Real-time Temperature Monitoring Research Articles

Optimizing gula apong production with an IoT-based temperature monitoring system

<p>Determining the quality of <em>gula apong</em> is crucial to optimizing its production, with cooking temperature being a key factor affecting both taste and shelf life. The <em>gula apong</em> industry faced challenges due to the lack of reliable real-time temperature monitoring methods during the cooking process. Traditional approaches were inefficient and inaccurate, leading to difficulties in maintaining consistent product quality and meeting market demands. This highlights the necessity of monitoring the temperature throughout each cooking process. This research aims to develop an internet of things (IoT)- based cooking temperature monitoring system to enhance quality control measures in the production of <em>gula apong</em>. The IoT prototype collects temperature data from the thermocouple sensor, then transmits it to cloud storage through a Wi-Fi communication network, utilizing the Node-RED platform for data processing and analysis. Data obtained from the on-site measurement shows that the optimal temperature for producing standard-quality <em>gula apong</em> is approximately around 165 °C. The recommended boiling temperature for Nipah sap is 140 °C. This IoT system can reduce the cooking time of <em>gula apong</em> to 3 hours from the traditional 4 to 6 hours. Utilizing the data acquired from this study helps the producers not only maintaining the quality of <em>gula apong</em> but also reduce the cooking time and cost.</p>

Real Time Monitoring of Temperature And Humidity Using LabVIEW and ML

Abstract: This project presents a Real-Time Temperature and Humidity Monitoring System built on LabVIEW, featuring ML predictive analytics. It facilitates real-time data collection from diverse sensors, ensuring precise environmental monitoring. The ML algorithms within the system forecast future temperature and humidity trends, aiding proactive decision-making. Users enjoy an intuitive graphical interface for visualizing data, analysing historical trends, and receiving alerts for threshold variations. Additionally, the system provides remote monitoring, data logging, and reporting, enhancing its usability and accessibility. This integrated solution caters to the crucial requirement for accurate environmental monitoring, with applications spanning industries like manufacturing, agriculture, and climate control, ultimately enhancing efficiency and decision-making in these sectors.

Posterior wall isolation in patients with persistent atrial fibrillation: a single center comparative study between ablation index and very high power short duration radiofrequency ablation protocol

Abstract Background Circumferential pulmonary veins ablation (CPVA) with posterior wall isolation (PWI) using radiofrequency catheter ablation (RFCA) is gaining increasing interest in terms of safety and efficacy. Purpose To compare procedural data and efficacy between two radiofrequency ablation protocols for the treatment of persistent atrial fibrillation (PersAF): very high power-short duration (vHPSD) ablation using a novel temperature-controlled ablation catheter and Ablation Index (AI) guided RFCA using traditional contact-force catheter. Methods This single center prospective study enrolled 75 consecutive patients (Group 1) with symptomatic PersAF undergoing RFCA using a novel contact-force catheter optimized for temperature-controlled ablation with microelectrodes and 6 thermocouples for real-time temperature monitoring during ablation. The vHPSD algorithm modulates power to maintain target temperature during these lesions (90 Watts, 4 s) in the posterior wall. RF delivery in anterior regions was performed in a temperature guided setting (1-45W, 45°C) to achieve a 500-550 AI. These patients were compared to 75 propensity matched patients (Group 2) undergoing PersAF ablation with traditional AI-guided ablation. Procedural data were compared between the two groups (Table 1). Procedural outcome was considered as loop recorder documented atrial arrhythmias recurrences and compared between the two groups. Results Seventy-five patients (69% male, 63.1±11.2 years old) underwent ablation with vHPSD protocol (Group 1). CPVA and PWI was achieved in all patients with total procedure and fluoroscopy times of 67.5±28.8 min and 4.5±2.8 min, respectively. After 12-month median follow-up, 57/75 patients (76%) were free from AF. Seventy-five propensity matched patients (69% male, 60.7±9.9 years old) have undergone ablation with standard contact-force catheter. CPVA and PWI was achieved in all patients with total procedure and fluoroscopy times of 81.99±20.5 min and 5.7±2.1 min, respectively. After 12-month median follow-up, 55/75 patients (73%) were free from AF. Procedural and fluoroscopy times were significantly lower in in Group 1 than in Group 2 (Table 1). AF recurrencies were comparable between the two groups as assessed with survival analysis (Figure 1, log-rank 0.509). There were no deaths or other severe periprocedural complications. Conclusions PWI on top of CPVA is effective in limiting arrhythmic recurrences following RFCA of PersAF. Compared to standard AI guided ablation, vHPSD protocol significantly reduces procedural time and fluoroscopy exposure. Clinical outcomes between the two protocols are similar.Figure 1

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.

Printed Thick Film Resistance Temperature Detector for Real-Time Tube Furnace Temperature Monitoring.

Accurately acquiring crucial data on tube furnaces and real-time temperature monitoring of different temperature zones is vital for material synthesis technology in production. However, it is difficult to achieve real-time monitoring of the temperature field of tube furnaces with existing technology. Here, we proposed a method to fabricate silver (Ag) resistance temperature detectors (RTDs) based on a blade-coating process directly on the surface of a quartz ring, which enables precise positioning and real-time temperature monitoring of tube furnaces within 100-600 °C range. The Ag RTDs exhibited outstanding electrical properties, featuring a temperature coefficient of resistance (TCR) of 2854 ppm/°C, an accuracy of 1.8% FS (full scale), and a resistance drift rate of 0.05%/h over 6 h at 600 °C. These features ensured accurate and stable temperature measurement at high temperatures. For demonstration purposes, an array comprising four Ag RTDs was installed in a tube furnace. The measured average temperature gradient in the central region of the tube furnace was 5.7 °C/mm. Furthermore, successful real-time monitoring of temperature during the alloy sintering process revealed approximately a 20-fold difference in resistivity for silver-palladium alloys sintered at various positions within the tubular furnace. The proposed strategy offers a promising approach for real-time temperature monitoring of tube furnaces.

IoT ENBALED SMART AQUARIUM FOR MONITORING THROUGH SENSORS

The development of an IoT-enabled smart aquarium system offering real-time monitoring and automated control of crucial environmental parameters. The core of the system lies in the ESP8266 microcontroller, facilitating seamless internet connectivity and data processing. Water quality assessment is achieved through a turbidity sensor, detecting suspended particles indicative of potential pollution. Environmental conditions are monitored using a DHT11 sensor, providing data on both temperature and humidity – essential factors for maintaining a healthy habitat for specific fish species. For automated lighting control mimicking natural day/night cycles and preventing excessive algae growth, a Light-Dependent Resistor (LDR) is implemented. The LDR adjusts aquarium lighting based on ambient light levels. Finally, a relay module controls the operation of a water pump, ensuring optimal water circulation and distribution of nutrients throughout the aquarium. While an I2C LCD connected to an Arduino Nano provides local data visualization within the aquarium setup, the ESP8266 transmits sensor readings to a user-friendly platform accessible remotely through a mobile application or web interface. This allows for convenient real-time monitoring of water quality, temperature, humidity, and lighting conditions. The system can be further programmed to generate alerts when critical parameters deviate from the ideal range, enabling proactive user intervention to maintain a healthy environment. This project demonstrates the potential of IoT technology to create a more automated, efficient, and user-friendly system for maintaining a thriving aquatic ecosystem. KEYWORDS: IoT (Internet of Things), Smart Aquarium, ESP8266, Turbidity Sensor, DHT11 Sensor, LDR(Light-Dependent Resistor), Relay Module, Water Pump, I2C LCD, Arduino Nano, Real-Time Monitoring, Automated Control, Remote Access, Mobile Application, Web Interface, Water Quality, Temperature, Humidity, Light Control, Aquatic Environment.

Heterogeneous CuS QDs/BiVO4@Y2O2S Nanoreactor for Monitorable Photocatalysis.

Exploration of multifunctional integrated catalysts is of great significance for photocatalysis toward practical application. Herein, a 1D confined nanoreactor with a heterogeneous core-shell structure is designed for synergies of efficient catalysis and temperature monitoring by custom encapsulation of Z-scheme heterojunction CuS quantum dots/BiVO4 (CuS QDs/BiVO4) and Y2O2S-Er, Yb. The dispersed active sites created by the QDs with high surface energy improve the mass transfer efficiency, and the efficient electron transport channels at the heterogeneous interface extend the carrier lifetime, which endows the nanoreactor with excellent catalytic performance. Meanwhile, real-time temperature monitoring is realized based on the thermally coupled levels 2H11/2/4S3/2→4I15/2 of Er3+ using fluorescence intensity ratio, which enables the monitorable photocatalysis. Furthermore, the nanoreactor with a multidimensional structure increases effective intermolecular collisions to facilitate the catalytic process by restricting the reaction within distinct enclosed spaces and circumvents potential unknown interaction effects. The design of multi-space nanoconfined reactors opens up a new avenue to modulate catalyst function, providing a unique perspective for photocatalytic applications in the mineralization of organic pollutants, hydrogen production, and nitrogen fixation.

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 (EMG), 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.

PEDOT/flower-like 1 T-2H MoS2/nitrogen-doped graphene ternary nanocomposite for efficient room temperature real-time monitoring of ammonia

PEDOT/flower-like 1 T-2H MoS2/nitrogen-doped graphene ternary nanocomposite for efficient room temperature real-time monitoring of ammonia

Benchmarking core temperature forecasting for lithium-ion battery using typical recurrent neural networks

One of the factors hindering the scaled application of lithium-ion batteries is their thermal safety issues. Real-time monitoring of core temperature holds promise in alleviating this concern. However, calibrating individual models for thousands of battery cells poses challenges and leads to big data issues. The maturing and rapidly advancing artificial intelligence technologies hold promise in addressing the task of core temperature prediction. In this study, we conduct a benchmark test using typical recurrent neural networks for core temperature prediction. The test encompasses three main aspects: data acquisition, data processing, and data analysis. Firstly, we conduct dynamic battery tests across a wide temperature range on a custom-built experimental platform. Subsequently, we employ big data techniques for data pre-processing, including filtering, normalization, and sliding window methods. Next, hyperparameter optimization is then performed using the Bayesian optimization with Tree-structured Parzen Estimator, followed by K-fold cross validation. Consequently, we obtain three optimal performing models based on typical recurrent neural networks. Finally, we extensively compare these models in terms of performance and complexity. According to the results, all data-driven methods achieve satisfactory model performance, with RMSE within 0.17 °C and MAE within 0.15 °C. Among the models with similar predictive performance conditions, gated recurrent unit exhibits the lowest model complexity and faster training speed. Meanwhile, we also validate the portability and generalizability of the forecasting models. This study lays the foundation for future research in this area and can provide guidance for engineering practical applications to ensure the safe operation of battery energy storage systems.

Rational Design of a Surface Acoustic Wave Device for Wearable Body Temperature Monitoring.

Continuous monitoring of vital signs based on advanced sensing technologies has attracted extensive attention due to the ravages of COVID-19. A maintenance-free and low-cost passive wireless sensing system based on surface acoustic wave (SAW) device can be used to continuously monitor temperature. However, the current SAW-based passive sensing system is mostly designed at a low frequency around 433 MHz, which leads to the relatively large size of SAW devices and antenna, hindering their application in wearable devices. In this paper, SAW devices with a resonant frequency distributed in the 870 MHz to 960 MHz range are rationally designed and fabricated. Based on the finite-element method (FEM) and coupling-of-modes (COM) model, the device parameters, including interdigital transducer (IDT) pairs, aperture size, and reflector pairs, are systematically optimized, and the theoretical and experimental results show high consistency. Finally, SAW temperature sensors with a quality factor greater than 2200 are obtained for real-time temperature monitoring ranging from 20 to 50 °C. Benefitting from the higher operating frequency, the size of the sensing system can be reduced for human body temperature monitoring, showing its potential to be used as a wearable monitoring device in the future.

Advancing 2D reaction rate measurements in BNCT: Validation of the indirect neutron radiography method

The precise characterization of neutron beams is a cornerstone of Boron Neutron Capture Therapy (BNCT). While Instrumental Neutron Activation Analysis (INAA) is the standard technique for neutron flux measurement, it is limited in its ability to capture two-dimensional (2D) reaction rate distributions. This study aims to validate the Indirect Neutron Radiography (INR) method for 2D reaction rate quantification, addressing critical variables such as temperature sensitivity and signal fading. We designed and constructed an optimized INR platform comprising an Imaging Plate (IP), readout device, activation detectors (copper foils), and real-time temperature monitoring. Comprehensive experiments were conducted to investigate the impact of ambient temperature and fading time on IP signal reliability. A robust calibration curve was formulated, linking IP signals to dose deposition metrics, thereby enabling precise reaction rate assessments. The study found that IP signals are minimally sensitive to temperature variations (less than 0.1% change per 1 °C), but are subjected to linear fading over time, necessitating stringent temperature control and time-dependent signal corrections. A mathematical relationship between IP signals and dose deposition was established, represented by Y = 20,500 × x0.01803-21103. Application of the INR method revealed that the depth-dependent reaction rates in copper strips closely aligned with those acquired through INAA, exhibiting a relative deviation of less than 5% within a 4cm depth range inside the phantom. Our findings demonstrate that the INR method offers a robust alternative to traditional INAA for capturing 2D reaction rates, effectively addressing complexities like temperature sensitivity and signal fading. While challenges persist, particularly in the realm of measurement errors, this study lays the groundwork for further methodological refinements and broadens the scope for future research in BNCT neutron beam characterization.

The marine diesel engine exhaust gas temperature baseline model based on particle swarm optimised generalised regression neural network

ABSTRACT This study addresses limitations in traditional condition monitoring for marine diesel engine (MDE) reliability and stability by proposing a hybrid machine learning and deep learning (DL) model called Particle Swarm Optimization-Generalized Regression Neural Network (PSO-GRNN) for real-time exhaust gas temperature (EGT) monitoring. The model integrates Mahalanobis distance (MD) calibration, wavelet packet denoising, and Pearson correlation analysis for comprehensive data preprocessing. Validation is conducted using historical '6L34DF' diesel engine data. Particle Swarm Optimization (PSO) optimizes the spread value (σ) of the Generalized Regression Neural Network (GRNN) to establish an optimal baseline model for EGT prediction. Experimental results show that PSO-GRNN outperforms Backpropagation Neural Network (BPNN), GRNN, Bidirectional Gated Recurrent Unit (BiGRU), Genetic Algorithm- Generalized Regression Neural Network (GA-GRNN), and Deep Belief Network-Support Vector Regression (DBN-SVR) in terms of training time and accuracy, demonstrating its suitability for MDE baseline modeling.

Thawing process of high-ice-content frozen soil subjected to saturated steam

Engineering measures based on permafrost protection to inhibit high-ice-content permafrost thawing and subsidence in some areas of the Qinghai-Tibetan Plateau (QTP) are challenging owing to global warming. Hence, early permafrost thawing is an effective method for reducing the later settlement of the upper infrastructure. Saturated steam can thaw permafrost rapidly, but the research on the process and mechanisms of thawing permafrost with steam is lacking. In this study, steam-thawing tests were conducted on four types of frozen soil with high ice contents (20 %, 30 %, 40 %, and 50 % by volume). The temporal and spatial migration patterns of temperature and moisture in permafrost subjected to steam and the resulting thawing rates were analyzed by real-time monitoring of temperature and the volumetric water content (VWC) during the thawing process. Based on this, the mechanism of steam thawing permafrost was analyzed. The results showed that the continuous injection of saturated steam into the permafrost for 2 h resulted in complete thawing of permafrost with all four ice-contents, but the thawing rates were significantly different. The injection of steam led to an increase in VWC of thawed soil within a 5 cm radius by 1.26–1.68 times. The thawing of permafrost at different ranges was dominated by two different modes, pressure-driven water steam migration leading to permafrost thawing at a radius of about 10 cm and thawing of permafrost at the rest of the range by hydrothermal migration induced by temperature and humidity gradient. Compared to other conventional thawing methods, steam thawing is more suitable for targeted pre-thawing of deeply permafrost layers with high ice content.

Microwave ablation combined with percutaneous vertebroplasty for treating painful non-small cell lung cancer with spinal metastases under real-time temperature monitoring.

To retrospectively study the therapeutic effect and safety performance of the combination strategies of the computed tomography (CT)-guided microwave ablation (MWA) and percutaneous vertebroplasty (PVP) as a treatment for painful non-small cell lung cancer (NSCLC) with spinal metastases. A retrospective review included 71 patients with 109 vertebral metastases who underwent microwave ablation combined with percutaneous vertebroplasty by the image-guided and real-time temperature monitoring. Treatment efficacy was determined by comparing visual analog scale (VAS) scores, daily morphine equivalent opioid consumption, and Oswestry Disability Index (ODI) scores before treatment and during the follow-up period. Technical success was achieved in all patients. The mean pre-procedure VAS score and morphine doses were 6.6 ± 1.8 (4-10) and 137.2 ± 38.7 (40-200) mg, respectively. The mean VAS scores and daily morphine doses at 24 h and 1, 4, 12, and 24 weeks postoperatively were 3.3 ± 1.9 and 73.5 ± 39.4 mg; 2.2 ± 1.5 and 40.2 ± 29.8 mg; 1.7 ± 1.2 and 31.3 ± 23.6 mg; 1.4 ± 1.1 and 27.3 ± 21.4 mg; and 1.3 ± 1.1 and 24.8 ± 21.0 mg, respectively (all P < 0.001). ODI scores significantly decreased (P < 0.05). Minor cement leakage occurred in 51 cases (46.8%), with one patient having a grade 3 neural injury. No local tumor progression was observed by follow-up imaging. MWA combined with PVP can significantly relieve pain and improve patients' quality of life, which implied this is an effective treatment option for painful NSCLC with spinal metastases. Additionally, its efficacy should be further verified through the mid- and long-term studies.

Dual-mode optical thermometers via the thermochromic Bi3+, Eu3+ co-doped La2LiSbO6 phosphors for real-time chip temperature monitoring

In recent years, with the increasing demand for non-contact temperature measurement, it is urgent to develop a new type of phosphor with double emission centers, high sensitivity and good reliability. A series of Bi3+and Eu3+ co-doped La2LiSbO6 double emission center phosphors were successfully prepared. The response characteristics of Bi3+and Eu3+ at different temperatures in the range of 293–473k were calculated by fluorescence intensity ratio (FIR) technique. The optimal relative sensitivity Sr and absolute sensitivity Sa were 1.13 % K−1 and 1.27 % K−1, respectively. Hence, La2LiSbO6:Bi3+, Eu3+ phosphors have the capability of non-contact optical temperature measurement and are a promising material for application. The phosphor is combined with the optical fiber temperature sensor to monitor the real-time temperature of the computer central processing unit. The optical fiber temperature sensor system breaks through the limitations of traditional temperature measurement, and can be used in special environments such as small size and strong electromagnetic interference. It has become an ideal choice to replace the traditional temperature sensor.

Design of a multi-physics coupling MEMS pressure sensor

The pressure measurement of explosion shock wave puts forward high requirements on the temperature resistance and response speed of the sensor. In this paper, a multi-physics coupling pressure sensor is designed to meet requirements. The sensor mainly consists of a Fabry-Perot cavity optical fiber sensing unit, a piezoelectric pressure sensing unit and a thermometric resistance temperature measurement module. Firstly, the structure of the sensor is designed. The top and bottom surfaces of the Fabry-Perot cavity are composed of a silicon diaphragm and a quartz glass. The piezoelectric pressure sensing unit is an AlN film including its electrodes. Pt thermometric resistance is developed to realize real-time temperature monitoring. Secondly, the fabrication process of the sensor is discussed, especially three key technologies. An Al film is sputtered to increase the reflectivity of the quartz glass. The SU-8 photoresist is applied to accurately control the thickness of Fabry-Perot cavity. Etching is adopted to work out the graphics of the AlN piezoelectric film. At last, a complete fabrication process of the sensor is described. The fabrication of multilayer films begins with a double-sided polishing silicon wafer, while Fabry-Perot cavity was from a double-sided polishing quartz glass. Then two parts of the sensor are combined to ensure the multi-physics coupling sensor to achieve complete function.

Effects of capacity on the thermal runaway and gas venting behaviors of large-format lithium iron phosphate batteries induced by overcharge

Large-capacity lithium iron phosphate (LFP) batteries are widely used in electric bicycles. However, while crucial, thermal runaway (TR) behaviors under overcharge conditions have rarely been studied, leading to frequent fire accidents. This paper investigates the overcharge behavior and TR characteristics of four LFP batteries with the same components and materials but varying capacities (86, 100, 120, and 140 Ah). Overcharge tests were conducted in a customized apparatus, and the surface temperature, mass loss, and gas venting phenomenon were recorded. The results revealed that under the same overcharge conditions, the TR early warning temperature can differ considerably with respect to battery capacity, highlighting the importance of selecting the appropriate early warning temperature in real-time temperature monitoring based on the capacity of the LFP battery. Three characteristic times were used to describe the thermal behavior of the batteries, namely, the time to reach the voltage plateau (tPV), the time when the safety valve opens (tSV), and the start time of TR (tTR). Analysis of critical heat accumulation indicated that the 86 Ah LFP battery has the lowest tolerance to overcharge and, thus, the highest TR risk. However, after TR, the 86 Ah LFP battery exhibited the smallest maximum temperature (Tmax), maximum temperature increase rate (Rmax), maximum total mass loss, and maximum mass loss rate (Mmax) among all the batteries, suggesting the least TR hazard. Consequently, a safety evaluation and scoring system using these six parameters was established considering the overcharge behavior and TR characteristics of LFP batteries. The results indicated that the TR risk ranking of LFP batteries with different capacities was 86 Ah LFP > 100 Ah LFP > 140 Ah LFP > 120 Ah LFP, while the TR hazard ranking was 140 Ah LFP > 120 Ah LFP > 100 Ah LFP > 86 Ah LFP. This comparative study of LFP batteries with varying capacities enhances our understanding of their overcharge behavior and TR characteristics, and a safety evaluation and scoring system can also be used for risk and hazard assessments of other types of batteries.

Self-powered wireless flexible sensing for food storage based on triboelectric-electromagnetic generator

In Controlled Atmosphere (CA) food storage, maintaining optimal conditions, including gas concentration, temperature, and humidity on the food surface, is crucial. This paper proposes the application of a self-powered wireless flexible sensing system based on a triboelectric-electromagnetic generator in food CA storage. To configure certain concentrations of mixed CA gases, we need to accurately control the volume of CA gas input into the mixed gas tank within a certain period, and the gas flow velocity in the pipeline will directly affect this factor. The gas flow velocity in the pipeline is detected by the CA gas flow velocity detection part (FVS) with an independent layer working mode triboelectric nanogenerator (FV-TENG) as the main component. Simultaneously, a flexible Temperature and Humidity Sensor (THS) affixed to the food surface provides real-time monitoring. EMG collects irregular wind energy in the CA warehouse to provide energy for the processing of information in the system and wireless transmission to the IoT cloud platform, realizing the system's self-power and wireless transmission. The self-powered wireless sensing system facilitates remote collection of CA gas flow velocity data, precise control of mixed atmosphere gas concentrations, and real-time monitoring of food surface temperature and humidity in the CA warehouse.

Integrated micro thermoelectric devices with self-power supply and temperature monitoring: Design and application in power grid early warning

The Internet of Things (IoT) and wearable technology have led to the development of wireless sensors at an unprecedented pace. Within the IoT framework, smart grid systems require real-time temperature monitoring of various switch contacts. Especially in the process of power transformation (China: ultra-high voltage → 10 kV → 380 / 220 V), poor switch contact will lead to overheating, resulting in equipment damage and major safety accidents. Micro-thermoelectric devices (micro-TEDs) exhibit a significantly greater thermoelectromotive force than metal-based thermocouples. By establishing two bivariate first-order equations for hot-end and cold-end temperatures, accurate detection of both temperatures and simultaneous self-power supply can be achieved. In this work, we observe that the Seebeck coefficient of hot extruded n-type Bi2Te2.7Se0.3 and p-type Bi0.5Sb1.5Te3 thermoelectric couple exhibits slight fluctuations around 435 μV/K, while its resistivity follows an almost linear trend with temperature from room temperature to 120 °C. By utilizing these two bivariate first-order equations, the temperatures at both the hot and cold ends can be determined. Based on this, we have fabricated a micro-TED (size: 14 × 6 × 2.5 mm3, TE leg: 0.8 × 0.8 × 1.6 mm3) with a temperature measurement accuracy of up to ± 0.1 °C and a power density of 10.12 mW/cm2 under a temperature difference of 30 °C. Furthermore, we used thirteen micro-TEDs and one circuit board to build a self-power supply temperature monitor. When this TE sensor device was deployed on the plum blossom connector surface and conducted online assessments for the 10 kV substation over a span of approximately 200 h, it continuously outputs a temperature signal and the power supply maintained normalcy, exhibiting no aberrations. This temperature monitoring system can be extended to other fields that require all-weather wireless temperature monitoring, which provides a new direction for the development of micro-TEDs.