Temperature Monitoring Research Articles

Climate-induced risk assessment of the quarantine room in a University Library

Microclimate conditions in conservation spaces such as museums, galleries, storages, archives, and libraries significantly impact the preservation of cultural materials, potentially leading to permanent damage. This study focuses on quarantine rooms, essential for isolating and inspecting incoming collections for infestations or contamination. Despite their importance, systematic microclimate investigations in such spaces have not been conducted until now. In this research, temperature and relative humidity monitoring has been conducted over the past 3 years (i.e., 2021-2022-2023) within the quarantine room of the Norwegian University of Science and Technology (NTNU) University Library (located within the DORA I concrete bunker, Trondheim, Norway). Data analysis revealed stable indoor conditions due to the buffering capacity of the massive building envelope together with climate control system. Specific metrics for the estimation of climate-induced chemical and biological risks on vulnerable artifacts were applied to compute the percentage of time for which thermohygrometric conditions could favour cellulose hydrolysis and biodeteriogens proliferation on a yearly basis. In this way, the study provided a decision-making tool useful to evaluate the best time (in terms of safest temperature and relative humidity conditions) when to introduce incoming collections into the quarantine room, depending on the material they are made of and on the timeframe selected for their isolation.

Volumetric lesion analysis and validation of various bipolar configurations in radiofrequency ablation of ventricular myocardium in a bovine model.

The bipolar radiofrequency ablation(B-RFA) strategy was increasingly used to target deep intramural re-entrant foci responsible for the arrhythmia not ablated by conventional unipolar RFA / sequential unipolar RFA. Lesional characteristics of various bipolar configurations were largely unknown. To investigate the lesional geometry in relation to various factors to determine the most effective ablation strategy that minimises steam pops and achieves transmurality. To assess the temperatures at the return electrode. A custom-made validated ex-vivo bipolar ablation model was used to assess lesion formation. The myocardial sample was placed between two ablation catheters in four different orientations. Lesions were created using different power (30W, 40W, 50W) and time settings(30, 40 and 50s) with different catheter orientations. Data was analysed using binary logistic regression and multiple linear regression. Among 107 lesions, The volume of the active catheter lesion (266 +/- 137mm^3) significantly differed from their return electrode counterparts (130 +/- 91.8mm^3) (p < 0.001), and the temperatures at the return electrode end were lower than at the active electrode (p = 0.004). Higher power and longer duration application led to more frequent steam pops (p < 0.001), while true parallel configuration resulted in fewer steam pops (p < 0.001). A custom model without ground electrode temperature monitoring is safe and cost-effective. The safest strategy is a true parallel configuration with an inter-electrode distance of at least 15mm and a power of 30W to 40W, which generates lower steam pops and better transmurality.

Smart photonic crystal hydrogels for visual glucose monitoring in diabetic wound healing

Diabetes is a global chronic disease that seriously endangers human health and characterized by abnormally high blood glucose levels in the body. Diabetic wounds are common complications which associate with impaired healing process. Biomarkers monitoring of diabetic wounds is of great importance in the diabetes management. However, actual monitoring of biomarkers still largely relies on the complex process and additional sophisticated analytical instruments. In this work, we prepared hydrogels composed of different modules, which were designed to monitor different physiological indicators in diabetic wounds, including glucose levels, pH, and temperature. Glucose monitoring was achieved based on the combination of photonic crystal (PC) structure and glucose-responsive hydrogels. The obtained photonic crystal hydrogels (PCHs) allowed visual monitoring of glucose levels in physiological ranges by readout of intuitive structural color changes of PCHs during glucose-induced swelling and shrinkage. Interestingly, the glucose response of double network PCHs was completed in 15 min, which was twice as fast as single network PCHs, due to the higher volume fraction of glucose-responsive motifs. Moreover, pH sensing was achieved by incorporation of acid-base indicator dyes into hydrogels; and temperature monitoring was obtained by integration of thermochromic powders in hydrogels. These hydrogel modules effectively monitored the physiological levels and dynamic changes of three physiological biomarkers, both in vitro and in vivo during diabetic wound healing process. The multifunctional hydrogels with visual monitoring of biomarkers have great potential in wound-related monitoring and treatment.Graphical abstract

Cold Storage Temperature Monitoring with Trilinear Interpolation

The Internet of Things (IoT) is widely applied in agriculture and in-dustry. This article proposes a 3-dimensional temperature monitoring model for cold storage, enhancing the ability to observe and detect abnormal temperature changes within the storage. Using real-time temperature acquisition and applying trilinear interpolation techniques, this model determines temperatures at all loca-tions in the storage. It accepts temperature values and sensor locations, appropri-ately segmenting the cold storage space to apply trilinear interpolation and deter-mine the temperature value at every location in the storage. The model was tested based on different number of sensors and different sensor locations. Based on that, we provide an optimal solution for the number of sensors needed for cold storage.

Design optimization of a phase-change capacitive sensor for irreversible temperature threshold monitoring and its eco-friendly and wireless implementation

Monitoring the temperature of perishable goods during transport and storage is essential to prevent waste and maintain product quality. Exploiting the unique property of phase-change materials (PCM), altering their physical state at specific temperatures, we optimize a capacitive sensor design based on a copper on polyimide interdigitated spiral (IDE) structure coated with a PCM to irreversibly detect temperature thresholds. The effect of the sensor dimensioning on its response is analyzed using a finite element model simulation. The model predicted up to 51% capacitance variation for optimal coverage of the PCM after spreading over the IDE, which was validated experimentally within a 5% error. Two melting concepts utilizing the spreading or the removal of the melted PCM over the IDE are investigated based on a capillary retention mechanism to maintain sensor sensitivity under inclination. Finally, an eco-friendly implementation of the capacitive structure and its wireless operation at 460 MHz is demonstrated on paper with a printed zinc transducer passivated with beeswax and covered with jojoba oil. Melting of the oil at a threshold temperature of 12.3 °C resulted in an irreversible shift in resonance frequency of 14 MHz. This study provides guidelines for the design and implementation of irreversible temperature monitoring capacitive sensors.

Downhole flow control: A key for developing enhanced geothermal systems in horizontal wells

Field evidence indicates that only a few large fractures in enhanced geothermal systems (EGS) may dominate fluid flow and lead to the development of thermal short-circuiting. This problem would worsen in heat harvesting through horizontal wells with abundant fractures (induced or natural ones). To limit flow shortcuts that may cause early thermal breakthroughs, we propose a temperature-sensitive flow management system and explore its potential benefits during the development of EGS in horizontal wells. The proposed downhole flow management system consists of sensors for real-time temperature monitoring, and flow control devices for adjusting injection distribution in the wellbore. We evaluate the performance of such systems over 50 years of operation by numerical analysis. The results indicate that, when the proposed system is utilized, the produced fluid temperature will be increased by up to 60 K for a field-scale example. Besides, the application of the proposed flow management system can increase the cumulative heat extraction by up to 48.25 % (0.69 × 1016 J) after 50 years. The heat extraction efficiency can be improved by up to 94.16 %. During the EGS operation, it is suggested to implement multi-stage fluid control, but it is not recommended to reopen the fluid injection that has been previously shut down. Finally, the flow management system can mitigate the negative impacts of geological heterogeneities or the heterogeneity in fracture hydraulic conductivities. This paper proposes a new concept of flow management to integrate horizontal wells into efficient EGSs for power generation and heat extraction in the future.

Relevance-Based Reconstruction Using an Empirical Mode Decomposition Informer for Lithium-Ion Battery Surface-Temperature Prediction

Accurate monitoring of lithium-ion battery temperature is essential to ensure these batteries’ efficient and safe operation. This paper proposes a relevance-based reconstruction-oriented EMD-Informer machine learning model, which combines empirical mode decomposition (EMD) and the Informer framework to estimate the surface temperature of 18,650 lithium-ion batteries during charging and discharging processes under complex operating conditions. Initially, based on 9000 data points from the U.S. NASA Prognostics Center of Excellence’s random battery-usage dataset, where each data point includes three features: temperature, voltage, and current, EMD is used to decompose the temperature data into intrinsic mode functions (IMFs). Subsequently, the IMFs are reconstructed into low-, medium-, and high-correlation components based on their correlation with the original data. These components, along with voltage and current data, are fed into sub-models. Finally, the model captures the long-term dependencies among temperature, voltage, and current. The experimental results show that, in single-step prediction, the mean squared error, mean absolute error, and maximum absolute error of the model’s predictions are 0.00095, 0.02114, and 0.32164 °C; these metrics indicate the accurate prediction of the surface temperature of lithium-ion batteries. In multi-step predictions, when the prediction horizon is set to 12 steps, the model achieves a hit rate of 93.57% where the maximum absolute error is within 0.5 °C; under these conditions, the model combines high predictive accuracy with a broad predictive range, which is conducive to the effective prevention of thermal runaway in lithium-ion batteries.

Energy-efficient and location-aware IoT and WSN-based precision agricultural frameworks

Precision agriculture has emerged as a promising approach to enhance crop yield, reduce environmental impact, and optimize resource utilization through advanced sensing and automation technologies. This paper proposes an energy-efficient and location-aware framework for Internet of Things (IoT) and Wireless Sensor Networks (WSN)-based precision agriculture systems. The framework leverages low-power wireless communication protocols, adaptive sensor scheduling, and location-based clustering algorithms to minimize energy consumption and prolong the network lifetime. Key features include real-time monitoring of soil moisture, temperature, humidity, and crop health through geographically distributed sensors, with automated decision-making for irrigation, fertilization, and pest control. The proposed framework also integrates machine learning models for predictive analysis and anomaly detection, enabling early identification of potential issues that could adversely affect crop productivity. Simulation results demonstrate a significant reduction in energy consumption and communication overhead, while maintaining high accuracy in environmental parameter monitoring and resource allocation. This framework offers a scalable and robust solution for implementing sustainable precision agriculture practices, particularly in remote and resource-constrained areas

THE TRAINING OF TEMPERATURE MONITORING SYSTEM WITH LM35 SENSOR BASED ON RESBEERY PI

Abstract. This community service activity aims to train the community on developing and implementing a temperature monitoring system using the LM35 sensor integrated with Raspberry Pi. This training was carried out to improve people's understanding and skills, especially in utilizing microcontroller-based technology and IoT (Internet of Things) in daily life, especially in the field of room temperature or environmental monitoring. This activity involves targets, for example, teachers, students, and technicians, at SMA AL-IRSYAD TEGAL, with training methods that include basic theories about temperature sensors, introduction to Raspberry Pi, and hands-on practice in assembling and programming temperature monitoring systems. The results of this training showed that participants could understand the basic concepts of temperature monitoring systems and could apply the knowledge gained in making simple devices that can measure and display temperature in real time. The activity evaluation showed that most of the participants felt helped by this training and showed high interest in further developing the system that had been studied. In the future, it is hoped that similar activities can continue to be carried out to empower the community to utilize technology for daily needs. Keywords: Community service, temperature monitoring system, LM35, Raspberry Pi, technology training.

Effectiveness evaluation of cooling measures for express highway construction in permafrost regions based on GPR and ERT

Global warming and human activities are accelerating the degradation of permafrost on the Qinghai-Tibet Plateau (QTP), leading to significant settlement and cracking issues in the local express highway infrastructures. In response, the Gonghe-Yushu Express Highway (GYE) on the east edge of the QTP incorporated extensive cooling measures during its construction to enhance embankment stability. Despite these efforts, field investigations have disclosed that embankment diseases persist across various sections, including those with implemented cooling measures. This study focuses on a specific test and demonstration section of the GYE, employing a suite of cooling measures to assess their engineering effectiveness. Utilizing a combination of multi-time ground-penetrating radar (GPR) and electrical resistivity tomography (ERT) detection, alongside on-site disease investigations and temperature monitoring, this research comprehensively evaluates the efficacy of different cooling interventions. Findings indicate that although cooling measures generally curb permafrost degradation in areas with ice-rich and ice-saturated soils, they fall short in sections with massive ground ice. Of the six cooling measures examined in the demonstration section, ventilation duct embankments emerge as the most effective, whereas crushed-rock layer embankments rank as the least. The study further reveals that the combined use of XPS insulation boards and two-phase closed thermosyphons inadequately addresses the issue of central heat accumulation in broad-width express highways, reducing uneven settlement issues but aggravating longitudinal cracking. Comparative analysis of on-site surveys and monitoring data suggests that regular application of GPR and ERT techniques can proficiently assess the performance of cooling measures.

Design, fabrication, and calibration of a micromachined thermocouple for biological applications in temperature monitoring

This paper presents a microneedle thermocouple probe designed for temperature measurements in biological samples, addressing a critical need in the field of biology. Fabricated on a Silicon-On-Insulator (SOI) wafer, the probe features a doped silicon (Si)/chrome (Cr)/gold (Au) junction, providing a high Seebeck coefficient, rapid response times, and excellent temperature resolution. The microfabrication process produces a microneedle with a triangular sensing junction. Finite Element Analysis (FEA) was employed to evaluate the thermal time constant and structural integrity in tissue, supporting the probe's suitability for biological applications. Experimental validation included temperature measurements in ex-vivo tissue and live Xenopus laevis oocytes. Notably, intracellular thermogenesis was detected by increasing extracellular potassium concentration to depolarize the oocyte membrane, resulting in a measurable temperature rise. These findings highlight the probe's potential as a robust tool for monitoring temperature variations in biological systems.

PEDOT:PSS-Based Wearable Flexible Temperature Sensor and Integrated Sensing Matrix for Human Body Monitoring.

Flexible temperature sensors have been widely used in electronic skins and health monitoring. Body temperature as one of the key physiological signals is crucial for detecting human body's abnormalities, which necessitates high sensitivity, quick responsiveness, and stable monitoring. In this paper, we reported a resistive temperature sensor designed as an ultrathin laminated structure with a serpentine pattern and a bioinspired adhesive layer, which was fabricated with a composite of poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate)/single-wall carbon nanotubes/reduced graphene oxide (PEDOT:PSS/SWCNTs/rGO) and polydimethylsiloxane (PDMS). The temperature sensor exhibited a high temperature sensitivity of 0.63% °C-1, coupled with outstanding linearity of 0.98 within 25-45 °C. Furthermore, it showed fast response and recovery speeds of 4.8 and 5.8 s, respectively, between 25 and 36 °C. It also demonstrated exceptional stability when subjected to stress and bending disturbances with the maximum bending interference deviation of 0.03%. Additionally, it displayed good cyclic stability over a broad temperature range from 25 to 85 °C, and the standard deviation at 25 °C is 0.14%. A series of experiments including blowing detection, respiratory monitoring with or without a mask, and during rest or sleep were conducted to show the potential of the flexible temperature sensors in human body monitoring. Furthermore, a 4 × 4 flexible temperature sensor matrix was integrated to detect and map objects such as wrenches and blood vessels through human hand skin. The results were consistent with those of infrared measurements. The flexible temperature sensor is capable of real-time temperature monitoring and has the potential in tracking human respiration, assessing sleep quality, and mapping the temperature of various objects.

Hardware Testing Methodologies for Wide Bandgap High-Power Converters

Wide bandgap (WBG) power semiconductor devices are increasingly replacing silicon IGBTs in high-power and high-voltage power electronics applications. However, there is a significant gap in the literature regarding efficient testing methodologies for high-power and high-voltage converters under constrained laboratory resources. This paper addresses this gap by presenting comprehensive, hardware-focused testing methodologies for high-power and high-voltage WBG power semiconductor-based converter bring-up before the control validation phase steps in. The proposed methods enable thorough evaluation and validation of converter hardware, including device switching characteristics, driving circuit functionality, thermal management performance, insulation integrity, and sustained operation at full power. We utilized the double pulse test (DPT) to characterize switching performance in a two-level phase leg configuration, extract circuit parasitics, and validate magnetic components. The DPT was further applied to optimize gate driving circuits, validate overcurrent protection mechanisms, and measure device on-resistance. Additionally, a multicycle test was introduced to rapidly assess steady-state converter performance and estimate efficiency. Recognizing the critical role of thermal management in high-power converters, our methodologies extend to the experimental extraction of key thermal parameters—such as junction-to-ambient thermal resistance and thermal capacitance—via a heat loss injection method. A correlation method between temperature sensor measurements and junction temperature is presented to enhance the accuracy of device temperature monitoring during tests. To ensure reliability and safety, dielectric withstand tests and partial discharge measurements were conducted at both component and converter levels under conventional 60 Hz sinusoidal and high-frequency PWM waveforms. Finally, we highlight the importance of testing converters under full voltage, current, and thermal conditions through power circulating tests with minimal power consumption, applicable to both non-isolated and isolated high-power converters. Practical examples are provided to demonstrate the effectiveness and applicability of these hardware testing methodologies.

Core–Shell Colloidal Nanocomposites for Local Temperature Monitoring during Photothermal Heating

Core–Shell Colloidal Nanocomposites for Local Temperature Monitoring during Photothermal Heating

A wearable, self-sustainable, and wireless plantar pressure and temperature monitoring system for foot ulceration prognosis and rehabilitation

Continuous foot temperature and pressure monitoring are desirable for prevention and care of foot health conditions such as Diabetic foot ulceration (DFU). However, monitoring these parameters simultaneously is complex, and mostly rely on sensors with undisrupted power supply integration. Herein, we propose high-performance self-sustainable smart footwear capable of monitoring foot pressure and temperature at various locations for real-time applications. The footwear consists of three modules of an integrated electromagnetic generator (EMG) and triboelectric (TENG) self-powered pressure sensors operated by very elastic 3D printed thermoplastic polyurethane (TPU) springs formed by stacking circular plates that allow TENG pressure sensors to be embedded within. Additionally, Laser-induced graphene (LIG) temperature sensors are used for monitoring foot temperature. The embedded EMG can deliver high output power during various activities: walking (38 mW), running (154 mW), and jumping (75 mW), sufficient to power signal processing and transmission circuitries for wireless monitoring of foot temperature and pressure. TENG-based pressure sensors and LIG temperature sensors (LTS) embedded in this design allow mapping of foot pressure (0.2 V/kPa at 1 Hz) and foot temperature (0.55 Ω °C−1) continuously. Finally, footwear was assembled successfully to demonstrate wireless self-sustainable multisensory smart footwear for monitoring foot pressure and temperature to prevent and rehabilitate diabetic foot ulcers.

Infrared Imaging Analysis as Non-Destructive Maintenance of Food Processing Equipment

The use of infrared imaging is a recently developed non-destructive tool to assess the safety, quality and efficiency of the food industry. This article explores the application areas and benefits of using infrared imaging in the maintenance of machinery used in the food industry. Applications covered include food quality control, packaging inspection, temperature monitoring and control, hygiene and sanitation control, foreign object detection, energy saving through optimization, baking, as well as cooking process monitoring and research. By using infrared cameras to their full potential, it is possible to reveal defects, check the level of cleanliness, optimize the maintenance of machines, and examine the thermal attributes of food. This article also provides an in-depth analysis of the historical development of infrared imaging technology. We also explore the basic ideas behind infrared image analysis. This includes the Stefan-Boltzmann Law, Planck's Law, and Wien's displacement law. Infrared image analysis has many advantages and some disadvantages. Despite these drawbacks, infrared imaging analysis has continued benefits. It saves costs, ensures equipment longevity and increases productivity. This makes it very important for the safety of food industry machinery.

Evidence-based strategies for optimizing long-term temperature monitoring in offices

Long-term monitoring of the thermal environment in office buildings has become increasingly relevant with the rise of wireless sensor networks. However, there is a notable absence of explicit guidelines for implementing monitoring strategies in such contexts. This lack of direction can lead to inconsistent deployment of sensor networks, resulting in higher maintenance costs and inaccurate long-term assessments of thermal conditions. Based on data analyses of high-accuracy, high-frequency field measurements conducted over a year or longer across multiple offices in Sydney and Shanghai, this study proposes a strategy for long-term temperature monitoring. The strategy advises practitioners to prioritize considerations such as air-conditioning type, room size, and space function when selecting "representative" sensor locations. Typically, sampling every 30minutes is deemed adequate for shared offices where an error margin of ±0.5°C is acceptable. For environments with stable indoor temperatures, less frequent sampling intervals suffice. A power regression model tailored for offices equipped with central AC and no operable windows was developed to predict the maximum allowable sampling interval based on several days of indoor temperature monitoring in winter. Regarding monitoring duration, the study advocates a preferred sampling period of one year to comprehensively capture seasonal variations. Alternatively, a minimum monitoring period of four to six months commencing in late spring or early summer is identified as potentially sufficient. These findings offer valuable insights for optimizing long-term thermal monitoring practices in offices and may contribute to expanding the scope of thermal comfort standards.

Optimizing Catfish Farming Through IoT-Driven Water Quality Management

General Background: Catfish play a significant role in the global fishing industry, necessitating effective farming practices to enhance productivity and sustainability. Specific Background: However, challenges such as maintaining optimal water quality in aquaculture ponds, particularly concerning ammonia levels and temperature fluctuations, pose threats to catfish health and production efficiency. Knowledge Gap: Previous research has often focused on single-parameter monitoring, lacking comprehensive systems that integrate real-time data for both ammonia and temperature management. Aims: This study aims to develop an advanced Internet of Things (IoT) system for real-time monitoring and control of ammonia levels and water temperature in catfish ponds, facilitating optimal farming conditions. Results: The implemented system utilizes two sensors to measure ammonia and temperature, integrated with the Blynk IoT platform for remote monitoring. The findings indicate that when water temperature exceeds 26 °C, the water pump activates to regulate temperature, while ammonia levels above 5 NH3 trigger the aerator to enhance oxygen levels and reduce ammonia toxicity. Novelty: This research innovatively combines multiple sensors and modern IoT applications, offering a more robust solution for aquaculture management compared to previous studies. Implications: The monitoring system effectively maintains water quality, enhancing catfish farming sustainability and efficiency, emphasizing the need for technology integration in aquaculture to effectively tackle environmental challenges. Highlights: Real-time Monitoring: Continuous tracking of ammonia and temperature levels. Automated Control: Activates pumps and aerators based on set thresholds. Enhanced Sustainability: Supports healthier catfish and optimizes production efficiency. Keywords: Catfish Farming, IoT, Water Quality Monitoring, Ammonia Control, Temperature Management

Gas, Temperature, and Humidity Sensors-Based Onion Quality Monitoring System

Gas, Temperature, and Humidity Sensors-Based Onion Quality Monitoring System

Rancang Bangun Alat Otomatisasi Pengomposan dari Sampah Organik Berbasis Internet of Things

Organic waste management in Indonesia is currently suboptimal. Approximately 33.7% of waste is not properly managed, and 56% of the total waste consists of organic waste, most of which comes from households. Composting is an effective method for managing organic waste. However, many people are still reluctant to convert organic waste into compost due to the lengthy process, which typically takes 2 to 3 months. This study proposes an IoT-based composting automation system with the goal of accelerating the composting process without the use of additional microorganisms, producing both dry and liquid compost. The system method involves integrating Internet of Things (IoT) technology with sensors: DHT22 for temperature monitoring, soil moisture sensors for measuring soil moisture, MQ-4 sensors for detecting methane gas, pH sensors for measuring soil acidity, and water level sensors for detecting the height of liquid compost. Data collected from these sensors is used to automatically control the organic waste mixing process until all composting specifications are met. The results indicate that this system effectively accelerates the composting process to 24 days for dry compost and 19 days for liquid compost without the need for additional microorganisms, while producing compost of high quality according to the SNI-19-7030-2004 specifications.