Temperature Monitoring Research Articles

Preventing Diabetic Foot Re-Ulceration Through an Innovative Pressure and Temperature Monitoring Clinical Device.

This study compared the outcome of an innovative in-shoe pressure and temperature measuring device as an adjunct to standard clinical care for diabetic foot versus standard clinical care alone. It included 88 participants with Type 2 diabetes mellitus with a history of one or more plantar foot ulceration who were already using prescription orthoses. These were randomly divided into the control group (n = 44, standard care only) and the experimental group (n = 44, standard care plus the innovative device). Both groups were monitored for re-ulceration for one year. Overall, the control group exhibited a higher number of re-ulcerations (n = 14) with 2 amputations in comparison with the experimental group (only 2 ulcerations and no amputations) at the end of the study. In conclusion, this innovative in-shoe pressure and temperature measuring device appears to reduce re-ulcerations by offering objective data for clinical decision making in the management of the diabetic high-risk foot.

Conductive dual-network hydrogel-based multifunctional triboelectric nanogenerator for temperature and pressure distribution sensing

Conductive hydrogels are gaining attention in flexible electronics due to their high electrical conductivity and stretchable mechanical properties. Utilizing conductive hydrogels as electrodes in triboelectric nanogenerators (TENG) offers a promising avenue for developing versatile, flexible devices. However, the preparation of a multifunctional hydrogel-based TENG remains a challenging task. Here, a dual-network hydrogel TENG is denoted as DNH-TENG. It comprises poly(3,4-ethylenedioxythiophene): poly (styrene sulfonate) (PEDOT: PSS)/poly (vinyl alcohol) (PVA)/carboxylated multi-walled carbon nanotube (MWCNT-COOH) and offers the advantages of straightforward fabrication, versatile applications, and efficient output performance. Preparing a double network by combining a conductive network with a soft network improves the tensile properties but reduces its conductivity significantly. Further doping MWCNT-COOH with the dual-network leads to a conjugation effect and hydrogen bonding. This enhancement fortifies the hydrogel structure and augments the electrode's conductivity and mechanical properties. In single electrode mode, the DNH-TENG exhibits a short-circuit current of 16.2 μA, a transfer charge of 97.3 nC, and an open-circuit voltage of 270.5 V. Furthermore, DNH-TENG exhibits an impressive stretchability, allowing it to be extended to 566%. Using DNH-TENG's exceptional stretchability and ultrahigh sensitivity to mechanical and temperature stimuli, we designed DNH-TENG-based sensor arrays to showcase temperature and pressure distribution monitoring applications.

Sensitive, accurate, and high spatiotemporal resolution photonic thermometry

Real-time temperature monitoring with high accuracy and spatiotemporal resolution is critical for many biological applications, including disease diagnosis, drug delivery, and biomedical research. However, traditional methods for measuring temperature in biological systems present difficulties for a variety of reasons, such as slow response time, limited spatial resolution, low amplitude, and susceptibility to electromagnetic interference. Most importantly, in many cases, the thermal mass of temperature probes limits the accuracy and speed of measurement significantly. Here, we show that photonic microring resonators (MRRs) can be used for sensitive, precise, and high spatiotemporal resolution measurement of temperature in the biological milieu. The high refractive index of Si MRR and negligible thermal mass enable sensitive, ultrafast, and accurate temperature transients. By using a double resonator circuit, we demonstrate that MRR sensors can measure temperature with a 1 mm spatial resolution. We then show that MRR yields more accurate results than fiber optic probes for measuring temperature transients. Finally, we demonstrate the localized temperature measurement capability of MRRs in mouse brain tissue heated by superparamagnetic nanoparticles in an alternating magnetic field. This compact, lab-on-chip photonic temperature sensing platform holds great promise for continuous monitoring of temperature in critical biological and biomedical applications.

Estimation of battery temperature during drive cycle operation by the time evolution of voltage and current

During the operation of electric vehicles, accurate temperature monitoring of lithium-ion batteries by battery management system is crucial to their safety. However, temperature sensors possess limitations as they necessitate regular calibration to avoid inaccuracies, which can be challenging to accomplish in certain scenarios. As an alternative, a general sensorless temperature estimation framework is developed in this study to either serve as a complementary approach alongside sensors or function independently to ensure reliable temperature measurement. The proposed framework relies solely on data regarding battery voltage and current measurements, and this information is employed to determine the parameters of the equivalent circuit model via the vector-type recursive least squares algorithm in the first step. In the second step, these parameters and the voltage/current data serve as the input to the deep neural network (DNN) to estimate the instantaneous battery surface temperature during dynamic driving cycles under various ambient temperatures and degradation scenarios. To reflect the effect of past battery operation on the current temperature, a newly defined current integral function is proposed, by which the input time length of the DNN model is suggested. Four combinations of the model input are studied, and the best input option yields an average estimation error of the battery temperature that falls below 0.5 °C.

Elevated water transport of polyamide nanofiltration membranes via aqueous organophosphorus mediated interfacial polymerization

As interfacial polymerization (IP) is a reaction-diffusion process, rational regulation of diamine diffusion rate is effective to heighten the nanofiltration (NF) performance of polyamide (PA) membranes. Herein, we propose an aqueous organophosphorus-regulated IP strategy that promotes the formation of thinner and moderately crosslinked films toward improved water transport. Two similar compounds tetrakis (hydroxymethyl) phosphonium chloride (THPC) and tetrakis (hydroxymethyl) phosphonium sulfate (THPS) were utilized as functional additives to physically interact with piperazine via hydrogen bonding and thus retard piperazine diffusion rate during an IP process. The resultant polyamide membranes have been analyzed in terms of surface morphologies, chemical structures, hydrophilicity, and separation performance in detail. Surface temperature monitoring results revealed that heat release from regulated IP was less than that from unmodified IP, which was ascribed to retard monomer diffusion toward organic boundary in the presence of THPC or THPS. Molecular dynamics simulation further confirmed that the addition of THPC and THPS could efficiently decline the monomer diffusion rate via hydrogen bonds and increased solution viscosity. The modified PA membranes exhibited a reduced film thickness, moderate crosslinking degree, as well as increased average pore size, as revealed by a series of characterizations. Furthermore, the incorporation of THPC or THPS resulted the PA membranes with higher surface hydrophilicity and stronger negative charges. These improved physicochemical features synergically conferred a two-fold water permeance while maintaining a high divalent salt rejection (THPC-0.01: 18.3 L m-2 h−1 bar−1, 99.0 %, THPS-0.015: 20.2 L m−2 h−1 bar−1, 98 %). This aqueous organophosphorus regulated IP offers a simple and feasible approach to the preparation of low-transport-resistance PA membranes for enhanced NF performance.

Adaptive fault detection for lithium-ion battery combining physical model-based observer and BiLSTMNN learning approach

Lithium-ion batteries possess one of the best energy-weight ratios, while safety and reliability are critical issues for its continued operation. Due to extreme fast-charging, huge application sizes and variable usage environment, the risk of soft battery faults, including soft internal short circuit (SISC), is increased and may evolve into severe accidents. Therefore, accurate early detection of lithium-ion battery fault is imperative to guarantee the battery performance. Motivated by this fact, we proposed a real time fault detection framework for battery soft faults. Based on the Equivalent Circuit Model (ECM) and coupling thermal model, Extended Kalman Filter (EKF) observer is used for reliable monitoring of battery voltage and surface temperature. Inspired by uncertainty interference during the observation process, we explore an observed-based learning approach that combine physical model-based observer and Bidirectional Long Short-Term Memory neural networks (BiLSTMNN) to empower the observer with the ability to learn the uncertainties, which is efficient in distinguishing soft faults information and uncertainties from residual. Furthermore, the memory characteristics of BiLSTMNN are considered to improve the robustness of the detection system, including comprehensive training data and optimized input features. Finally, through the comparative analysis and the robustness evaluation experiments including various driving cycles and internal short circuits (ISC) faults test verity the good performance of the proposed method.

Exploring the Potential of Promising Sensor Technologies for Concrete Structural Health Monitoring.

Structural health monitoring (SHM) is crucial for maintaining concrete infrastructure. The data collected by these sensors are processed and analyzed using various analysis tools under different loadings and exposure to external conditions. Sensor-based investigation on concrete has been carried out for technologies used for designing structural health monitoring sensors. A Sensor-Infused Structural Analysis such as interfacial bond-slip model, corroded steel bar, fiber-optic sensors, carbon black and polypropylene fiber, concrete cracks, concrete carbonation, strain transfer model, and vibrational-based monitor. The compressive strength (CS) and split tensile strength (STS) values of the analyzed material fall within a range from 26 to 36 MPa and from 2 to 3 MPa, respectively. The material being studied has a range of flexural strength (FS) and density values that fall between 4.5 and 7 MPa and between 2250 and 2550 kg/m3. The average squared difference between the predicted and actual compressive strength values was found to be 4.405. With cement ratios of 0.3, 0.4, and 0.5, the shear strength value ranged from 4.4 to 5.6 MPa. The maximum shear strength was observed for a water-cement ratio of 0.4, with 5.5 MPa, followed by a water-cement ratio of 0.3, with 5 MPa. Optimizing the water-cement ratio achieves robust concrete (at 0.50), while a lower ratio may hinder strength (at 0.30). PZT sensors and stress-wave measurements aid in the precise structural monitoring, enhanced by steel fibers and carbon black, for improved sensitivity and mechanical properties. These findings incorporate a wide range of applications, including crack detection; strain and deformation analysis; and monitoring of temperature, moisture, and corrosion. This review pioneers sensor technology for concrete monitoring (Goal 9), urban safety (Goal 11), climate resilience (Goal 13), coastal preservation (Goal 14), and habitat protection (Goal 15) of the United Nations' Sustainable Development Goals.

Deep learning-assisted common temperature measurement based on visible light imaging

Abstract Real-time, contact-free temperature monitoring of low to medium range (30°C ~ 150 °C) has been extensively used in industry and agriculture, which is usually realized by costly infrared temperature detection methods. This paper proposes an alternative approach of extracting temperature information in real time from the visible light images of the monitoring target using a convolutional neural network (CNN). A mean square error of ＜ 1.119 °C was reached in the temperature measurements of low to medium range using the CNN and the visible light images. Imaging angle and imaging distance do not affect the temperature detection using visible optical images by the CNN. Moreover, the CNN has a certain illuminance generalization ability capable of detection temperature information from the images which were collected under different illuminance and were not used for training. Compared to the conventional machine learning algorithms mentioned in the recent literatures, this real-time, contact-free temperature measurement approach that does not require any further image processing operations facilitates temperature monitoring applications in the industrial and civil fields.

Possible Prophylactic Effects of Sulforaphane on LPS-Induced Recognition Memory Impairment Mediated by Regulating Oxidative Stress and Neuroinflammatory Proteins in the Prefrontal Cortex Region of the Brain.

Alzheimer's disease (AD) presents a significant global health concern, characterized by neurodegeneration and cognitive decline. Neuroinflammation is a crucial factor in AD development and progression, yet effective pharmacotherapy remains elusive. Sulforaphane (SFN), derived from cruciferous vegetables and mainly from broccoli, has shown a promising effect via in vitro and in vivo studies as a potential treatment for AD. This study aims to investigate the possible prophylactic mechanisms of SFN against prefrontal cortex (PFC)-related recognition memory impairment induced by lipopolysaccharide (LPS) administration. Thirty-six Swiss (SWR/J) mice weighing 18-25 g were divided into three groups (n = 12 per group): a control group (vehicle), an LPS group (0.75 mg/kg of LPS), and an LPS + SFN group (25 mg/kg of SFN). The total duration of the study was 3 weeks, during which mice underwent treatments for the initial 2 weeks, with daily monitoring of body weight and temperature. Behavioral assessments via novel object recognition (NOR) and temporal order recognition (TOR) tasks were conducted in the final week of the study. Inflammatory markers (IL-6 and TNF), antioxidant enzymes (SOD, GSH, and CAT), and pro-oxidant (MDA) level, in addition to acetylcholine esterase (AChE) activity and active (caspase-3) and phosphorylated (AMPK) levels, were evaluated. Further, PFC neuronal degeneration, Aβ content, and microglial activation were also examined using H&E, Congo red staining, and Iba1 immunohistochemistry, respectively. SFN pretreatment significantly improved recognition memory performance during the NOR and TOR tests. Moreover, SFN was protected from neuroinflammation and oxidative stress as well as neurodegeneration, Aβ accumulation, and microglial hyperactivity. The obtained results suggested that SFN has a potential protective property to mitigate the behavioral and biochemical impairments induced by chronic LPS administration and suggested to be via an AMPK/caspase-3-dependent manner.

Fiber Bragg grating as a temperature sensor for human body temperature monitoring

Fiber Bragg grating as a temperature sensor for human body temperature monitoring

A Multiplexing Optical Temperature Sensing System for Induction Motors Using Few-Mode Fiber Spatial Mode Diversity

Induction motors are widely applied in motor drive systems. Effective temperature monitoring is one of the keys to ensuring the reliability and optimal performance of the motors. Therefore, this paper introduces a multiplexed optical temperature sensing system for induction motors based on few-mode fiber (FMF) spatial mode diversity. By using the spatial mode dimension of FMF, fiber Bragg grating (FBG) carried by different spatial modes of optical paths is embedded in different positions of the motor to realize multipoint synchronous multiplexing temperature monitoring. The paper establishes and demonstrates a photonic lantern-based mode division sensing system for motor temperature monitoring. As a proof of concept, the system demonstrates experiments in multiplexed temperature sensing for motor stators using the fundamental mode LP01 and high-order spatial modes LP11, LP21, and LP02. The FBG sensitivity carried by the above mode is 0.0107 nm/°C, 0.0106 nm/°C, 0.0097 nm/°C, and 0.0116 nm/°C, respectively. The dynamic temperature changes in the stator at different positions of the motor under speeds of 1k rpm, 1.5k rpm, 2k rpm with no load, 3 kg load, and 5 kg load, as well as at three specific speed–load combinations of 1.5k rpm_3 kg, 1k rpm_0kg, 2k rpm_5 kg and so on are measured, and the measured results of different spatial modes are compared and analyzed. The findings indicate that different spatial modes can accurately reflect temperature variations at various positions in motor stator winding.

Balancing preservation and utilization: Window retrofit strategy for energy efficiency in historic modern building

In modern historic buildings, the decline in energy efficiency due to aging necessitates both the preservation of historical value and the need for energy retrofits. However, the windows cannot be altered due to the historical significance of the building's windows in terms of design and materials. Retrofitting windows in modern historic buildings require a more multifaceted improvement than other envelopes. This research examines the energy efficiency of a retrofit strategy that adds double-glazed windows to existing exterior windows. Through the application of retrofit technology, the performance of the windows and their impact on indoor temperature and relative humidity were analyzed. The research conducted an energy performance analysis using building energy simulations to assess energy savings. To ensure accurate energy efficiency evaluation, on-site indoor temperature and relative humidity monitoring, as well as non-destructive thermal performance assessments, were performed. The results revealed a 35 % improvement in airtightness with window retrofit. Based on these results, simulation results showed a 17 % reduction in annual cooling energy consumption and a 24 % reduction in annual heating energy consumption following the implementation of retrofit strategies. Thermal performance improvements proved effective in reducing cooling energy consumption, while airtightness improvements were more effective for heating energy consumption reduction. Consequently, for buildings requiring both heating and cooling, the double-glazed windows retrofit strategy emerged as an energy effective retrofit approach capable of enhancing both airtightness and thermal performance. This retrofit will unlock new possibilities for the preservation and revitalization of modern historic buildings, allowing them to continue their legacy.

Polarity control of siloxane composite films for triboelectric nanogenerator based self-powered body temperature monitoring

Body temperature monitoring systems serve as key indicators for identifying potential patients with various illnesses such as influenza and COVID-19. Conventionally, these monitoring devices rely on batteries, limiting their long-term and widespread application. Self-powered thermometers are considered a sustainable solution that enable continuous monitoring. Among various self-charging power sources, triboelectric nanogenerators (TENGs) are a novel development, but suffer several critical drawbacks for medical applications. In this study, we present a high-performance TENG for self-powered body temperature monitoring systems by controlling the polarity of glass-fabric reinforced vinyl-thiol functionalized hybrid materials (GFR-VTHs) by varying the phenyl group contents. The effect of polarity modification on the performance of the developed TENG was confirmed by the high electrical output and stable operation under 10,000 cycles of repetitive pressing tests. Finally, self-powered body temperature monitoring systems were constructed by measuring human body temperature with a clinical thermometer powered by the GFR-VTH-based TENG, demonstrating device feasibility as a power source for self-powered healthcare systems.

Design Implementation of a Logger-Based Digital Thermometer

LM35 temperature sensor is a widely used 3-terminal sensor known for its precise temperature measurement capabilities without the need for external calibration circuitry. With a sensitivity of 10mV/°C, it operates within a temperature range of -55°C to 150°C, with an error margin of 0.5°C. This paper presents the development and testing of a logger-based digital thermometer using LM35 sensor, integrated with a dsPIC30F4013 digital signal processing controller, an LCD, a real-time clock (DS1307), and other essential components. The methodology involves the analysis of the sensor's sensitivity and its response under both stress and non-stress temperature conditions. The findings indicate that the sensor's output closely follows the theoretical sensitivity, with a negligible offset, ensuring accurate temperature measurements. Comparative analysis with a standard laboratory thermometer suggests that the digital sensor provides superior precision, with an error margin of ±0.01°C. The results confirm LM35's suitability for accurate ambient temperature monitoring and validation of environmental temperature fluctuations.

Optical Fiber Temperature Sensing Probe Based on F–P Cavity for Human Body Temperature Monitoring

Optical Fiber Temperature Sensing Probe Based on F–P Cavity for Human Body Temperature Monitoring

Implantable Wireless Sensors for Three-Node Temperature Monitoring of Artificial Knee Joints

Implantable Wireless Sensors for Three-Node Temperature Monitoring of Artificial Knee Joints

SMART TEMPERATURE SENSORS FOR TEMPERATURE CONTROL SYSTEMS

Temperature control systems are pivotal in various applications, ranging from industrial processes and environmental monitoring to everyday comfort and safety. Smart temperature sensors represent a significant advancement in this field, enabling precise, efficient, and intelligent temperature regulation. This article provides an overview of the latest developments in smart temperature sensors and their integration into temperature control systems. The paper begins by discussing the fundamental importance of temperature control systems in maintaining optimal conditions in diverse settings. It highlights the growing need for sensors that offer improved accuracy, reliability, and adaptability, all of which smart temperature sensors are designed to provide. Subsequently, the article delves into the key features and capabilities of smart temperature sensors. These sensors incorporate advanced technologies such as microelectromechanical systems (MEMS), integrated digital signal processing, and wireless connectivity, enabling them to collect and transmit data with unprecedented precision and efficiency. Moreover, smart temperature sensors often offer real-time data monitoring, remote access, and the capacity for data analytics, making them invaluable tools for enhancing temperature control. The discussion then shifts to the various applications of smart temperature sensors. Whether it be optimizing energy consumption in HVAC systems, ensuring compliance with safety standards in industrial processes, or monitoring temperature-sensitive goods during transport and storage, these sensors have the potential to revolutionize temperature control across numerous domains. Additionally, the article addresses the challenges and considerations in implementing smart temperature sensors, including calibration, data security, and power efficiency. The integration of artificial intelligence and machine learning algorithms for predictive temperature control is explored, as they promise to further enhance the adaptability and responsiveness of temperature control systems. In conclusion, smart temperature sensors are at the forefront of temperature control system innovation. Their ability to provide real-time, highly accurate data, alongside intelligent features, positions them as key components in achieving optimal temperature conditions in a wide range of applications. As technology continues to evolve, the adoption of smart temperature sensors is expected to grow, ushering in a new era of temperature control efficiency and precision. Keywords: Smart temperature sensors, Precision temperature monitoring, Digital signal processing, Safety, Industrial processes.

A broadband hybrid blue energy nanogenerator for smart ocean IoT network

Blue energy represents a crucial renewable energy resource; however, its development has been limited by the low and variable frequency characteristics of sea waves. In this study, we present a novel approach, wherein a spring pendulum coupled hybrid energy harvester (SPC-HEH) is employed to address these limitations. The SPC-HEH integrates two electromagnetic generators (EMGs), two piezoelectric nanogenerators (PENGs), and two multilayer-structured triboelectric nanogenerators (TENGs) into a vessel, enabling efficient wave energy harvesting. Notably, the integration of the lightweight PENG and TENG with the heavy EMG not only enhances the water wave energy harvesting capability of SPC-HEH but also optimizes its spatial utilization efficiency. Additionally, owing to the spring pendulum, the SPC-HEH is capable of simultaneously harnessing the wave kinetic energy of horizontal and vertical directions and realize the decoupling of vertical energy capture from horizontal energy capture. Remarkably, owing to its precise geometric design and efficient space utilization, the SPC-HEH achieves an impressive peak power density of 82.4 Wm−3. Finally, we demonstrate the practical applicability of the SPC-HEH by integrating it with a circuit as a self-powered ocean buoy for real-time monitoring of ocean temperature, wave frequency, ocean turbidity and other information. The remarkable potential of this system for integration into self-powered smart ocean IoT networks holds significant promise for advancing marine research and environmental monitoring capabilities.

In-fiber chirped Fabry-Perot cavity for temperature sensing.

Measurement resolution and dynamic range of conventional optical fiber sensors are often mutually restricted. In this work, an in-fiber chirped Fabry-Perot cavity (interferometer) is proposed, for the first time to our knowledge, to resolve the conflict between the resolution and dynamic range. The chirped Fabry-Perot interferometer is constructed by two chirped fiber Bragg gratings inscribed in the opposite directions, resulting in a gradually varied (i.e., chirp) cavity length for different reflection wavelengths. As such, the interference spectrum exhibits high figure of merit (FOM) and large free spectrum range (FSR) at long and short wavelength regions, respectively, enabling high-resolution and large-dynamic-range measurement simultaneously. Temperature tests are then carried out to confirm the validity of the solution. The proposed sensing schema may be developed further and find vital applications in biomedicine fields such as endosomatic temperature monitoring of living bodies. The proposed concept of chirped Fabry-Perot interferometer can provide breakout ideas for other sensing scenarios where high-resolution and large-dynamic range are demanded and can be further generalized to other measurands or even free-space interference metrologies.

A critical review of distributed fiber optic sensing applied to geologic carbon dioxide storage

AbstractIn the context of global climate change, carbon capture and storage (CCS) has become a direct and effective measure for reducing greenhouse gases emission. However, injecting CO2 into the subsurface reservoirs may pose risks related to geological hazards. Therefore, monitoring the variations in underground temperature fields, strain fields, and vibration fields induced by CO2 injection is essential for predicting and controlling geological hazards. Distributed fiber optic sensing (DFOS) technology, with its unique features, enables real‐time monitoring of temperature, strain, and vibration. By deploying fiber optic (FO) cables inside wellbores, a DFOS can be used to effectively capture multiple underground response parameters. This paper reviews the applications of DFOS technology in CO2 geological sequestration. The chapter covers aspects such as the literature review, principles and applications of fiber optics, and representative monitoring projects. Finally, the paper discusses the challenges and proposed solutions for DFOS technology in this context. © 2024 Society of Chemical Industry and John Wiley & Sons, Ltd.