Temperature Research Articles

Bright Quantum-Grade Fluorescent Nanodiamonds.

Optically accessible spin-active nanomaterials are promising as quantum nanosensors for probing biological samples. However, achieving bioimaging-level brightness and high-quality spin properties for these materials is challenging and hinders their application in quantum biosensing. Here, we demonstrate bright fluorescent nanodiamonds (NDs) containing 0.6-1.3-ppm negatively charged nitrogen-vacancy (NV) centers by spin-environment engineering via enriching spin-less 12C-carbon isotopes and reducing substitutional nitrogen spin impurities. The NDs, readily introduced into cultured cells, exhibited improved optically detected magnetic resonance (ODMR) spectra; peak splitting (E) was reduced by 2-3 MHz, and microwave excitation power required was 20 times lower to achieve a 3% ODMR contrast, comparable to that of conventional type-Ib NDs. They show average spin-relaxation times of T1 = 0.68 ms and T2 = 3.2 μs (1.6 ms and 5.4 μs maximum) that were 5- and 11-fold longer than those of type-Ib, respectively. Additionally, the extended T2 relaxation times of these NDs enable shot-noise-limited temperature measurements with a sensitivity of approximately . The combination of bulk-like NV spin properties and enhanced fluorescence significantly improves the sensitivity of ND-based quantum sensors for biological applications.

Measurement of non-invasive rectal and ear temperature in inpatients ≥ 18 years old: A cross-sectional comparative study.

Body temperature measurement is a fundamental requirement for clinical decisions in nursing care, medical diagnosis, and treatment. Therefore, it is pivotal that body temperature measurements are accurate and precise. To test the diagnostic accuracy of an ear temperature screening procedure among adult hospitalized patients. Further aims were to test the precision of the measurements being carried out by trained registered nurses compared with daily routine practice and to investigate patients' preferences for different measurement methods. In Aalborg University Hospital, 274 patients were included in a cross-sectional comparative study. Each patient had four temperature measurements and responded to a survey regarding their preference for measurement. Bland-Altman analysis was used to evaluate the difference between ear- and rectal measurements. Sensitivity and specificity were evaluated at different cut-off points. The ear temperature was 0.1 to 0.2 °C lower than rectal temperature. At a cut-off point at 37.5 °C an ear thermometer is accurate and can be used for screening, while higher cut-off points risk missing patients with fever. There was no significant difference in the mean temperature measured by a trained registered nurse or other staff members and patients. Patients preferred ear measurements to rectal measurements. The tested ear thermometer is accurate for screening fever in an adult population during hospital admission. Using ear measurement as a screening tool can contribute to a less resource-demanding care activity and a more convenient alternative to rectal measurements in hospitalised patients.

The influence of NOx, temperature, wind and total radiation on the level of ozone concentration in the Upper Silesian agglomeration

In 2019, ozone was responsible for about 365,000 premature deaths worldwide (6.21 million healthy life years lost) and acute ozone exposure led to 16,800 premature deaths in the European Union. The aim of the study was to estimate the influence of NO, NO2, wind direction (WD) wind speed (WS), air temperature (TA), and total radiation (GLR) on ozone concentration levels. Data provided by 3 automatic air quality monitoring stations of the Regional Environmental Protection Inspectorate in Katowice, were used in this study. The measurements were conducted in from January 1 2009 to December 31 2017. The data obtained from the measuring stations were statistically analysed. The study showed that the strongest influencing factors for O3 values are air temperature and total radiation, with each showing a high correlation with ozone concentration. NO and NO2 had a dual effect on O3 concentration, causing an increase in ozone concentration at low NO and NO2 concentrations and a decrease in ozone concentration at higher NO and NO2 concentrations. We noted that the direction of the wind had very little effect on the concentration of O3. The influence of wind speed on the O3 level was also small, but stronger than that of the wind direction. The research shows that in the analysed years for selected measuring stations the strongest factors influencing O3 concentration are air temperature and total radiation, the NO and NO2 concentrations had a dualistic effect on the O3 concentration.

Resolving the Corbino Shockley-Ramo paradox for hydrodynamic current noise

Johnson noise thermometry enables direct measurement of the electron temperature, a valuable probe of many-body systems. Practical use of this technique calls for nonequilibrium generalizations of the Johnson-Nyquist theorem. For a hydrodynamic Corbino device, however, a naive use of the Shockley-Ramo theorem alongside the “Corbino paradox” leads to yet another paradox: current noise through the contacts would seem to be completely insensitive to bulk fluctuations. In this work, we resolve the unphysical “Corbino Shockley-Ramo paradox” by correctly formulating the hydrodynamic Shockley-Ramo problem. This allows us to properly formulate the problem of current noise in a hydrodynamic multiterminal device of arbitrary geometry, as well as validate a previously unjustified assumption for rectangular geometry results. As an example, we compute the Johnson noise in a hydrodynamic Corbino device, where we find a suppression of Johnson noise with magnetic field. This unusual characteristic serves as a qualitative signature of viscous hydrodynamic behavior. Published by the American Physical Society 2024

Zero and double-knotted droplet-shaped optical fiber sensor for rapid temperature variation monitoring

Compact, simple optical fiber sensors based on a macro-bend fiber configuration were fabricated and proposed for instantaneous temperature measurement. These fiber sensors were manufactured by straightforwardly mechanically bending two different pieces of single-mode fiber (SMF) into zero-knotted droplet-shaped and double-knotted droplet-shaped, separately, with radii of a few millimeters. The sharp bending excites mode interferences among the core mode and the stimulated modes transmitting in the cladding region of the silica SMF. Many resonant interference fringes were perceived in the transmission signal and were noticed to shift to a shorter spectral region with the rise of environmental temperature (ET). The resultant data prove the practicality of the fabricated optical fiber sensors with excellent temperature sensitivities of about −1.128 nm/°C and −1.904 nm/°C, for zero-knotted droplet-shaped and double-knotted droplet-shaped, respectively, which is several times larger than sensors based on straight-transmitted fiber constructions. The proposed sensors’ structures may contribute effectively to temperature variations monitoring for environmental or industrial uses due to their large thermo-optical coefficient of the silica core and the big RI difference between the core and cladding of the bending fiber section.

Jellyfish‐Inspired High‐Sensitivity Pressure‐Temperature Sensor

AbstractIn recent years, biomimetic high‐sensitivity tactile sensors increasingly become a research focus. Specifically, hydrogel tactile sensors based on ionic‐electronic mechanisms gain widespread attention due to their excellent pressure sensitivity. However, due to the saturation deformation of sensitive elements, these sensors struggle to accurately measure pressure under high‐pressure conditions. Additionally, as hydrogels cause signal drift under constant pressure and ionic‐electronic mechanisms are susceptible to temperature interference, these characteristics limit their application. Inspired by the jellyfish's “mesoglea” and “ectoderm” structures, a novel tactile sensor is developed that combines the ionic‐electronic mechanism with a filling structure. This sensor integrates the hydrogel with a flexible framework to create a jellyfish‐like umbrella structure. This design achieves extremely high pressure sensitivity and improves signal drift. By utilizing the different response characteristics of the capacitance and resistance values of a single sensing element to pressure and temperature changes, it enables simultaneous measurement of temperature and pressure, thereby enhancing its potential for application in wearable electronics and robotics.

Development of a low-cost, 3-D printed, aspirated air-temperature measurement radiation shield

Abstract Accurate measurement of ambient air temperature is critical to numerous applications. This task is complicated by solar radiation which can heat the air-temperature sensor body, causing it to record temperatures in excess of the true air temperature. The standard way to protect against this interference is to house the sensors in passive radiation shields which block the majority of solar radiation. However, solar radiation still heats the shield body which can then heat the sensor, causing measurement errors under low-wind conditions. An alternative method is to aspirate the sensor using a fan to force air movement over the sensor body. Aspirated temperature sensing units are commercially available, but they can be expensive and consume a significant amount of power. Here, we present the design and initial rigorous testing of a low-cost, low-power aspirated temperature sensing unit designed to integrate with any low-cost distributed sensor platform. The design uses a freely available, custom designed 3-D printed housing that enables rapid assembly. The new, open source unit is shown to perform as well as passive shields and commercial aspirated shields of significantly higher cost. This success shows the potential of leveraging 3-D printing technologies for other housing units. Further testing is needed to better quantify long-term robustness of the shield.

Heat disproportionately kills young people: Evidence from wet-bulb temperature in Mexico.

Recent studies project that temperature-related mortality will be the largest source of damage from climate change, with particular concern for the elderly whom it is believed bear the largest heat-related mortality risk. We study heat and mortality in Mexico, a country that exhibits a unique combination of universal mortality microdata and among the most extreme levels of humid heat. Combining detailed measurements of wet-bulb temperature with age-specific mortality data, we find that younger people who are particularly vulnerable to heat: People under 35 years old account for 75% of recent heat-related deaths and 87% of heat-related lost life years, while those 50 and older account for 96% of cold-related deaths and 80% of cold-related lost life years. We develop high-resolution projections of humid heat and associated mortality and find that under the end-of-century SSP 3-7.0 emissions scenario, temperature-related deaths shift from older to younger people. Deaths among under-35-year-olds increase 32% while decreasing by 33% among other age groups.

Design of a Nested Hollow-Core Anti-Resonant Fiber Sensor for Simultaneous Measurement of Temperature and Strain

A highly sensitive sensor, which can detect the temperature and strain simultaneously, is proposed using a hollow-core anti-resonant fiber with composite nested tubes. The sensing fiber contains two kinds of nested tubes, and two different sensing mechanisms, the resonance coupling effect and the intermodal interference, are realized in the same section of a hollow-core anti-resonant fiber fully filled with ethanol. Five conjoined nested anti-resonant tubes are introduced to suppress the confinement loss of the higher-order mode LP02. One hybrid conjoined nested tube, which consists of a half-circular anti-resonant tube and a half-circular resonant tube, is introduced to induce a resonant coupling between the LP02 mode in the core and the dielectric mode in the nested resonant tubes. Numerical investigations demonstrate the shifts of the feature wavelengths of the resonance coupling effect, and the intermodal interference shows different velocities with temperature and strain, while a simultaneous measurement of temperature and strain can be realized with high sensitivities (3.36 nm/°C and −0.003 nm/με to temperature and strain, respectively). Since the sensor can be fabricated by full infiltration with liquid into the large-size core and cladding tubes of hollow-core anti-resonant fibers, and special post-processing, such as selective infiltration or coating, is notneeded. The proposed sensors based on hollow-core anti-resonant fibers with functional liquid infiltration provide a more efficient and versatile platform for the temperature and strain sensing.

Constructing Dual-Emitting via Energy Transfer in Sr2ScO3F:Mn4+,Nd3+ Phosphors For High-Performance Temperature Sensing.

The development of high-sensitivity thermometers has become increasingly important in recent years as the demand for noncontact optical temperature measurement has grown. Herein, we report a series of Sr2ScO3F:Mn4+,Nd3+ (SSOF:Mn4+,Nd3+) phosphors synthesized by the traditional high-temperature solid-state method for high-performance temperature sensing. Sr2ScO3F possesses a [ScO6F] octahedron and [SrO9F] tridecahedron, doped Mn4+ ions occupy the octahedral sites and emit deep red light at 650-750 nm, and doped Nd3+ ions occupy the tridecahedral sites and emit near-infrared light. Due to the energy transfer from the Mn4+ to Nd3+ ions, the SSOF:Mn4+,Nd3+ phosphor exhibits intense dual-center emissions. On the basis of the different thermal quenching behaviors of the Mn4+ and Nd3+ ions, high-sensitivity ratiometric thermometers can be achieved using fluorescence intensity ratio technology, and the high relative sensitivity (Sr) and absolute sensitivity (Sa) reached 3.40% K-1 (at 343 K) and 0.2580 K-1 (at 483 K), respectively. Our work not only studies the photoluminescence characteristics of SSOF:Mn4+,Nd3+ phosphors but also points out that SSOF:Mn4+,Nd3+ phosphors have important potential applications in optical temperature measurement.

Vanadium Dioxide-Based Terahertz Metamaterials for Non-Contact Temperature Sensor

Temperature sensors play important roles in wide-spreading human activities. The non-contact method of using temperature sensors offers significant advantages but faces challenges in detection precision. In this work, a double-layer asymmetric terahertz (THz) metamaterial combined with phase transition oxide was proposed to realize non-contact temperature sensor with high sensitivity. The metamaterial exhibited band-stop filtering effects in the simulated transmission spectra. Temperature changes induced a reversible phase transition in VO2, resulting in altered conductivity. The numerical results indicated that the S21 parameter increases from −44.33 dB to −4.78 dB at a frequency of 1.22 THz as the conductivity of the VO2 film increases from 10 to 5000 S/m, achieving a modulation depth of 89%. In addition, the 86 nm thick VO2 film underwent a phase transition in the temperature range of 54.93 °C to 66.93 °C, achieving a sensitivity of 1.82 dB/°C for temperature sensing. This work provided great insights into the development of metamaterials based on high-precision temperature measurement.

In situ embedding of resistance temperature detectors with the use of laser-foil-printing additive manufacturing

Abstract Additive manufacturing (AM) allows sensor embedding with the freedom of geometry flexibility. This research aims to experimentally determine the viability of integrating Platinum resistance temperature detectors (RTDs) into AM 304L stainless steel parts using laser foil printing (LFP) for real-time measurement applications. Using metal foils as a feedstock in LFP provides higher conductivity and faster cooling rate resulting in higher strength compared to powder-bed AM. However, one of the common challenges during the laser aided metal AM processes is that the heat accumulation can damage the embedded sensor. This study uses spot pattern welding processing strategy to mitigate these process-related risks by minimizing the melt pool volume during the layered fabrication process. High-temperature resistant ceramic adhesives are employed to fill the gap, and to create a conductive interface between the feedstock and the sensor. After curing the ceramic adhesives, in situ temperature measurement data are collected to investigate the success of the sensor embedding process. This work demonstrates the feasibility for LFP smart manufacturing, offering the potential for component embedding and an advanced real-time monitoring system.

Gridded global dataset of industrial water use predicted using the Random Forest

Spatially distributed industrial water use (IWU) data are essential for effective region-specific water resource management. Such data are often scarce in underdeveloped and developing countries. We propose a random forest regression model to predict IWU at a spatial resolution of 0.5° by combining socioeconomic, climatic, and geographical datasets. These datasets included nighttime light (NL), global power plants, country-wise IWU, elevation data (DEM), gross domestic product (GDP), road density (RD), cropland (CRP), wetland (WLND), population (POP), precipitation (PCP), temperature (TEMP), wet days (WET) per year, and potential evapotranspiration (PET). The results show that RD, CRP, POP, GDP, DEM, and TEMP were the most influential variables. We assessed the accuracy of the global IWU map using published and observed datasets from various sources for the major industrialized countries such as the USA and China from 2000 to 2015. The predicted global map shows a reasonable distribution of grid-wise values for highly industrialized countries and data-scarce regions. Thus, fine-resolution maps can support local planning and decision-making for large basins worldwide.

Application of Anomaly Detection to Real World Monitoring Data of a Rail-Road Bridge

The growing importance of automatic bridge condition monitoring, driven by the need to sustain infrastructure and reduce construction-related CO2 emissions, has led to increased deployment of sensor technologies on bridges. However, the overwhelming volume of data generated necessitates innovative approaches. Machine learning (ML), particularly the Regression Based Thermal Response Prediction Method (RBTRPM), offers a promising solution. RBTRPM employs ML models to predict structural responses from temperature measurements. These predictions are compared with actual responses to identify anomalies. While introduced in 2014, its practical applicability has been limited. This paper successfully translates RBTRPM into practice, exploring key considerations. A measurement duration of at least six months is recommended, though a longer period would provide even better results. It is also important to emphasize the preference for using only structural influences in the response predictions. The study introduces the Peak over Threshold method for threshold determination applied on RBTRPM and employs moving metrics for concise result presentation, providing valuable insights for the implementation of RBTRPM in real-world bridge monitoring scenarios.

Old-growth beech forests in Germany as cool islands in a warming landscape

The climate crisis seriously threatens Central European forests and their ecosystem functions. There are indications that old-growth forests are relatively resilient and efficient in micro-climatic regulation during extreme climatic conditions. This study evaluates five well-protected old beech forests in Germany, part of a UNESCO World Heritage Site. We examined temperature dynamics and vitality in core, buffer, and border zones during hot days from 2017 to 2023, using Landsat 8 and 9 imageries to assess Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI), alongside on-site Air Temperature (AT) measurements. Our findings reveal that all five forests were impacted by recent extreme heat events, with core zones remaining cooler and more vital, followed by buffer zones. Temperature-regulating patterns varied with landscape characteristics and the surrounding matrixes. We observed a site-dependent cooling effect of the forest interior that increased with higher LST. Our study highlights the value of old-growth forests and recommends increasing effective protection around mature forests, establishing corridors between isolated patches, and creating mosaics in managed landscapes that include unmanaged areas capable of developing into old-growth ecosystems.

Riemannian manifold-aided data-driven diagnosis of photovoltaic hot spots

Abstract This paper proposes a novel data-driven approach for hot-spot fault detection in photovoltaic (PV) modules, utilizing a curved Riemannian manifold (RM) to characterize the sample space. The proposed method detects hot spots in PV systems by mining geometric features in high-dimensional data. The RM-based method combines the geometric properties of RMs for fault detection. In addition, it also provides information about the severity of the hot spots. The proposed method has three main advantages: (1) it is capable of actively learning fault characteristics and can be applied to diagnose various degrees of hot spots; (2) unlike other data-driven methods, the proposed method considers non-Euclidean spatial data features, which further improves the accuracy of the method and reduces the false alarm rate; (3) it does not rely on mathematical models or expert knowledge, making it able to meet the real-time monitoring needs of actual PV systems. The effectiveness and superiority of the proposed approach are verified through 12 sets of hot spots tests conducted on a PV experimental platform.

Intensity and volume of physical exercise influence DOMS and skin temperature differently in healthy adults

It remains unclear whether exercises leading to different outcomes of delayed onset muscle soreness (DOMS) may also elicit different skin temperature responses. The aim of this study was to determine whether different intensities and volumes of a single-joint exercise influence the DOMS and skin temperature measurements differently in healthy adults. Thirty-nine men and women were randomly assigned to three groups performing different exercise of different intensities and volumes (Exhaustion, Fatigue, Submaximal) to induce DOMS in the biceps brachii. DOMS (numeric pain rate scale, NPRS), pressure pain threshold (PPT) and skin temperature (infrared thermography, IRT) were measured on exercise day and 48 h later. The different exercises resulted in lower PPT responses 48 h after exercise and different DOMS reported across the different groups. Skin temperature outcomes did not differ following the different protocols. We found an increased minimum skin temperature 48 h after exercise in groups performing more intense exercises, but such differences were found in both exercised and non-exercised arms. Differently of PPT outcomes, pain reported depended on exercise intensity, and skin temperature 48 h after exercise could not show acute exercise adaptations. Skin temperature responses are contingent upon the characteristics of the participants rather than exercise intensity.

Multipoint Thermal Sensing System for Power Semiconductor Devices Utilizing Fiber Bragg Gratings

This study investigates the feasibility of using fiber Bragg grating (FBG) sensors for multipoint thermal monitoring of several power semiconductor devices (PSDs), such as insulated gate bipolar transistors (IGBTs), and rectifiers assembled on a common heatsink in a three-phase inverter. A novel approach is proposed to integrate FBG sensors beneath the baseplates of the IGBT modules, avoiding the need for invasive modifications to the device structure. By strategically positioning multiple FBG sensors, accurate temperature profiles of critical components can be obtained. The experimental results demonstrate the effectiveness of the proposed method, with the temperature measurements from FBG sensors closely matching those obtained using thermal infrared (IR) cameras within ±1.1 °C. This research highlights the potential of FBG sensors for reliable and precise thermal management in power electronic systems, contributing to improved performance and reliability.

Microstructural characterisation studies on hot corrosion behaviour of YSZ/Gd2Zr2O7 based plasma spray coatings on SS316

Abstract The present study uses microstructural analysis to examine the impact of integrating the rare earth oxide Gadolinium Zirconate (Gd2Zr2O7) into the primary YSZ powder during plasma spray coating on an SS316 substrate. The ceramic coatings are formulated with two distinct concentrations: 5 wt. % Gd2Zr2O7, designated as 5GDZ, and 15 wt. % Gd2Zr2O7, designated as 15GDZ. The coating thickness was consistently maintained at a bond coating of 50 μm and a top coating of 200 μm across all coated samples by controlling relevant process parameters, including current, powder feed rate, and standoff distance. Hot corrosion tests were conducted on the samples using 50 mg/cm2 of molten salt comprising 60 wt. % V2O5 and Na2SO4 at a temperature of 700 °C for 12 h. The results indicated that YSZ and 15GDZ effectively prevented corrosion in the hot molten salt environment. The corrosive products containing YVO4 and m-ZrO2 in the YSZ and 15GDZ coating act as a passivation layer to inhibit corrosion to a certain extent. Compared to YSZ, 5GDZ shows a weight gain of 162.5 mg/cm2, 58.54% higher. However, there is no noticeable improvement in hot corrosion resistance. The 5GDZ coating exhibited the formation of thin, corrosive products. More spallation, cracks, and fractures are evident in the 5GDZ coating. The weight gain of 15GDZ is quantified at 115.32 mg/cm2, representing a 40.9% reduction compared to the 5GDZ coating. Hence, further increases in the weight of Gd2Zr2O7 were added with YSZ beyond 5 wt. % demonstrate an enhancement in hot corrosion resistance. The penetration of molten salt into the bond coating interface and substrate is completely inhibited in all three coatings, as evidenced by the SEM and EDAX analysis.

Malt Barley Yield and Quality Response to Crop Water Stress Index

Malt barley is a crucial irrigated crop in the semi-arid Western United States, where the states of Idaho, Colorado, Wyoming, and Utah account for 92% of the irrigated production acreage and 30% of total U.S. production. In this region, spring malt barley’s seasonal evapotranspiration ranges from 400 to 650 mm, and competition for limited water supplies, coupled with drought, is straining regional water resources. This study aimed to investigate the use of canopy temperature for deficit irrigation scheduling of malt barley. Specifically, the objectives were to use data-driven models to estimate well-watered (TLL) and non-transpiring (TUL) canopy temperatures, correlate the crop water stress index (CWSI) with malt barley yield and quality measures, and assess the applicability of CWSI for malt barley irrigation scheduling in a semi-arid climate. A 3-year field study was conducted with five irrigation treatments relative to estimated crop evapotranspiration (full, 75%, 50%, 25%, and no irrigation) and four replicates each. Continuous canopy temperature measurements and meteorological data were collected, and a feedforward neural network model was used to predict TLL, while a physical model was used to estimate TUL. The neural network model accurately predicted TLL, with a strong correlation (R2 = 0.99), a root mean square error of 0.89 °C, and a mean absolute error of 0.70 °C. Significant differences in calculated season-average CWSI were observed between the irrigation treatments, and relative evapotranspiration, malt barley relative yield, test weight, and plump kernels were negatively correlated with the season-average CWSI, while seed protein was positively correlated. The relationship between daily CWSI and fraction of available soil water was well described by an exponential decay function (R2 = 0.72). These results demonstrate the applicability of data-driven models for computing CWSI of irrigated spring malt barley in a semi-arid environment and their ability to assess plant water stress and predict crop yield and quality response from CWSI.