Low-cost Temperature Sensor Research Articles

Dual-Approach Calibration Unlocks Potential of Low-Power, Low-Cost Temperature and Humidity Sensors

Dual-Approach Calibration Unlocks Potential of Low-Power, Low-Cost Temperature and Humidity Sensors

Smart Temperature Sensor Design and High-Density Water Temperature Monitoring in Estuarine and Coastal Areas.

Acquiring in situ water temperature data is an indispensable and important component for analyzing thermal dynamics in estuarine and coastal areas. However, the long-term and high-density monitoring of water temperature is costly and technically challenging. In this paper, we present the design, calibration, and application of the smart temperature sensor TS-V1, a low-power yet low-cost temperature sensor for monitoring the spatial-temporal variations of surface water temperatures and air temperatures in estuarine and coastal areas. The temperature output of the TS-V1 sensor was calibrated against the Fluke-1551A sensor developed in the United States and the CTD-Diver sensor developed in the Netherlands. The results show that the accuracy of the TS-V1 sensor is 0.08 °C, while sensitivity tests suggest that the TS-V1 sensor (comprising a titanium alloy shell with a thermal conductivity of 7.6 W/(m °C)) is approximately 0.31~0.54 s/°C slower than the CTD-Diver sensor (zirconia shell with thermal conductivity of 3 W/(m °C)) in measuring water temperatures but 6.92~10.12 s/°C faster than the CTD-Diver sensor in measuring air temperatures. In addition, the price of the proposed TS-V1 sensor is only approximately 1 and 0.3 times as much as the established commercial sensors, respectively. The TS-V1 sensor was used to collect surface water temperature and air temperature in the western part of the Pearl River Estuary from July 2022 to September 2022. These data wells captured water and air temperature changes, frequency distributions, and temperature characteristics. Our sensor is, thus, particularly useful for the study of thermal dynamics in estuarine and coastal areas.

Remote Monitoring of Skin Temperature Through a Wristband Employing a Printed VO Sensor

The need for highly sensitive, environmentally stable, mechanically flexible, and low-cost temperature sensors for on-body measurements has been increasing with the wide adoption of personal healthcare-Internet-of-Things (H-IoT) devices. Printed electronics (PE) is a good platform for such sensors because it enables the realization of flexible devices through simple and rapid methods at a relatively low cost. However, previously reported printed temperature sensors suffer from poor sensitivity and/or environmental instability. In this article, we report a custom tungsten (W)-doped vanadium dioxide (VO2) ink-based screen-printed temperature sensor having the highest temperature coefficient of resistance (TCR) of 2.78% C-1 with a resolution of 0.1 °C between 30 °C and 40 °C. To protect it from environmental effects, a fluoropolymer-basedpassivation layer is added for accurate temperature readings even in 90% relative humidity. The sensor is printed on a flexible substrate and shows minimal deterioration in performance over 1000 bending cycles. For wearability and remote monitoring, the sensor is integrated with a custom Bluetooth low energy (BLE) wireless readout in the form of wristband. The BLE readout comprises an ultrathin and flexible patch antenna optimized for both BLE bandwidth (BW) and human wearability. It demonstrates a minimal specific absorption rate (SAR) value of only 0.068 W/kg, making it safe to wear. Despite the antenna’s thin structure (0.004λ), it has a gain of 1.65 dBi, enabling an excellent communication range. The proposed wristband is tested on ten volunteers and under daily activities, which shows promising results with a maximum error of 0.16 °C with reference to those of a commercial thermometer.

Canopy height impacts on the growing season and monthly microclimate in a burned forest of British Columbia, Canada

Forest canopies can buffer seedlings from extreme climate conditions. Yet, how disturbed forest canopies influence microclimate is not well understood, despite the important implications of microclimate for seedling establishment and post-disturbance successional trajectories. Better understanding of the relationship between a forest canopy and sub-canopy temperature and moisture conditions requires easily acquired and continuous forest canopy data, which is increasingly available due to new technology. Here, we measured canopy height using a remotely piloted aircraft (RPA) and monitored microclimate with low-cost temperature and soil moisture sensors in a sub-boreal forest impacted by fires of variable severity. We used regression models to investigate how differences in canopy height influenced microclimate variables. Mean growing season temperatures at -8 cm (soil), 0 cm (surface), and 15 cm (near-surface) relative to the ground surface were higher under shorter more disturbed canopies. Soil temperature was most sensitive to canopy height differences: linear models for the observed data range predicted a 2.0 °C increase in mean growing season soil temperature with every 10 m decrease in canopy height. We observed a weak negative relationship between canopy height and mean growing season soil moisture. We found that canopy height summarized at moderate resolution (15 m) better explained differences in temperature in our disturbed landscape. This work informs future methods to produce gridded microclimate datasets and outlines the impact of disturbed forest structure on microclimate variables. Our results show that the characteristics of the forest canopy remaining after a burn impact microclimates, which has important implications for post-fire ecosystems.

Capacitive Temperature Sensing via Displacement Amplification

We propose the realization of capacitive temperature sensors based on the concept of displacement amplification. Our design features two high coefficient of thermal expansion (CTE) metallic layers separated by a low-CTE dielectric layer; conductive and dielectric layers are then separated by a thin air gap and glued together at a few locations. As the temperature increases, the high-CTE layer tends to expand more than the low-CTE one. Owing to the constraint to planar expansion imposed by the low-CTE layer, this results in large out-of-plane displacements of the high-CTE layer – hence the displacement amplification term. In our case, the high-CTE layer buckles and causes a reduction of the gap between conductive and dielectric layers; in turn, this results in a large change of capacitance. First, we illustrate the concept via numerical simulations. Then, we realize a low-cost prototype of such sensor by using aluminum foil as conductor, paper as dielectric, and by gluing the layers together with cyanoacrylate. Our results demonstrate the potential of this simple design as a route towards efficient and low-cost temperature sensors.

Heterogeneous hybrid extreme learning machine for temperature sensor accuracy improvement

High-accuracy temperature sensors usually come at a high cost, making them cost-ineffective solutions for mass-produced commercial products. The overall product cost can be one of the decisive factors for its success (e.g., commercial freezers). On the other hand, low-cost temperature sensors are viable options, but they suffer from accuracy issues. This paper presents a system aimed at improving the accuracy of a low-cost temperature sensor using a hybrid machine learning algorithm. The system utilizes a low-cost single-board computer connected to a series of sensors to collect data. These data are then used as an input vector to the proposed linear regression heterogeneous hybrid extreme learning machine (LR-HE-HyELM), a supervised learning algorithm responsible for increasing the sensors’ accuracy. The proposed linear regression heterogeneous extreme learning machine (LR-HE-HyELM) combines linear regression with the self-adaptive heterogeneous hybrid extreme learning machine (SA-He-HyELM) algorithm. SA-He-HyELM is an extreme learning machine (ELM) based algorithm that utilizes a genetic algorithm (GA) for the creation of an optimized single-layer neural network (SLNN) having different combinations of custom-created neurons at its hidden layer. The proposed training algorithm has all the advantages of SA-He-HyELM. These advantages include utilizing low-order and higher-order neurons in the same network while retaining ELM’s simplicity by making all GA parameters self-adaptive. The utilization of low-order and higher-order unit types in the same network resulted in optimized neural networks with better generalization ability than those with the same neuron types in their hidden layer. The creation of SA-He-HyELM was nature-inspired since the human brain contains different neuron types. The proposed system is tested with eight existing methods, with most of them having different neuron types in the hidden layer. The experimental results involved using a custom-created regression dataset having measurements taken from a commercial fridge. The proposed method managed to get the lowest mean square error (MSE) compared to the other eight methods.

Lack of vegetation exacerbates exposure to dangerous heat in dense settlements in a tropical African city

Both climate change and rapid urbanization accelerate exposure to heat in the city of Kampala, Uganda. From a network of low-cost temperature and humidity sensors, operational in 2018–2019, we derive the daily mean, minimum and maximum Humidex in order to quantify and explain intra-urban heat stress variation. This temperature-humidity index is shown to be heterogeneously distributed over the city, with a daily mean intra-urban Humidex Index deviation of 1.2 ∘C on average. The largest difference between the coolest and the warmest station occurs between 16:00 and 17:00 local time. Averaged over the whole observation period, this daily maximum difference is 6.4 ∘C between the warmest and coolest stations, and reaches 14.5 ∘C on the most extreme day. This heat stress heterogeneity also translates to the occurrence of extreme heat, shown in other parts of the world to put local populations at risk of great discomfort or health danger. One station in a dense settlement reports a daily maximum Humidex Index of 40 ∘C in 68% of the observation days, a level which was never reached at the nearby campus of the Makerere University, and only a few times at the city outskirts. Large intra-urban heat stress differences are explained by satellite earth observation products. Normalized Difference Vegetation Index has the highest (75%) power to predict the intra-urban variations in daily mean heat stress, but strong collinearity is found with other variables like impervious surface fraction and population density. Our results have implications for urban planning on the one hand, highlighting the importance of urban greening, and risk management on the other hand, recommending the use of a temperature-humidity index and accounting for large intra-urban heat stress variations and heat-prone districts in urban heat action plans for tropical humid cities.

Fabrication of 3D Temperature Sensor Using Magnetostrictive Inkjet Printhead

Recently, the three-dimensional (3D) printing technique has received much attention for the shape forming and manufacturing. In this work, for the first time, we present the fabrication of inkjet printed 3-diamentional (3D) low cost temperature sensor on a 3D shaped thermoplastic substrate suitable for packaging, flexible electronics and other printed application. The design, fabrication and testing of a 3D printed temperature sensor is presented. The sensor pattern is designed using computer-aided design (CAD) program and fabricated by inkjet printing with a drop-on-demand (DoD) using magnetostrictive inkjet printhead at room temperature. The sensing pattern is printed using commercially available conductive silver nanoparticles ink (AgNPs). The moving speed of 90 mm/min is chosen to print the sensor pattern. The inkjet printed temperature sensor is demonstrated and characterized with good electrical properties, showing a good sensitivity and linearity. The results indicate that the 3D inkjet printing technology may have a great applied potential in sensor fabrication.

Radiosity from Individual Urban Landscape Elements Measured Using a Modified Low-Cost Temperature Sensor

Loss of green space in our suburban environment is contributing to increased urban heat. The material properties of surface treatments or landscape elements (LEs) are a determining factor in the amount, timing, and type of radiation present in the local environment. Landscape designers can use this information to better design for urban heat management, as emitted and reflected radiation (radiosity) from LEs can affect pedestrians via heat stress and glare and affect energy usage in buildings and houses if the landscape sky view factor is low. Low-cost black painted iButton temperature sensors were successfully used as radiometers to concurrently measure the daytime radiosity from 19 LEs samples located on an oval in the warm temperate climate of Perth, (Australia). Normalisation against gloss white paint on polystyrene removed the effect of varying weather conditions. Each LE had the same normalised average radiosity (DRav) between seasons (within ±5%), meaning the relative radiosity of new LEs can be measured on any day. White and lighter coloured LEs had the highest DRav and would have the most detrimental effect on nearby objects. Plants and moist LEs had the least DRav and would be most beneficial for managing local daytime urban heat. Measuring relative radiosity with iButtons presents a new way to examine the effect of LEs on the urban environment.

Towards low-cost QEPAS sensors for nitrogen dioxide detection

Increasing awareness of the adverse health effects of air pollution leads to a demand of low-cost sensors for the measurement of pollutants such as NO2. However, commercially available low-cost sensors lack accuracy and long-term stability, and suffer from cross-sensitivity to other gases. These drawbacks can be overcome by the method of quartz-enhanced photoacoustic spectroscopy (QEPAS). In QEPAS modulated light is absorbed by the NO2 molecules, which results in the production of a sound wave. The sound wave is detected by resonance of a quartz tuning fork, which results in a measurable electric signal. Due to the small size of the tuning forks, the gas sensing element can be smaller than 1 cm3. We present the first bare fork QEPAS setup for the ppb-level detection of NO2, which is ideally suited for environmental trace gas detection without the need of using micro-resonators. Micro-resonators are commonly used to amplify photoacoustic signals. However, micro-resonators have different dependencies on environmental conditions than tuning forks, which makes them difficult to operate in changing conditions. In contrast, our bare fork QEPAS setup is more robust and easily adopted by the use of a low-cost temperature and humidity sensor. By using acoustic filters the integration time could be increased to offer higher sensitivity at a continuous flow rate of 200 std cm3 min−1. The 1σ noise equivalent concentration is determined to 21 ppb NO2 in synthetic air for 120 s measurement time, allowing detection which satisfies international health and safety standards thresholds.

Empirical evaluation of two-dimensional unsteady hydraulic models for applications in floodplain forest ecology

ABSTRACT Two-dimensional unsteady hydraulic models can be attractive tools for characterizing the inundation dynamics relevant to floodplain forest ecosystems because of their ability to make dynamic, spatially continuous predictions. Such models are challenging to evaluate because they require spatially and temporally extensive datasets that are expensive or logistically difficult to collect. As a result, there are few examples of model evaluation methods intended for ecological applications in terrestrial or semi-terrestrial areas. Here, we demonstrated one evaluation approach using 3D arrays of low-cost temperature sensors to characterize wetted extent, depth and duration. Simulations of observed flood events in two geomorphically distinct river reach exhibited strong agreement with indirect measurements of wetted extent (>80%). Errors in depth prediction (average 24–29 cm) were similar to those of other ecological applications. Discrepancies in simulated flood durations were almost exclusively less than 24 h in length, which we interpreted as acceptable for the intended model applications. We found that unsteady 2D hydraulic models can capture a level of reality in wetted extent, depth and duration appropriate for characterizing key environmental gradients thought to drive forest composition. We also demonstrated a spatio-temporal model evaluation method that can be tailored to specific ecological research questions.

RoC: Robust and Low-Complexity Wireless Indoor Positioning Systems for Multifloor Buildings Using Location Fingerprinting Techniques

Most existing wireless indoor positioning systems have only success performance requirements in normal operating situations whereby all wireless equipment works properly. There remains a lack of system reliability that can support emergency situations when there are some reference node failures, such as in earthquake and fire scenarios. Additionally, most systems do not incorporate environmental information such as temperature and relative humidity level into the process of determining the location of objects inside the building. To address these gaps, we propose a novel integrated framework for wireless indoor positioning systems based on a location fingerprinting technique which is called the Robust and low Complexity indoor positioning systems framework (RoC framework). Our proposed integrated framework consists of two essential indoor positioning processes: the system design process and the localization process. The RoC framework aims to achieve robustness in the system design structure and reliability of the target location during the online estimation phase either under a normal situation or when some reference nodes (RNs) have failed. The availability of low-cost temperature and relative humidity sensors can provide additional information for the location fingerprinting technique and thereby reduce location estimation complexity by including this additional information. Experimental results and comparative performance evaluation revealed that the RoC framework can achieve robustness in terms of the system design structure, whereby it was able to provide the highest positioning performance in either fault-free or RN-failure scenarios. Moreover, in the online estimation phase, the proposed framework can provide the highest reliability of the target location under the RN-failure scenarios and also yields the lowest computational complexity in online searching compared to other techniques. Specifically, when compared to the traditional weighted k-nearest neighbor techniques (WKNN) under the 30% RN-failure scenario at Building B, the proposed RoC framework shows 74.1% better accuracy performance and yields 55.1% lower computational time than the WKNN.

Construction of a Compact Low-Cost Radiation Shield for Air-Temperature Sensors in Ecological Field Studies.

Low cost temperature sensors are increasingly used by ecologists to assess climatic variation and change on ecologically relevant scales. Although cost-effective, if not deployed with proper solar radiation shielding, the observations recorded from these sensors will be biased and inaccurate. Manufactured radiation shields are effective at minimizing this bias, but are expensive compared to the cost of these sensors. Here, we provide a detailed methodology for constructing a compact version of a previously described custom fabricated radiation shield, which is more accurate than other published shielding methods that attempt to minimize shield size or construction costs. The method requires very little material: corrugated plastic sheets, aluminum foil duct tape, and cable ties. One 15 cm and two 10 cm squares of corrugated plastic are used for each shield. After cutting, scoring, taping and stapling of the sheets, the 10 cm squares form the bottom two layers of the solar radiation shield, while the 15 cm square forms the top layer. The three sheets are held together with cable ties. This compact solar radiation shield can be suspended, or placed against any flat surface. Care must be taken to ensure that the shield is completely parallel to the ground to prevent direct solar radiation from reaching the sensor, possibly causing increased warm biases in sun-exposed sites in the morning and afternoon relative to the original, larger design. Even so, differences in recorded temperatures between the smaller, compact shield design and the original design were small (mean daytime bias = 0.06 °C). Construction costs are less than half of the original shield design, and the new design results in a less conspicuous instrument that may be advantageous in many field ecology settings.

Bimorph material/structure designs for high sensitivity flexible surface acoustic wave temperature sensors

A fundamental challenge for surface acoustic wave (SAW) temperature sensors is the detection of small temperature changes on non-planar, often curved, surfaces. In this work, we present a new design methodology for SAW devices based on flexible substrate and bimorph material/structures, which can maximize the temperature coefficient of frequency (TCF). We performed finite element analysis simulations and obtained theoretical TCF values for SAW sensors made of ZnO thin films (~5 μm thick) coated aluminum (Al) foil and Al plate substrates with thicknesses varied from 1 to 1600 μm. Based on the simulation results, SAW devices with selected Al foil or plate thicknesses were fabricated. The experimentally measured TCF values were in excellent agreements with the simulation results. A normalized wavelength parameter (e.g., the ratio between wavelength and sample thickness, λ/h) was applied to successfully describe changes in the TCF values, and the TCF readings of the ZnO/Al SAW devices showed dramatic increases when the normalized wavelength λ/h was larger than 1. Using this design approach, we obtained the highest reported TCF value of −760 ppm/K for a SAW device made of ZnO thin film coated on Al foils (50 μm thick), thereby enabling low cost temperature sensor applications to be realized on flexible substrates.

PLA conductive filament for 3D printed smart sensing applications

PurposeThis paper aims to present a study on a commercial conductive polylactic acid (PLA) filament and its potential application in a three-dimensional (3D) printed smart cap embedding a resistive temperature sensor made of this material. The final aim of this study is to add a fundamental block to the electrical characterization of printed conductive polymers, which are promising to mimic the electrical performance of metals and semiconductors. The studied PLA filament demonstrates not only to be suitable for a simple 3D printed concept but also to show peculiar characteristics that can be exploited to fabricate freeform low-cost temperature sensors.Design/methodology/approachThe first part is focused on the conductive properties of the PLA filament and its temperature dependency. After obtaining a resistance temperature characteristic of this material, the same was used to fabricate a part of a 3D printed smart cap.FindingsAn approach to the characterization of the 3D printed conductive polymer has been presented. The major results are related to the definition of resistance vs temperature characteristic of the material. This model was then exploited to design a temperature sensor embedded in a 3D printed smart cap.Practical implicationsThis study demonstrates that commercial conductive PLA filaments can be suitable materials for 3D printed low-cost temperature sensors or constitutive parts of a 3D printed smart object.Originality/valueThe paper clearly demonstrates that a new generation of 3D printed smart objects can already be obtained using low-cost commercial materials.

A robust and accurate surrogate method for monitoring the frequency and duration of combined sewer overflows

Discharges of untreated wastewater from combined sewer overflows (CSOs) can affect hydraulic stress and have significant environmental impacts on receiving water bodies. Common flow rate and water level sensors for monitoring of CSO events are expensive in terms of investment costs, installation, operation and maintenance. This paper presents a novel surrogate method to detect CSO events by using two low-cost temperature sensors. The novelty is the experimental setup for installation of temperature sensors in CSO structures and an algorithm developed to automatically calculate the duration of CSO events considering the response time of the system. The occurrence and duration of CSO events is computed based on the convergence of the two temperature signals. The method was tested under field conditions in a CSO structure, and the results were compared to the information gathered from a parallel installed flow sensor. The application of two temperature sensors installed inside a CSO structure was proven to be robust and accurate for the automatic detection of the occurrence and duration of CSO events. Within the 7-month test phase, 100% of the 20 CSO events could be detected without false detections. The accuracy of detecting the start and end of the CSO events was 2 min in comparison to the flow sensor.

STCS: a practical solar radiation based temperature correction scheme in meteorological WSN

Wireless sensor network (WSN) has been used to construct intelligent meteorological observation network to achieve fine observation data. But the accuracy of the low-cost onboard temperature sensor is too low to meet demand in some applications. To resolve this problem, some pioneer methods have been proposed. But the precision always cannot reach the ideal effect. We propose a solar radiation based air temperature error correction scheme (STCS). After some pretreatments such as interpolation, and time transformation, the relationship between solar radiation and temperature error is established by training GA-BP (Back Propagation neural network optimised by Genetic Algorithm). Then the trained neural network is applied to the temperature correction. Finally, it is the smoothing process to the corrected data. Expermental results show that STCS achieves an average error of smaller than 0.34°C. Consider to three core indicators, our results can be improved by 14%, 6% and 12% respectively.

Attractive mechanical properties of a lightweight highly sensitive bi layer thermistor: polycarbonate/organic molecular conductor

The paper covers some of the basic mechanical characteristics of a recently developed bi layer thermistor: polycarbonate/(001) oriented layer of organic molecular conductor α’-(BEDT-TTF)2IxBr3-x, were BEDT-TTF=bis(ethylenedithio)tetrathiafulvalen. The nano and macro mechanical properties have been studied in order to use this flexible, low cost thermistor in sensing applications by proper way. The nano-mechanical properties of the temperature sensitive semiconducting layer of α’-(BEDT-TTF)2IxBr3-x were tested using nanoindentation method. The value of Young's modulus in direction being perpendicular to the layer plan was found as 9.0 ±1.4 GPa. The macro mechanical properties of the thermistor were studied using a 5848 MicroTester. The tensile tests showed that basic mechanical characteristics of the thermistor are close to those of polycarbonate films. This indicates a good mechanical strength of the developed sensor. Therefore, the thermistor can be used in technologies that need to be instrumented with highly robustness lightweight low cost temperature sensors. The paper also reports synthetic details on fabricating temperature sensing e-textile. As the temperature control is becoming more and more important in biomedical technologies like healthcare monitoring, this work strongly contributes on the ongoing research on engineering sensitive conducting materials for biomedical applications.

Detection of embryo mortality and hatch using thermal differences among incubated chicken eggs

Accurate diagnosis of both the stage of embryonic mortality and the hatch process in incubated eggs is a fundamental component in troubleshooting and hatchery management. However, traditional methods disturb incubation, destroy egg samples, risk contamination, are time and labour-intensive and require specialist knowledge and training. Therefore, a new method to accurately detect embryonic mortality and hatching time would be of significant interest for the poultry industry if it could be done quickly, cheaply and be fully integrated into the process. In this study we have continuously measured individual eggshell temperatures and the corresponding micro-environmental air temperatures throughout the 21 days of incubation using standard low-cost temperature sensors. Moreover, we have quantified the thermal interaction between eggs and air by calculating thermal profile changes (temperature drop time, drop length and drop magnitude) that allowed us to detect four categories of egg status (infertile/early death, middle death, late death and hatch) during incubation. A decision tree induction classification model accurately (93.3%) predicted the status of 105 sampled eggs in comparison to the classical hatch residue breakout analyses. With this study we have provided a major contribution to the optimisation of incubation processes by introducing an alternative method for the currently practiced hatch residue breakout analyses.

Research and Analysis of a Portable Precision Temperature Sensor

Based on the excellent performance of Pt100, a portable low-cost precision temperature sensor has been designed, whose core chips are REF03, AD8603, AD7788 and precision resistors. Constant current source (CCS) for 4-wire Pt100 is constituted by REF03, AD8603 and precision resistors. AD7788 measures the differential signal on Pt100 and suppresses the common mode interference signal. Analysis software running on the micro control unit (MCU) filters the digital code from AD7788, and then calculates the current temperature value according to the resistance-temperature mathematical model of Pt100. Analysis and experimental results show that the temperature measurement accuracy of the sensor can reach ±1°C within the range of 0°C-650°C.