Search Results (2,583)

Radiative cooling (RC) offers a passive pathway to reduce surface and system temperatures by emitting thermal radiation through the atmospheric window, yet its daytime effectiveness is often constrained by geometry, angular solar exposure, and practical integration limits. This work experimentally investigates the use of passive non-imaging optics, specifically compound parabolic concentrators (CPCs), as enhancers of RC performance under realistic conditions. A three-tier experimental methodology is followed. First, controlled indoor screening using an infrared lamp quantifies the intrinsic heat gain suppression of a commercial RC film, showing a temperature reduction of nearly 88 °C relative to a black-painted reference. Second, outdoor rooftop experiments on aluminum plates assess partial RC coverage, with and without CPCs, under varying orientations and tilt angles, revealing peak daytime temperature reductions close to 8 °C when CPCs are integrated. Third, system-level validation is conducted using a modified GUNT ET-202 solar thermal unit to evaluate the transfer of RC effects to a water circuit absorber. While RC strips alone produce modest reductions in water temperature, the addition of CPC optics amplifies the effect by factors of approximately three for ambient water and nine for water at 70 °C. Across all configurations, statistical analysis confirms stable, repeatable measurements. These results demonstrate that coupling commercially available RC materials with non-imaging optics provides consistent and measurable performance gains, supporting CPC-assisted RC as a scalable and retrofit-friendly strategy for urban and building energy applications while calling for longer-term experiments, durability assessments, and techno-economic analysis before deriving definitive deployment guidelines. Full article

The scale and magnitude of urban heating are often assessed using Satellite-Derived Land Surface Temperature (SD-LST). Yet, discrepancies in spatial resolution limit SD-LST’s ability to reflect pedestrian thermal experience, potentially leading to ineffective mitigation strategies. Hyper-local measurements of urban heat, defined as surface temperatures (T_S) at the scale of pedestrian activity (e.g., bus stops or street segments), may provide more accurate insights into thermal comfort. This study compares hyper-local ~0.01 m resolution T_S collected via consumer-grade Forward-Looking Infrared (FLIR) thermography with resampled 30 m resolution SD-LST from Landsat 8 and 9 images to evaluate their utility in predicting thermal comfort indices across 60 bus stops in Denver, Colorado. During the summer of 2023, 270 FLIR measurements were collected over 19 dates, with a four-day subset (n = 33) coinciding with Landsat imagery. FLIR T_S averaged 25.12 ± 5.39 °C, while SD-LST averaged 35.90 ± 12.56 °C, a significant 10.77 °C difference (95% CI: 6.81–14.73; p < 0.001). FLIR T_S strongly correlated with biometeorological metrics such as air temperature and mean radiant temperature (r > 0.8; p < 0.001), while SD-LST correlations were weak (r < 0.3). Linear mixed-effects models using FLIR T_S explained 50–66% of the variance in thermal comfort indices and met ISO 7726 standards. Each 1 °C increase in FLIR TS predicted a 0.75 °C rise in mean radiant temperature. These results highlight hyper-local thermography as a reliable, low-cost tool for urban heat resilience planning. Full article

The combination of gold nanoparticles (AuNPs) with hydrogels has drawn significant interest in the design of smart materials as advanced platforms for biomedical applications. These systems endow light-responsiveness enabled by the AuNPs localized surface plasmon resonance (LSPR) phenomenon. In this study, we propose a nanocomposite hydrogel in which gold nanorods (AuNRs) are included in an agarose–carbomer–hyaluronic acid (AC-HA)-based hydrogel matrix to study the correlation between light irradiation, local temperature increase, and drug release for potential light-assisted drug delivery applications. The gel is obtained through a facile microwave-assisted polycondensation reaction, and its properties are investigated as a function of both the hyaluronic acid molecular weight and ratio. Afterwards, AuNRs are incorporated in the AC-HA formulation, before the sol–gel transition, to impart light-responsiveness and optical properties to the otherwise inert polymeric matrix. Particular attention is given to the evaluation of AuNRs/AC-HA light-induced heat generation and drug delivery performances under near-infrared (NIR) laser irradiation in vitro. Spatiotemporal thermal profiles and high-resolution thermal maps are registered using fiber Bragg grating (FBG) sensor arrays, enabling accurate probing of maximum internal temperature variations within the composite matrix. Lastly, using a high-steric-hindrance protein (BSA) as a drug mimetic, we demonstrate that moderate localized heating under short-time repeated NIR exposure enhances the release from the nanocomposite hydrogel. Full article

Early diagnosis of heat load in beef cattle remains a challenge due to the limited understanding of behaviour-based indicators. This preliminary longitudinal study aimed to validate behavioural and physiological responses previously identified as heat load indicators. Black Angus steers were exposed to high environmental temperatures expected to cause heat load in the following sequence: an initial thermoneutral period, a hot period, and a recovery period. Changes in the positioning of key body parts, feeding behaviour, body maintenance, respiratory dynamics, and eye temperature were monitored. In the hot period, cattle increased their respiration rate, panting, and infrared eye temperature. Increased stepping by their left limbs suggested involvement of the right brain hemisphere in a stress response to high environmental temperatures. Cattle also held their heads more downward, ears backward, and their tail vertical, and reduced eating, grooming, and scratching during the hot period. Cattle responses to hot conditions were persistent in the recovery period, reflecting diagnostic relevance of the head, ear, and tail movements, stepping, especially by left limbs, and infrared eye temperature as non-invasive tools to identify heat load condition in cattle. The study reinforces our understanding of the specific behavioural and physiological responses to heat load condition, especially those involving left-limb stepping, ear and tail posture, and infrared eye temperature, are reliable indicators for identifying cattle experiencing high environmental temperature. Full article

Topaz is one of the most economically important fluorine-rich nesosilicates, which are predominantly colorless in natural crystals. Hence, the trade relies almost entirely on irradiated blue topaz with an unstable color center, which has been shown to fade over time. The cobalt (Co) diffusion treatment is a stable alternative process for converting colorless topaz to blue by a solid-state diffusion mechanism. To investigate the potential role of Co²⁺ substitution in the formation of the blue layer and the coupled behavior of F/OH dehydroxylation in facilitating this process, systematic diffusion treatments have been successfully conducted and compared. In this study, gem-quality topazes were annealed in air at 1000 °C for 20–40 h (hr) along with CoO, Fe₂O₃, Cr₂O₃, and CuO powders. The diffused products were characterized using Scanning Electron Microscope (SEM), Ultraviolet-Visible absorption spectroscopy (UV-Vis), Near-Mid Infrared spectroscopy (NMIR), and X-ray photoelectron spectroscopy (XPS). Parallel runs with CuO, Fe₂O₃, or Cr₂O₃ alone confirmed that none of these oxides produces a stable blue layer, underscoring the unique role of Co. The Co-diffused sample displays an intense blue layer characterized by a Co²⁺ octahedral isomorphism triplet at 540, 580, and 630 nm, which are absent from both untreated and heat-only controls. XPS analysis reveals the emergence of Co²⁺ (binding energy: 780.63 eV) and a concomitant depletion in F⁻, along with the disappearance of the OH overtone absorption at 7123 cm⁻¹. These observations confirm that defluorination generates octahedral vacancies accommodated by the coupled substitution: CoF₂ (solid reactant) + (AlO₂)⁻ (fragment of topaz structure) → AlOF (solid product) + (CoOF)⁻ (fragment of topaz structure). Prolonged annealing leads to decreased relative atomic percentages of K⁺ and F⁻ ions, consistent with volatilization losses during the high-temperature process, thereby directly correlating color intensity with cobalt valence state, which transfers from Co²⁺ to Co³⁺. These findings establish a Co-incorporation chronometer for F–rich aluminosilicate systems, with an optimal annealing time of approximately 20 hr at 1000 °C. Furthermore, the above results demonstrate that the color mechanism in nesosilicate gems is simultaneously governed by volatile release and cation availability. Full article

In this study, a three-dimensional Inverse Conjugate Heat Transfer Problem (ICHTP) is numerically and experimentally investigated to estimate the heat-source strength of multiple chips mounted on a printed circuit board (PCB) using the Conjugate Gradient Method (CGM) and infrared thermography. The interfaces between the PCB and the surrounding air domain are assumed to exhibit perfect thermal contact, establishing a fully coupled conjugate heat transfer framework for the inverse analysis. Unlike the conventional Inverse Heat Conduction Problem (IHCP), which typically only accounts for conduction within solid domains, the present ICHTP formulation requires the simultaneous solution of the governing continuity, momentum, and energy equations in the air domain, along with the heat conduction equation in the chips and PCB. This coupling introduces substantial computational complexity due to the nonlinear interaction between convective and conductive heat transfer mechanisms, as well as the sensitivity of the inverse solution to measurement uncertainties. The numerical simulations are conducted first with error-free measurement data and an inlet velocity of u_in = 4 m/s; the recovered heat-sources exhibit excellent agreement with the true values. The computed average errors for the estimated temperatures ERR1 and estimated heat sources ERR2 are as low as 0.0031% and 1.87%, respectively. The accuracy of the estimated heat sources is then experimentally validated under various prescribed inlet air velocities. During experimental verification at an inlet velocity of 4 m/s, the corresponding ERR1 and ERR2 values are obtained as 0.91% and 3.34%, while at 6 m/s, the values are 0.86% and 2.81%, respectively. Compared with the numerical results, the accuracy of the experimental estimations decreases noticeably. This discrepancy arises because the numerical simulations are free from measurement noise, whereas experimental data inherently include uncertainties due to thermal picture resolutions, environmental fluctuations, and other uncontrollable factors. These results highlight the inherent challenges associated with inverse problems and underscore the critical importance of obtaining precise and reliable temperature measurements to ensure accurate heat source estimation. Full article

Infrared thermal field (ITF) prediction for meter boxes is crucial for the early warning of power system faults, yet this method faces three major challenges: data sparsity, complex geometry, and resource constraints in edge computing. Existing physics-informed neural network-graph neural network (PINN-GNN) approaches suffer from redundant physics residual calculations (over 70% of flat regions contain little information) and poor model generalization (requiring retraining for new box types), making them inefficient for deployment on edge devices. This paper proposes the PhysGraphIR framework, which employs an Adaptive Residual Sampling (ARS) mechanism to dynamically identify hotspot region nodes through a physics-aware gating network, calculating physics residuals only at critical nodes to reduce computational overhead by over 80%. In this study, a ‘hotspot region’ is explicitly defined as a localized area exhibiting significant temperature elevation relative to the background—typically concentrated around electrical connection terminals or wire entrances—which is critical for identifying potential thermal faults under sparse data conditions. Additionally, it utilizes a Physics Knowledge Distillation Graph Neural Network (Physics-KD GNN) to decouple physics learning from geometric learning, transferring universal heat conduction knowledge to specific meter box geometries through a teacher–student architecture. Experimental results demonstrate that on both synthetic and real-world meter box datasets, PhysGraphIR achieves a hotspot region mean absolute error (MAE) of 11.8 °C under 60% infrared data missing conditions, representing a 22% improvement over traditional PINN-GNN. The training speed is accelerated by 3.1 times, requiring only five infrared samples to adapt to new box types. The experiments prove that this method significantly enhances prediction accuracy and computational efficiency under sparse infrared data while maintaining physical consistency, providing a feasible solution for edge intelligence in power systems. Full article

Non-destructive estimation of high-temperature stress effects on tobacco plants is crucial for both scientific research and practical applications. Normalized difference vegetation index (NDVI), chlorophyll index, and spectra in the range of 900–1700 nm of Burley, Oriental, and Virginia tobacco plants under control and high-temperature stress conditions were measured using portable instruments. NDVI and chlorophyll index measurements indicate that young leaves of all tobacco types are tolerant to high temperatures. In contrast, the older leaves (the fifth leaf) showed increased sensitivity to heat stress. The chlorophyll content of these leaves decreased by 40 to 60% after five days of stress, and by the seventh day, the reduction reached 80% or more in all plants. The vegetative index of the fifth leaf also decreased on the seventh day of stress in all tobacco types. Differences in near-infrared spectra were observed between control, stressed, and recovered plants, as well as among different stress days, and among tobacco lines. The most significant differences were in the 1300–1500 nm range. The first characterization of heat-induced changes in the molecular structure of water in tobacco leaves using an aquaphotomics approach was conducted. Models for determining days of high-temperature treatment based on near-infrared spectra achieved a standard error of cross-validation (SECV) from 0.49 to 0.62 days. The total accuracy of the Soft Independent Modeling of Class Analogy (SIMCA) classification models of control, stressed, and recovered plants ranged from 91.0 to 93.6% using leaves’ spectra of the first five days of high-temperature stress, and from 90.7 to 97.7% using spectra of only the fifth leaf. Similar accuracy was obtained using Partial Least Squares–Discriminant Analysis (PLS-DA). Near-infrared spectroscopy and aquaphotomics can be used as a fast and non-destructive approach for early detection of stress and additional tools for investigating high-temperature tolerance in tobacco plants. Full article

This study utilized capric acid (CA), lauric acid (LA), myristic acid (MA), palmitic acid (PA), and stearic acid (SA) as alternative feedstocks to conduct theoretical analyses on ten fatty acid-based ternary eutectic systems. By leveraging the Schrader equation, phase diagrams for each system were constructed, and their theoretical eutectic points were calculated. The CA-LA-MA (capric acid–lauric acid–myristic acid) ternary system was selected as a representative for experimental fabrication: differential scanning calorimetry (DSC) was employed to characterize its thermal properties, while Fourier transform infrared spectroscopy (FT-IR) and thermogravimetric analysis (TGA) were used to assess its functional group composition and thermal stability, respectively. Theoretical calculations indicate that the ten ternary eutectic systems exhibit melting temperatures ranging from 17.11 °C to 37.61 °C, with phase change latent heats spanning 167.8 J·g⁻¹ to 189.6 J·g⁻¹. For the CA-LA-MA system, experimental DSC results confirm that its eutectic melting temperature is 16.0 °C (accompanied by a phase change latent heat of 177.0 J·g⁻¹, with minor deviations from theoretical predictions attributed to reagent impurities and operational errors). TGA characterization further reveals that the CA-LA-MA mixture has an initial weight loss temperature (corresponding to ~1% mass loss) of 115.6 °C and an extrapolated onset weight loss temperature of 164.8 °C, confirming reliable thermal stability below 100 °C—consistent with its low-temperature application design. These results validate the consistency between theoretical predictions and experimental data, and demonstrate that fatty acid-based ternary eutectic mixtures are promising candidates for low-temperature thermal energy storage applications. Full article

Land Surface Temperature (LST) plays a key role in climate monitoring, urban heat assessment, and land–atmosphere interactions. However, current thermal infrared satellite sensors cannot simultaneously achieve high spatial and temporal resolution. Spatio-temporal fusion (STF) techniques address this limitation by combining complementary satellite data, one with high spatial but low temporal resolution, and another with high temporal but low spatial resolution. Existing STF techniques, from classical models to modern deep learning (DL) architectures, were primarily developed for surface reflectance (SR). Their application to thermal data remains limited and often overlooks LST-specific spatial and temporal variability. This study provides a focused review of DL-based STF methods for LST. We present a formal mathematical definition of the thermal fusion task, propose a refined taxonomy of relevant DL methods, and analyze the modifications required when adapting SR-oriented models to LST. To support reproducibility and benchmarking, we introduce a new dataset comprising 51 Terra MODIS-Landsat LST pairs from 2013 to 2024, and evaluate representative models to explore their behavior on thermal data. Full article

The study presents the thermal evaluation of soft knitted textile systems with removable gel cooling panels. Two prototype configurations with different geometries and gel panel sizes were investigated using infrared thermography under controlled laboratory conditions. The results show a moderated and gradual cooling response during contact. The strongest surface cooling occurred shortly after contact, followed by a gradual increase in the surface temperature of the textile system due to heat transfer from the skin-temperature simulator. While the temperature of the skin-temperature simulator stabilised rapidly, the textile surface maintained a perceptible cooling effect over a longer period. Surface temperatures remained within ranges associated with comfort and safety under the applied experimental conditions. The findings indicate that system geometry and gel panel size influence heat exchange, while the knitted textile structure contributes to the observed cooling behaviour of the complete system. The results support the potential of knitted textile systems with removable gel cooling panels for gentle, localised cooling applications in controlled, non-clinical settings. Full article

Far-infrared (FIR)-emitting textiles are increasingly used in sleepwear; however, their influence on sleep physiology has not been comprehensively evaluated with multi-modal wearable sensing. This randomized, double-blind, placebo-controlled crossover study examined whether FIR-emitting garments modulate nocturnal thermoregulation, autonomic activity, and sleep architecture. Fifteen healthy young men completed two overnight laboratory sleep sessions wearing either FIR-emitting garments or visually matched polyester controls. Tympanic membrane temperature (TMT), sweating rate, skin temperature, and humidity were continuously monitored using wearable sensors, and sleep stages and heart rate variability (HRV) were assessed using validated portable systems. Compared with control garments, FIR garments produced consistently lower TMT across the night (p = 0.004) and reduced mid-sleep sweating (condition × time interaction: p = 0.026). The proportion of rapid eye movement (REM) sleep was higher in the FIR condition (22.2% ± 6.5% vs. 18.6% ± 6.5%, p = 0.027), despite no changes in total sleep time or sleep efficiency. A transient increase in low-frequency power during early sleep (p = 0.027) suggested baroreflex-related thermal adjustments without sympathetic activation. These findings indicate that FIR-emitting garments facilitate mild nocturnal heat dissipation and support REM expression, demonstrating their potential as a passive intervention to improve sleep-related thermal environments. Full article

The built environment is responsible for 40% of global energy demand, and, in line with urbanisation and population growth, this demand is expected to increase steadily. Urban areas are mostly composed of materials that can absorb energy from solar radiation and dissipate the accumulated energy in the form of heat. This study integrates a UAV-based Zenmuse XT S IR camera and handheld FLIR C5 thermal camera with ENVI-met microclimate simulation, providing quantitative insights for sustainable urban planning. From the 24 h experiment results, the characteristics of building surface materials are profiled for lowering energy use for internal thermal control during the operation stage of buildings. This study shows that building surface materials with the lowest solar reflectance and highest specific heat capacity reached a peak surface temperature of 73.5 °C in Jakarta (tropical hot climate) and 44.3 °C in Xiamen (subtropical late winter climate). In contrast, materials with the highest solar reflectance and lowest specific heat only reach a peak surface temperature of 58.1 °C in Jakarta and 27.9 °C in Xiamen. The peak surface temperature occurs at 2 PM in the afternoon. Moreover, we demonstrate the capability of an infrared drone to identify the peak surface temperatures of 55.8 °C at 2 PM in the study area in Xiamen. In addition, the ENVI-met validated model shows satisfactory correlation values of R > 0.9 and R2 > 0.8. This result demonstrates UAV-IR and ENVI-met simulation integration as a scalable method for city-level UHI diagnostics and monitoring. Full article

Infrared and visible image fusion (IVIF) integrates the thermal saliency of infrared images (IRs) with the structural details of visible images (VIs) to produce comprehensive scene representations. Existing methods often overemphasize one modality, leading to loss of temperature readability or visual details. To address this, we propose Wave-Cross, a wavelet-based fusion framework. Using the discrete wavelet transform (DWT), IR low-frequency sub-bands encode thermal distribution, while VI high-frequency sub-bands capture textural details. Cross-attention adaptively recombines these sub-bands, suppressing modality-specific noise and balancing complementary features. Additionally, we introduce a Heat-Consistency Loss, which enforces pixel-wise thermal ordering and local energy preservation in a self-supervised manner, ensuring the fused image retains IR interpretability while enhancing VI sharpness. Experiments on the TNO, MSRS, and M3FD datasets demonstrate the effectiveness of the proposed method. Compared with state-of-the-art baselines, Wave-Cross achieves superior performance on objective metrics such as SD, AG, SCD, SF, CC, EN, NABF, and MS-SSIM yielding clearer details and more stable thermal saliency under challenging interference conditions. These results highlight the framework’s potential for practical applications in surveillance, autonomous driving, and fault diagnosis. Full article

CO₂ laser processing has emerged as an efficient dry-finishing technique capable of inducing controlled chemical and morphological transformations in cotton and denim textiles. The strong mid-infrared absorption of cellulose enables localised photothermal heating, leading to selective dye decomposition, surface oxidation, and micro-scale ablation while largely preserving the bulk fabric structure. These laser-driven mechanisms modify colour, surface chemistry, and topography in a predictable, parameter-dependent manner. Low-fluence conditions predominantly produce uniform fading through fragmentation and oxidation of indigo dye; in comparison, moderate thermal loads promote the formation of carbonyl and carboxyl groups that increase surface energy and enhance wettability. Higher fluence regimes generate micro-textured regions with increased roughness and anchoring capacity, enabling improved adhesion of dyes, coatings, and nanoparticles. Compared with conventional wet processes, CO₂ laser treatment eliminates chemical effluents, strongly reduces water consumption and supports digitally controlled, Industry 4.0-compatible manufacturing workflows. Despite its advantages, challenges remain in standardising processing parameters, quantifying oxidation depth, modelling thermal behaviour, and assessing the long-term stability of functionalised surfaces under real usage conditions. In this review, we consolidate current knowledge on the mechanistic pathways, processing windows, and functional potential of CO₂ laser-modified cotton substrates. By integrating findings from recent studies and identifying critical research gaps, the review supports the development of predictable, scalable, and sustainable laser-based cotton textile processing technologies. Full article