Temperature Monitoring Research Articles

Cryoballoon-Assisted Pulmonary Vein Isolation and Left Atrial Roof Ablation Using a Simplified Sedation Strategy without Esophageal Temperature Monitoring: No Notable Thermal Esophageal Lesions and Low Arrhythmia Recurrence Rates after 2 Years.

Atrial fibrillation (AF) ablation is increasingly effective for managing heart rhythm but poses risks like esophageal fistulas. Minimizing esophageal thermal lesions while simplifying procedures is crucial. This prospective study involved 100 consecutive AF patients undergoing cryoballoon ablation with simplified sedation, without esophageal temperature monitoring. Patients with paroxysmal AF (Group A) received pulmonary vein isolation only, while those with persistent AF (Group B) also had left atrial roof ablation. Gastroesophageal endoscopy was performed post-procedure to detect lesions, and cardiological follow-ups were conducted at 3, 12, and 24 months. The cohort included 69% men, with a median age of 65.5 years. Post-ablation endoscopy was performed in 92 patients; esophageal lesions were found in 1.1% of Group A and none of Group B. GERD was diagnosed in 14% of patients, evenly distributed between groups and not linked to lesion occurrence. Gastric hypomotility was observed in 16% of patients, with no significant difference between groups. At 24 months, arrhythmia-free survival was 88% in Group A and 74% in Group B. Cryoballoon-assisted pulmonary vein isolation, with or without additional left atrial roof ablation and without esophageal temperature monitoring during a simplified sedation strategy, shows low risk of esophageal thermal injury and effective ablation outcomes.

Decreasing incidence of admission neonatal hypothermia in Gandhi Memorial Hospital, Addis Ababa, Ethiopia: quality improvement project

BackgroundWHO reported that neonatal hypothermia accounts for about 27% of newborn deaths worldwide. It is a serious concern in Ethiopia and other parts of sub-Saharan Africa; it poses a serious...

Eye Temperature Measured with Infrared Thermography to Assess Stress Responses to Road Transport in Horses.

The aim of the present study was to investigate eye temperature modifications after road transport in athletic horses habituated to travel. Eight adult Italian saddle horses traveled 100 km and, two weeks later, 300 km. Eye temperature (ET), rectal temperature (RT) and serum cortisol concentration were assessed before (T1), after (T2) and 60 min (T3) after the road transport. ET was evaluated with infrared thermography (IRT) in three regions of interest: EL1 (medial canthus), EL2 (central cornea) and EL3 (lateral canthus). Two-way analysis of variance (ANOVA) for repeated measures showed statistically higher values at T2 and T3 for EL1 (p < 0.01), EL2 (p < 0.01) and EL3 (p < 0.01) following the 100 km journey. RT (p < 0.01) showed higher values at T2 and T3 after the 100 km journey and higher values at T2 (p < 0.01) following the 300 km journey. ET values were positively correlated with RT at T1, T2 and T3 following the 100 km journey and at T2 following the 300 km journey and positively correlated with serum cortisol concentration at T1, T2 and T3 following the 100 km journey and at T2 and T3 following the 300 km journey. Eye temperature monitoring with IRT allows quick and practical strategies to monitor an animal's physiological state and welfare during daily activities.

DATAWARE AND SOFTWARE OF THE AUTOMATED TECHNOLOGY FOR COMPUTER- INTEGRATED CONTROL OF HEAT PUMP SYSTEMS

The paper has solved a scientific and applied problem as for substantiating requirements to technical and functional characteristics of the automated technologies of computer-integrated control of heat pump systems in the heating and cooling modes owing to its dataware and software developing and testing. Following functional procedures have been taken into consideration during the dataware and software development of the computer-integrated subsystem for heating of buildings: monitoring of ambient air temperature, ground temperature, and operating modes of functional elements of the heat pump system as well as zonal monitoring of indoor air temperature; computer-integrated processing of the monitoring data; automated control of the heat pump as well as modes of zonal thermal medium supply to the building; and analysis of energy efficiency as for implementation of the proposed computer-integrated method to control systems of thermodynamic heating. It has been identified that thermal capacity of a heat pump is rather sensitive to indicators of the total heat losses of a building. If the total heat losses increase from 0.2°С/hour up to 0.5°С/hour then constant value of the thermal capacity increases by 48%. Moreover, thermal capacity of a heat pump is quite sensitive to ambient air temperature dynamics: if temperature variation is 12°С (i.e. 3°С-9 °С) then average increase in thermal capacity is 58 %. Following functional procedures have been involved during the dataware and software development of the computer-integrated subsystem for cooling of buildings: monitoring of ambient air temperature and operating modes of functional elements of the heat pump system; zonal computer-integrated monitoring of indoor air temperature; computer-integrated the monitoring data processing based upon microcontrollers; automated control of the thermal pump operating modes (i.e. heat energy outfeed and release to the main intercooler unit); and analysis of energy efficiency as for implementation of the proposed computer-integrated method to control systems of thermodynamic cooling of buildings. The abovementioned has helped identify that thermal capacity of a heat pump is rather sensitive to indicator of heat exchange with surrounding air: four times increase in heat-exchange coefficient (i.e. from 1°С/hour up to 4°С/hour) results in 20% growth of thermal capacity of the cooling subsystem system at similar ambient air temperature (it varies from 30°С up to 36 °С) if the maintained target indoor temperature is 22°С.

A data-driven method for estimating sewer inflow and infiltration based on temperature and conductivity monitoring

Quantitation of sewer inflow and infiltration (I/I) is important for maintaining efficient wastewater transport and treatment. I/I flows can be quantified based on flow rate and water quality measurements. Flow rate-based methods require continuous monitoring of flow rates using flow meters that are costly and prone to fouling. In comparison, conductivity and temperature, as simple water quality parameters, are more easily measurable with more cost-effective and reliable sensors. In this study, a data-driven methodology is developed for estimating I/I flows based on online conductivity and temperature measurements. A Prophet-model-based analytic algorithm is first developed to reconstruct the temperature and conductivity profiles of the base wastewater flow (BWF) from the measured temperature and conductivity time series. The algorithm is shown to be able to reconstruct the BWF temperature and conductivity profiles in two monitored catchments. The reconstructed BWF data are then incorporated into mass/energy balance equations for estimating I/I flows from the measured temperature and conductivity data. The overall I/I quantification method is finally demonstrated using simulation studies of a real-life sewer network and validated against the known I/I flows. This work provides a reliable method for I/I quantification based on simple measurements.

Temperature Monitoring Efficiency With Internet of Things-Based Temperature Sensor Variations

This research was conducted in the lab. Automation to measure temperature by several temperature sensors monitored through Internet of Things (IoT) technology. The method used is a quantitative method with experiments. The temperature monitoring system with a variety of temperature sensors based on the Internet of Things (IoT) can work well. The difference in temperature measurements taken through the IoT simulation within 1 hour between the sensors used has a small value, an average of 0.07 Celsius. This means that the precession level of each sensor is relatively good. The need for calibration on the sensor in order to obtain the best measurement results. Noting the results of the above analysis, it is recommended that users conduct a more in-depth study to obtain a more appropriate (actual) value. This analysis can also be taken into consideration for users to develop IoT-based monitoring systems in the future.

Application of ZigBee Technology in Smart Soil Temperature and Humidity Control Systems

As global food security increasingly becomes a focus, challenges faced by Chinese agriculture are particularly prominent. This study introduces an intelligent monitoring and control system for soil temperature and humidity using ZigBee technology, aimed at enhancing agricultural production efficiency and crop yield. The system's core, equipped with an STC89C52 microcontroller and DHT11 temperature and humidity sensors, gathers data and wirelessly transmits it to a central control unit via ZigBee modules, which then automatically adjusts the agricultural production environment to ensure crops grow in the most ideal soil temperature and humidity conditions. Compared to traditional methods, this system achieves remote monitoring and precise control, providing an innovative solution for modern agricultural production. Experiments prove that the system not only improves control accuracy but also significantly enhances operational convenience and response speed, playing an important role in promoting the development of smart agriculture.

Harnessing bright upconversion luminescence from PEG-coated NaGdF4:Ho3+/Yb3+ nanoparticles for contrast enhancement in dual mode OCT imaging and optical temperature sensing

In this work, authors synthesized the Polyethylene Glycol (PEG) coated NaGdF4:Ho3+/Yb3+ nanoparticles using the thermal decomposition reaction route. Our primary objective was to comprehensively evaluate their suitability for augmenting image contrast, particularly within the intricate domain of Swept Source Optical Coherence Tomography (SSOCT) and photo-thermal Optical Coherence Tomography (PTOCT) imaging modalities. Our meticulous inquiry yielded the revelation of extraordinary upconversion emissions, which emanated from the distinct transitions of Ho3+ ions, most notably the 5F4(5S2)→ 5I8 and 5F5→ 5I8 transitions. These transitions manifested as strikingly vibrant green emissions, precisely at the 539 nm and 661 nm wavelengths. Furthermore, we investigated the underlying upconversion mechanism, delving deeply into the elucidation of decay times and the nanoparticles' ability to transform incident radiation into thermal energy. Impressively, these nanoparticles exhibited a noteworthy photo-thermal conversion efficiency, specifically measured at an impressive 47.4 % along with the bright green luminescence. Our study also extended to bio-compatibility, where we achieved highly encouraging outcomes. Following 24 h of incubation with HeLa cells treated with UCNP-PEG, we ascertained a compelling cell survival rate exceeding 80 %, underscoring the favorable biocompatibility of these nanoparticles. Moreover, these nanoparticles demonstrated a conspicuous thermal sensitivity, quantified at 5.1 × 10−3 K−1 at 380K, thus signaling their potential for precision temperature monitoring and could be applied at the cellular level.

Uncertainty assessment of thermal recovery and subsurface temperature changes induced by high-temperature aquifer thermal energy storage (HT-ATES): A case study

High-temperature aquifer thermal energy storage (HT-ATES) systems can store renewable-based or waste heat in the subsurface on a seasonal scale and may thus reduce the carbon footprint of future heat supply systems. Thermal recovery, i.e. the ratio of extracted to injected heat over one cycle, is required in a pre-application assessment because it determines the operational and economic viability of a HT-ATES system. The induced temperature changes in the subsurface are required to obtain the legal permits and are of interest for the design of a monitoring network. However, uncertainty in our knowledge on subsurface hydraulic and thermal parameters translates into uncertainties of the expected thermal recovery and the temperature changes induced during HT-ATES operation. In order to address these uncertainties for a case study in Hamburg, Germany, we use numerical modeling of the coupled thermo-hydraulic processes during the design stage of a HT-ATES system, based on a realistic load curve and the local geological setting of the storage aquifer. An ensemble of 50 scenarios was parameterized based on site-specific parameter uncertainties using Latin hypercube sampling to reflect the global parameter distributions, and the HT-ATES operation was simulated over a period of 26 years in each case. Most of the scenarios show high thermal recoveries with a median of 89 % in the 26th year, with thermal recovery most sensitive to the vertical hydraulic conductivity. The expected temperature distribution is well defined by the ensemble of model simulations, with far-field temperature changes reaching for hundreds of meters and showing greater variability between scenarios than those in the near-field region of the warm HT-ATES well on the tens of meters scale. Locations with large temperature differences between scenarios are identified as suitable for the placement of temperature monitoring wells. The presented work thus contributes directly to the design and permitting of HT-ATES systems and can also be used for uncertainty assessment of future HT-ATES plants and the identification of suitable monitoring setups.

Lithium‐ion Battery Safety Enhancement via Internal Multi‐Parameter Simulation of Thermal and Pressure Conditions

AbstractSafety incidents with lithium‐ion batteries have impeded the battery industry‘s progress. As internal battery reactions often precede visible symptoms, and traditional electrical parameters are insufficient for comprehensive state assessment and hazard prediction, this study utilized multi‐physics simulations to analyze non‐electrical parameters, focusing on temperature and pressure dynamics. Partial validation of the simulation results was achieved through temperature monitoring experiments under varying charge/discharge rates. The findings indicated that internal temperature uniformly increased, with higher rates leading to more pronounced rises, and identified that lithium dendrite formation and mechanical stress could initiate thermal runaway. To mitigate these risks, specific operational guidelines were suggested. The study also established a positive correlation between pressure changes and temperature, influencing the battery‘s volume. Temperature monitoring further validated the simulations′ precision. This research deepens the understanding of non‐electrical parameter functions during battery operation and provides scientific evidence for the prediction and prevention of lithium‐ion battery safety risks.

Noninvasive Temperature Measurements in Tissue-Simulating Phantoms Using a Solid-State Near-Infrared Sensor.

The monitoring of body temperature is a recent addition to the plethora of parameters provided by wellness and fitness wearable devices. Current wearable temperature measurements are made at the skin surface, a measurement that is impacted by the ambient environment of the individual. The use of near-infrared spectroscopy provides the potential for a measurement below the epidermal layer of skin, thereby having the potential advantage of being more reflective of physiological conditions. The feasibility of noninvasive temperature measurements is demonstrated by using an in vitro model designed to mimic the near-infrared spectra of skin. A miniaturizable solid-state laser-diode-based near-infrared spectrometer was used to collect diffuse reflectance spectra for a set of seven tissue phantoms composed of different amounts of water, gelatin, and Intralipid. Temperatures were varied between 20-24 °C while collecting these spectra. Two types of partial least squares (PLS) calibration models were developed to evaluate the analytical utility of this approach. In both cases, the collected spectra were used without pre-processing and the number of latent variables was the only optimized parameter. The first approach involved splitting the whole dataset into separate calibration and prediction subsets for which a single optimized PLS model was developed. For this first case, the coefficient of determination (R2) is 0.95 and the standard error of prediction (SEP) is 0.22 °C for temperature predictions. The second strategy used a leave-one-phantom-out methodology that resulted in seven PLS models, each predicting the temperatures for all spectra in the held-out phantom. For this set of phantom-specific predicted temperatures, R2 and SEP values range from 0.67-0.99 and 0.19-0.65 °C, respectively. The stability and reproducibility of the sample-to-spectrometer interface are identified as major sources of spectral variance within and between phantoms. Overall, results from this in vitro study justify the development of future in vivo measurement technologies for applications as wearables for continuous, real-time monitoring of body temperature for both healthy and ill individuals.

Passive thermal control design and flight operation results of nano moon lander OMOTENASHI

OMOTENASHI is a 6U CubeSat launched by the first NASA SLS rocket. Its mission was to demonstrate that a CubeSat can make a semi-hard landing on the Moon. Tight resource constraints make it difficult to mount a heater and a radiator in a CubeSat, so passive thermal control is a prominent design feature of OMOTENASHI that resolves the technical difficulty. In November 2019, a thermal vacuum test was conducted. This paper discusses the thermal design and the test results. OMOTENASHI was launched by SLS rocket on November 16, 2022. After the SLS rocket separation, OMOTENASHI's attitude control malfunctioned so it could not generate power from its solar cells. Eventually, communications were lost and have not been restored. Brief telemetry data confirmed that the temperature monitor functioned correctly for 30 min. This paper reports the flight results of the thermal status during the first ground station pass. Also, a comparison between the analysis model and telemetry data is shown. Finally, based on its estimated trajectory, the future thermal profile of the spacecraft and the result of the search operation are discussed.

Design of Temperature Monitoring and Control System in Smart Hen Coop Based on Internet of Things

Every temperature error detected by the system would be notified to users by the Blynk application, and the system would attempt to stabilize the temperature in hen coop with a lamp and fan. Notification from the Blynk application is set to stop until the temperature returns to an ideal. The Blynk application is equipped with a reset button to reset the chicken age counter. This system used temperature and humidity sensors DHT22 to sense the temperature, which the data from the sensor output read by Arduino Uno. The data that Arduino Uno reads is sent to NodeMCU. Afterward, NodeMCU sent the data to the Blynk server; therefore, the Blynk application displayed the temperature and humidity data on the Blynk dashboard. This system is expected to facilitate users/breeders in monitoring the temperature in the hen coop. Therefore, users/breeders do not need to worry about chickens dying due to heat or cold stress. The waterfall method is used in this design of the system. This system was tested in a 70 x 70 x 130 cm sized hen coop and entirely tested the system each minute for 35 minutes.

Smart Spam Detection and Correction for Temperature in Heater

The efficient operation of heating systems relies heavily on accurate temperature monitoring. However, such systems are vulnerable to data corruption, which can lead to erroneous temperature readings and potentially hazardous conditions. We propose a novel approach to address this challenge by employing machine learning techniques for spam detection and correction in heater temperature data for the detection phase. We explore various machine learning algorithms including spam detection and classification models, to identify spam temperature readings. These models are trained on the preprocessed dataset and evaluated using appropriate metrics to assess their performance.

Anesthesia in Emergency PDA Stenting with Pulmonary Arteri Atresia

Pulmonary atresia with an intact ventricular septum is a condition that is characterized by a complete obstruction to right ventricular outflow. This condition is fatal if untreated. Ductus arteriosus communicate between pulmonary artery and proximal descending aorta. It is essential in fetal life as a communicating shunt to send blood from right ventricle bypassing the underdeveloped lungs. Ductal patency may be lifesaving in few cyanotic congenital heart diseases to maintain pulmonary blood flow.Case Illustration : We present a case of a 1‑day‑old, 3405 g child who presented to casualty with bluish discoloration. The child had central cyanosis, tachypnea with saturation 80% on 1 L/minute nasal cannula and desaturate to 40%. Transthoracic echocardiography (TTE) revealed intact intraventricular septum with pulmonary atresia and patent ductus arteriosus (PDA). The patient scheduled to an emergency PDA stenting. A heating blanket is installed on the operation table, oxygen saturation with Pulse‑oxymeter, noninvasive blood pressure, end-tidal CO2 monitor, and temperature monitoring were done. Anesthesia induction was performed with 50% oxygen in air, fentanyl 3 µg/kg, ketamine 2 mg/kg, atracurium 0.5 mg/kg and maintained with sevoflurane. The procedure was performed about 20 to 30 minutes. After procedure, oxygen saturation was increased up to 60%. The child was successfully extubated after 24 hours in the Neonatal Intensive Care Unit (NICU) with saturation 92%.Conclusion: PDA stenting is a less invasive palliative procedure for ductal dependent complex congenital heart diseases. Anesthesiologist's role were maintaining arterial saturation and hemodynamics, securing vascular assess, maintaining temperature. It needs thorough understanding of underlying physiology.

Analytical methods for studying mummification technique and degradation process of a human mummy from the late period

Improper burial and storage conditions significantly impact the degradation process of mummies. Factors such as pressure, fluctuations in humidity and temperature, and exposure to light can accelerate this degradation. This study focuses on understanding the degradation mechanism of a mummy from the Late Period, currently stored in the excavation stores at Saqqara, Giza, Egypt. Various techniques including temperature and humidity monitoring, light intensity measurement, internal water content analysis, digital microscopy, scanning electron microscopy with energy dispersive X-ray spectroscopy (FESEM-EDX), X-ray radiography, internal endoscopy, Fourier-transform infrared spectroscopy (FTIR), and isolation and identification of fungi were employed to investigate the mummy's condition.The study found that the soil's mechanical stress caused bone fractures in the mummy's body. Increased humidity led to the dissolution of natron salt, which penetrated the body and surface, resulting in decay of soft tissue and bone. Various microscopes revealed cracks, insect holes, and other damage. Internal endoscopy confirmed body removal and decay. X-ray radiography showed the brain had been removed without resin filling the space. FTIR analysis identified the presence of natron salt, bitumen, beeswax, Arabic gum, and plant oil in the mummification process. Additionally, it showed the chemical stability changes in the mummy components. Fungi such as Penicillium implicatum, Aspergillus flavus, and Trichoderma sp. were also identified.

Thermoelectric-Feedback Nanocomposite Hydrogel for Temperature-Synchronized Monitoring and Regulation in Accurate Photothermal Therapy.

Photothermal therapy (PTT) is a promising approach for tumor ablation and cancer treatment. However, controlling the therapeutic temperature during treatment remains challenging, and imprecise thermal regulation can harm adjacent healthy tissues, reduce therapeutic accuracy, and promote the thermotolerance of cellular phenotypes, potentially leading to tumor invasion and recurrence. Although existing methods provide basic temperature control by adjusting irradiation power and photothermal agent dosing, they lack real-time temperature monitoring and feedback control capabilities, underscoring the urgent need for more integrated and precise PTT systems. In this context, an innovative photothermoelectric (PTE) cobalt-infused chitosan (CS) nanocomposite hydrogel (PTE-Co@CS) is developed for precise temperature-regulated PTT, exhibiting desirable mechanical properties and exceptional biocompatibility. Enhanced by embedded nanoparticles, PTE-Co@CS demonstrates superior photothermal conversion efficiency compared with existing methods, while also featuring thermoelectric responsiveness and increased sensitivity to photostimuli. Its advantageous PTE response characteristics ensure a linear correlation between temperature shifts and resistance changes (e.g., R2 = 0.99919 at 0.5Wcm⁻2), enabling synchronized qualitative and quantitative control of PTT temperature through electrical signal monitoring. This allows for real-time monitoring and regulation during PTT, effectively addressing the issue of uncontrollable temperatures and improving therapeutic efficacy.

Colorimetric sensor to monitor the temperature with in situ synthesized silver nanoparticles embedded in carboxymethyl cellulose metallogel

A colorimetric sensing platform based on carboxymethyl cellulose-silver nanoparticles (CMC-AgNPs) metallogel was proposed to monitor the temperature history and quality of perishable products stored at low temperature. The carboxymethyl cellulose (CMC) metallogel, fabricated in the presence of Ag+, incorporates ionic crosslinking of biopolymers, complexation between reactants, and subsequent in situ synthesis of AgNPs, which are designed to lead the color transition of metallogel from colorless to dark brown depending on temperature and time. The mechanical and structural properties of metallogels and AgNPs were characterized by rheology, XRD, and FT-IR. Color changes associated with temperature, time, and metal ion precursors were examined by UV–visible spectroscopy and colorimetry. The longer the exposure time to thermal stress condition such as room temperature (25 °C) or high temperature (60 °C), the deeper the color of metallogel. The CMC-AgNPs metallogel-based sensor provides an efficient, safe way to track temperature history and assess perishable products’ quality, crucial for the safe distribution of food, vaccines, and medicines. This cost-effective and reliable visual sensor minimizes spoilage and health risks by enabling accurate temperature monitoring without the need for complicated equipment.

Utilizing a CNN-RNN machine learning approach for forecasting time-series outlet fluid temperature monitoring by long-term operation of BHEs system

The Borehole Heat Exchanger (BHE) plays a pivotal role in enhancing heat exchange efficiency within Ground Source Heat Pump (GSHP) systems. The accurate prediction of the BHE's outlet fluid temperature is crucial for optimizing GSHP performance, energy storage, and resource conservation. However, conventional machine learning methods encounter challenges in manual feature extraction, learning complex nonlinear relationships, and adapting to real-world scenarios. To address these limitations, this research proposes a crossbreed model integrating Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) architectures to forecast long-term outlet fluid temperature in BHE systems. The model framework encompasses data preprocessing, utilizing refined data in the CNN module for temporal feature extraction, subsequently passed to the RNN module to capture sequential and temporal patterns from each dataset. Specifically, the advanced CNN-RNN architecture is designed to establish a comprehensive input-output mapping, leveraging essential input features such as inlet fluid, ambient air, and subsurface temperatures at varying depths (0, 10, and 20 m). Performance evaluation metrics, including R2, RMSE, MAE, and AARE, are employed to compare and assess prediction accuracy across various models, including LSTM, CNN, and SimpleRNN. The obtained results demonstrate the superior performance of the proposed model, achieving an RSME of 0.818, MAE of 0.642, AARE of 0.0305, and an R2 value of 98.75 %. This surpasses the performance of traditional prediction models (LSTM, CNN, and SimpleRNN) by 3.01 %, 5.80 %, and 19.52 %, respectively. Notably, the remarkably low MAE of 0.642 exhibited by a CNN-RNN model underscores its capability to outperform traditional approaches, especially when handling large datasets. These findings emphasize the significance of the developed model in facilitating efficient operation, positioning it as a valuable tool for advancing the long-term sustainability of BHE systems.

Sagnac interferometer embedded with fiber Bragg grating for relative humidity and temperature sensing

In this paper, we first propose and demonstrate an ultra-compact fiber sensor consisting of fiber Bragg grating (FBG) and Sagnac loop interferometer with a specific taper-based coupling region. It is utilized for the relative humidity and temperature monitoring at the same time. The taper-based coupling region in the Sagnac loop can enhance the interaction between high-order propagating modes and the surrounding perturbations, therefore improving the fiber-optic sensitivity. The Sagnac interferometer demonstrates the humidity sensitivity of 89.4 p.m./%RH and the temperature sensitivity of −8.7 p.m./oC. FBG, the structure embedded in the Sagnac loop, shows the temperature sensitivity of 11.6 p.m./oC, while along with no feedback on the ambient relative humidity. It could be used as the reference component to demodulate the humidity information from the total reflection spectrum, finally realizing the simultaneous response of temperature and relative humidity.S