resolution of 1080p at 30fps was used to capture facial data, which was then processed on a Raspberry Pi 4 microprocessor. It uses an MLX90614 sensor to monitor body temperature, which is controlled by anArduino Uno microcontroller. It is well integrated into the database based on body temperature testing and facial recognition. The development of a more accurate temperature sensor reading method for distance and employee body temperature is a priority for future research.

Analysis of The Accuracy of Temperature Sensors at The Calibrator Incubator Laboratory are equipped with data storage base on Internet of Thing

A laboratory incubator is a tool used to incubate or incubate a breed. Incubators provide optimum temperature conditions for microorganisms to grow. The incubator has a temperature regulator so that the temperature can be adjusted according to the breed to be incarnated. Incubators utilize hot-dry like ovens. The purpose of this study is to conduct testing and analyze the accuracy of thermocouple sensors with incubator media in laboratory incubator calibrator tools. The contribution of the research is to know the level of accuracy of the sustainable sensor for sensing the temperature in the lab incubator. The main Design consists of 8 MAX 6675 Standards, 8 Thermocouple type K, Arduino Mega, and SD Card Standards. The temperature not in the incubator device is measured by a Type K thermocouple sensor. Thermocouple sensor numbers 8 channels that measure the temperature at each incubator camber point. The temperature will be stored on the SD card to analyze the data and the data can be processed into the form of a graphic. Benchmarking is done using a data logger temperature tool. This is done to make the Design results are under the standards of the Standard. After comparing with the Standard get the largest error value is 3.98%, at channel T6 temperature 35 °C with ordinary incubator media and the smallest error in ordinary incubator media point T6 temperature 37 ° C which is 0.06 % and in fan incubator temperature 35 C has the largest error which is 2.98 % and the smallest error 0.86%. The conclusion of this study is that the design can work well in measuring the temperature of the incubator, as well as the system for storing readings using the SD card Design and sending data using the internet network can work well.

Tunable Fluorescence and Afterglow in Organic Crystals for Temperature Sensing.

The modulation of the properties of emission from multiple emission states in a single-component organic luminescent material is highly desirable in data anticounterfeiting, information storage, and bioapplications. Here, a single-component luminescent organic crystal of difluoroboron diphenyl β-diketonate with controllable multiple emission colors is successfully reported. The temperature-dependent luminescence experiments supported by high-level theoretical calculations demonstrate that the ratio of the fluorescence between the monomer and excimer and the phosphorescence maxima of the excimer can be effectively regulated. In addition, the temperature-dependent fluorescence and afterglow dual-emission color changes provide a new strategy for the design of highly accurate double-checked temperature sensors.

Up-Converting Luminescence and Temperature Sensing of Er3+/Tm3+/Yb3+ Co-Doped NaYF4 Phosphors Operating in Visible and the First Biological Window Range.

Accurate and reliable non-contact temperature sensors are imperative for industrial production and scientific research. Here, Er3+/Tm3+/Yb3+ co-doped NaYF4 phosphors were studied as an optical thermometry material. The typical hydrothermal method was used to synthesize hexagonal Er3+/Tm3+/Yb3+ co-doped NaYF4 phosphors and the morphology was approximately rod-like. The up-conversion emissions of the samples were located at 475, 520, 550, 650, 692 and 800 nm. Thermo-responsive emissions from the samples were monitored to evaluate the relative sensing sensitivity. The thermal coupled energy level- and non-thermal coupled energy level-based luminescence intensity ratio thermometry of the sample demonstrated that these two methods can be used to test temperature. Two green emissions (520 and 550 nm), radiated from 2H11/2/4S3/2 levels, were monitored, and the maximum relative sensing sensitivities reached to 0.013 K−1 at 297 K. The emissions located in the first biological window (650, 692 and 800 nm) were monitored and the maximum relative sensing sensitivities reached to 0.027 (R692/650) and 0.028 K−1 (R692/800) at 297 K, respectively. These results indicate that Er3+/Tm3+/Yb3+ co-doped NaYF4 phosphors have potential applications for temperature determination in the visible and the first biological window ranges.

Experimental Study of Large-Temperature-Range and Long-Period Monitoring for LNG Marine Auxiliary Based on Fiber Bragg Grating Temperature Measurement

Temperature is a key variable to evaluate the energy consumption and thermodynamic performance of traditional marine auxiliary machinery, chillers and piping systems. In particular, for the cryogenic storage tanks and fuel gas supply systems of LNG ships, explosion-proof and low-temperature-resistance properties bring new challenges to the onboard temperature measurement and monitoring. In order to promote the development of high-performance and safer monitoring systems for LNG ships, this paper adopted fiber Bragg grating (FBG) technology to ensure the measurement safety and accuracy of temperature sensors, and performs a series of experiments in a large temperature range on the chiller, pipeline, and cryogenic storage tank of an LNG ship and their long-term reliabilities. Firstly, the principle and composition of the designed FBG temperature sensors are introduced in detail, and the measurement accuracy and range of different metal-coated optical fibers were tested in a large temperature range and compared against the traditional thermistors. Then, the effects of different operating conditions of the LNG marine chiller system and cryogenic storage tank on the temperature measurements were investigated. In addition, the drift degrees of the optical fibers and industrial thermistors were analyzed to figure out their reliabilities for long-term temperature measurements. The results showed that for the long-period (16 months) monitoring of LNG ships in a large temperature range (105–315 K) under different shipping conditions, the optical temperature measurement based on FBG technology has sufficient accuracy and dynamic sensitivity with a higher safety than the traditional thermoelectric measurement. Besides, the ship vibration, ambient humidity, and great temperature changes have little impact on its measurement reliability and drifts. This research can provide references and technical supports to the performance testing systems of LNG ships and other relevant vessels with stricter safety standards.

A temperature compensated biaxial eFM accelerometer in Epi-seal process

This paper reports on the implementation of a vacuum-encapsulated bi-axial resonant accelerometer utilizing the electrostatic frequency modulation (eFM) technique. A novel flexure structure is designed to enable fully-decoupled in-plane displacement of the proof-mass to minimize cross-axis sensitivity. A differential readout scheme leveraging pairs of high quality factor (>12k) free-free beam resonators mitigates the first-order nonlinearities due to temperature effects and unwanted response to proof-mass spurious modes. The fabricated device measures a scale factor of 45.8 Hz/g with negligible cross-axis sensitivity (<2 %). Furthermore, the smooth characteristics of temperature coefficients of frequency (TCf) of the resonators enable an accurate temperature sensor using the common-mode frequency of the resonator pair. Applying a temperature compensation scheme utilizing the common-mode frequency output and pre-characterized TCfs, the accelerometer demonstrates superior performance measuring a VRW of 5.8 μg/√Hz and BI of 5.7 μg with a scale factor stability of 0.38 % and zero-g-output variation of 8.3 mg over temperature range from –20 °C to 80 °C.

An efficient numerical model for nonisothermal fluid flow through porous media

AbstractTemperature transient analysis has gained increasing attention in recent years due to the widespread application of downhole temperature sensors with high resolution and accuracy. With the fast, high resolution, and accurate temperature sensors in oil and gas wells, there is a need for the models and methods for analysis of transient temperature data in order to identify production and well completion problems and calculation of reservoir properties. Although there are many proposed analytical and numerical models, a majority of models are not applicable in practice due to limitations in basic assumptions and computational time. A procedure is proposed for numerical simulation of transient variations of sandface pressure and temperature. The fluid flow and energy equations are decoupled and solved simultaneously and the dependency of all properties on pressure and temperature is treated by nested iteration loops. The solution was compared with some analytical and numerical methods and was found as a computationally efficient calculation method. The benefit of the proposed method is its versatility, simplicity, speed, and ease of implementation in a programming language which can be used in conjunction with well test analysis models to correct for the effect of temperature and also constrain the uncertainty of reservoir parameters. It can also be embedded in compositional and reactive models to widen their applicability and accuracy.

Thermoelectric effect and devices on IVA and VA Xenes

AbstractEmerging Xenes, mostly group IVA and VA elemental two‐dimensional (2D) materials, have small and tunable band gaps between graphene and transition metal dichalcogenides, giving versatile electrical properties. While their microelectronic or optoelectronic properties are being extensively explored, there remains a lack of study on Xenes' uniquely advantageous thermoelectric performance. This review highlights state‐of‐the‐art experimental and theoretical progress in the thermoelectric effect and devices of IVA and VA Xenes. Vertically displaced, a.k.a. “buckled” or “puckered,” atomic arrays result in exotic and tunable electrical or thermal transport behaviors. Different from chemical doping strategies usually employed in bulk thermoelectric materials, 2D Xenes can be tuned by physical means, such as atomic layer control and quantum confinement effects. A precise and compatible platform for 2D thermoelectric effect and devices study is available via the engagement between micro/nanofabrication of 2D Xene transistors and thermal property measurement techniques. This review also reveals potential thermoelectric applications of Xenes and their compounds (Bi2Te3, Bi2Se3, etc.), such as accurate stretchable temperature sensors, fast terahertz photodetectors, and so on.image

Accurate Flexible Temperature Sensor Based on Laser‐Induced Graphene Material

Body temperature is an essential physiological index reflecting human health. Accurate measurements of body temperature play a vital role in the diagnosis and treatment of diseases. In this paper, a temperature sensor manufactured by laser‐induced graphene is introduced. This sensor has high measurement accuracy, simple preparation, and low production cost. The sensor is made of laser‐induced graphene and is easier to fabricate and operate than traditional thermal resistance sensors. The sensor is of high accuracy, is easy to manufacture, and is of low cost. The sensor has high accuracy and is linear between 30°C and 40°C in the human body temperature ranges. Laser‐induced graphene (LIG) sensor’s resistance value is correlated linearly with the temperature value, and compared with the infrared thermometer, the accuracy of the sensor is ±0.15°C while that of the infrared thermometer is ±0.30°C. The sensitivity of the LIG sensor is −0.04145%°C−1.