Search Results (611)

In this study, α-cellulose was extracted from lignocellulosic simpor leaf residue as a sustainable alternative to conventional cellulose sources. The extraction process involved the removal of hemicellulose, lignin, and other phytocompounds using alkali (NaOH) treatment and bleaching with hydrogen peroxide (H₂O₂). The nanocrystalline cellulose (NCC) was isolated from α-cellulose using sulfuric acid hydrolysis treatment followed by ultrasonication. The extracted α-cellulose and isolated NCC were characterized using Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), thermogravimetric analysis (TGA), and dynamic light scattering (DLS). The obtained results confirmed that the extracted NCC exhibited characteristic cellulose functional groups and a crystallinity index of 64.7%, indicating the effective removal of amorphous regions through sulfuric acid hydrolysis. The thermal stability of the extracted cellulose increased to 332 °C due to the elimination of extractives. DLS analysis showed that the extracted NCC exhibited high colloidal stability in polar solvents, characterized by a zeta potential of −70.8 mV and an average particle size of 251.7 nm. This study highlights an environmentally friendly approach for converting low-value biomass waste into high-value cellulose materials with potential applications in sustainable packaging, biomedical applications and composite reinforcement. Full article

This study’s field observations of Light Source Efficiency (LSE) in the Taklamakan Desert have unveiled significant daily average variations across different wavelengths, with LSE values ranging from 0.827 at 9.1 μm to a peak of 0.969 at 12.1 μm, and notably, a substantial daily variation (DV) of Δε = 0.080 in the 14.3 μm band. These findings underscore the necessity for wavelength-specific analysis in LSE research, which is crucial for enhancing the precision of remote sensing applications and climate models. This study’s high-temporal-resolution FTIR field observations systematically reveal the diurnal dynamics of infrared surface emissivity in the desert for the first time, challenging existing satellite-based inversion products and highlighting the limitations of traditional temperature–emissivity separation algorithms in arid regions. The diurnal fluctuations are governed by three primary mechanisms: the amplification of lattice vibrations in quartz minerals under high daytime temperatures, changes in the surface topography due to thermal expansion and contraction, and nocturnal radiative cooling effects. The lack of a significant correlation between environmental parameters and the emissivity change rate suggests that microclimate factors play a dominant indirect regulatory role. Model comparisons indicate that sinusoidal functions outperform polynomial fits across most wavelengths, especially at 12.1 μm, confirming the significant influence of diurnal forcing. The high sensitivity of the 14.3 μm band makes it an ideal indicator for monitoring desert surface–atmosphere interactions. This study provides three key insights for remote sensing applications: the development of dynamic emissivity correction schemes, the prioritization of the long-wave infrared band for surface temperature inversion in arid regions, and the integration of ground-based observations with geostationary high-spectral data to construct spatiotemporally continuous emissivity models. Future research should focus on multi-angle observation experiments and the exploration of machine learning’s potential in cross-scale emissivity modeling. Full article

Geothermal resources are crucial for sustainable energy development, yet accurately detecting geothermal anomalies in complex terrains remains a significant challenge. This study develops a multi-source thermal infrared approach to enhance geothermal anomaly detection using Landsat 8 and ASTER land surface temperature (LST) data. The Yangbajing–Yangyi Basin in Tibet, characterized by high altitude and rugged topography, serves as the study area. Landsat 8 winter time-series data from 2013 to 2023 were processed on the Google Earth Engine (GEE) platform to generate multi-year average LST images. After water body removal and altitude correction, a local block thresholding method was applied to extract daytime geothermal anomalies. For nighttime data, ASTER LST products were analyzed using global, local block, elevation zoning, and fault buffer strategies to extract anomalies, which were then fused using Dempster–Shafer (D–S) evidence theory. A joint daytime–nighttime analysis identified stable geothermal anomaly regions, with results closely aligning with known geothermal fields and borehole distributions while predicting new potential anomaly zones. Additionally, a 21-year time-series analysis of MODIS nighttime LST data identified four significant thermal anomaly areas, interpreted as potential magma chambers, whose spatial distributions align with the identified anomalies. This multi-source approach highlights the potential of integrating thermal infrared data for geothermal anomaly detection, providing valuable insights for exploration in geologically complex regions. Full article

Musculoskeletal diseases of the knee joint affect a large percentage of the population, particularly athletes at the competitive level where stress on the joints is higher. These conditions can be diagnosed and monitored using various imaging techniques, such as radiography, computed tomography, and magnetic resonance imaging. Additionally, digital infrared thermal imaging is gaining popularity for screening, diagnosis, and disease progression monitoring. This method measures the heat radiating from the superficial dermal microcirculation located 1–2 mm below the epidermal surface. Numerous pathological processes, such as inflammatory, metabolic, and toxic conditions, manifest as local changes in heat production, making infrared thermal imaging a valuable clinical tool. In the present study, the temperature of the knee area in 22 participants was monitored using an infrared camera while performing sit-to-stand cycles. The change in temperature correlated with weight distribution between the legs during exercise, measured using a Wii Balance Board. The results of this new trial protocol are promising and suggest that further investigations should be conducted with more patients. Infrared thermal imaging demonstrated consistency in repeated knee measurements and showed potential for evaluating the relationship between regional knee temperatures and pathological conditions. Its strengths lie in its simplicity, accuracy, non-invasive nature, radiation-free nature, and patient specificity, which can improve clinical management. In combination with other diagnostic techniques, thermography provides a comprehensive overview of patients’ clinical conditions. Full article

Background/Objectives: The pathophysiological mechanisms of temperature asymmetry differ between patients with warm and cold subtypes of Complex Regional Pain Syndrome (CRPS). Consequently, the response to lumbar sympathetic blocks (LSBs) and the resulting temperature improvement may vary between these two subtypes. We aimed to evaluate whether there was a significant difference in temperature elevation following sympathetic blocks in warm versus cold subtypes of CRPS. Methods: We calculated the temperature difference by analyzing forward-looking infrared (FLIR) thermal camera images of the affected extremity at pre-block and 5-min post-block time points. The primary outcome measure was that the mean temperature increase following LSB would be higher in the cold CRPS group than in the warm CRPS group. The secondary outcome measure was that the mean temperature elevation following the sympathetic block in the cold CRPS subtype would be at least 50% higher than in the warm CRPS subtype. Results: The study assessed warm and cold CRPS subtypes by analyzing temperature profiles from 90 lumbar sympathetic blocks performed on 34 patients. The temperature change in the affected extremity following LSB varied widely, with the highest increase observed in one patient at 10.99 °C. The cold CRPS patients demonstrated a higher mean temperature increase at the 5 min time point following LSB, averaging 3.37 °C in initial cases and 2.67 °C across all cases. In comparison, warm CRPS patients had lower mean increases of 0.58 °C in initial cases and 1.23 °C across all cases. Notably, the mean temperature rise in the cold CRPS group exceeded that of the warm CRPS group by more than 50%, meeting the secondary outcome goal. Conclusions: Our results indicated that patients with the cold subtype of CRPS tend to experience greater temperature improvements compared to those with the warm subtype after undergoing a sympathetic block. Therefore, our findings suggest that the criteria for determining the success of a sympathetic block should be revised to account for the cold and warm subtypes of CRPS. Full article

Emissions caused by polluting gases, such as carbon dioxide, are one of the main contributors to the generation of the greenhouse effect that leads to global warming, responsible for climate change. An alternative to mitigating these emissions is the use of adsorbents capable of capturing CO₂. Zeolites are considered one of the most effective adsorbents in gas adsorption and separation technologies due to their high specific area and pore size and, consequently, greater adsorption capacity when compared to other commonly used materials. Despite this, reagents used in syntheses as the source of silica often make obtaining these materials more expensive. Seeking to overcome this limitation, in this work, materials (for CO₂ capture) were developed with a zeolitic structure using a low-cost alternative source of silica from beach sand called MPI silica to make the synthesis process eco-friendly. The crystallization time of the materials was studied, obtaining an LTA zeolite with MPI silica in a period of 1 h (ZAM 1 h), with a relative crystallinity of 74.26%. The materials obtained were characterized using the techniques of X-ray diffraction (XRD), X-ray fluorescence (XRF), absorption spectroscopy in the infrared region with Fourier transform (FTIR), scanning electron microscopy (SEM), and thermal analysis. The evaluation of the experimental adsorption isotherms showed that the zeolite LTA Aerosil^®200 (standard zeolite) and MP had adsorption capacities of 5.25 mmol/g and 4.83 mmol/g of CO₂, respectively. The evaluation of mathematical models indicated that the LTA zeolites fit the Temkin model best and had the same trend, with calculated adsorption capacities of 3.97 mmol/g and 3.75 mmol/g, respectively. Full article

Advective heat fluxes (chimney effect) in porous debris facilitate ground cooling on scree slopes, even at low altitudes, and promote the occurrence of sporadic permafrost. The spatial distribution of ground surface temperature on an overcooled, low-altitude scree slope in the Romanian Carpathians was analyzed using UAV-based infrared thermography in different seasons. The analysis revealed significant temperature gradients within the scree slope, with colder, forest-insulated lower sections contrasting with warmer, solar-exposed upper regions. Across all surveyed seasons, this pattern remained evident, with the strongest temperature contrasts in December and April. February exhibited the most stable temperatures, with thermal readings primarily corresponding to snow surfaces rather than exposed rock. Rock surfaces displayed greater temperature variation than vent holes. Vent holes were generally cooler than rock surfaces, particularly in warmer periods. The persistent presence of ice and low temperatures at the end of the warm season suggested the potential existence of isolated permafrost. The results confirm the chimney effect, where cold air infiltrates the lower talus, gradually warms as it ascends, and outflows at higher elevations. UAV-based thermal imagery proved effective in detecting microclimatic variability and elucidating thermal processes governing talus slopes. This study provides valuable insights into extrazonal permafrost behavior, particularly in the context of global climate change. Full article

Monitoring the training load is crucial in sports science research, as it provides scientific evidence for assessing training effects, optimizing athletic performance, and preventing overtraining by quantifying both external and internal loads. Although traditional monitoring methods have made significant progress, infrared thermography (IRT) technology, with its non-contact, real-time, and non-invasive characteristics, is gradually emerging as an effective tool for evaluating the relationship between the training load and physiological responses. This study evaluated 31 healthy male adults (age 21.9 ± 2.7 years, weight 75 ± 8.26 kg, and training duration 240 ± 65 min/week) performing incremental exhaustive exercise on a cycle ergometer (with a 60W starting load, increasing by 20W per minute). Entropy analysis was used to quantitatively assess the surface radiation patterns of regions of interest (forehead, chest, and abdomen) obtained through thermal imaging. Compared to baseline, significant differences in the surface radiation patterns of the regions of interest were observed at the point of exhaustion (p ≤ 0.01). Correlation analysis revealed strong associations between the external load, oxygen consumption, and chest temperature entropy (r = 0.973 and 0.980). Cluster analysis of the chest entropy, external load, and oxygen consumption showed a non-linear increasing trend in their inter-relationships. Further individual analysis demonstrated positive correlations between the percentage increase in the chest entropy and both the external load (r = 0.70–0.98) and oxygen consumption (r = 0.65–0.97). Entropy analysis offers a new approach for quantitatively assessing surface radiation patterns from infrared thermography, and reveals the coupling relationship between thermoregulation and metabolic responses during exercise. Full article

The ice surface temperature (IST) derived from thermal infrared remote sensing is crucial for accurately monitoring ice or snow surface temperatures in the polar region. Generally, the remote sensing IST needs to be validated by the in situ IST to ensure its accuracy. However, due to the limited availability of in situ IST measurements, previous studies in the validation of remote sensing ISTs are scarce in the Antarctic ice sheet. This study utilizes ISTs from eight broadband radiation stations to assess the accuracy of the latest-released Moderate Resolution Imaging Spectroradiometer (MODIS) IST and Visible Infrared Imager Radiometer Suite (VIIRS) IST products, which were derived from two different algorithms, the Split-Window (SW-based) algorithm and the Temperature–Emissivity Separation (TES-based) algorithm, respectively. This study also explores the sources of uncertainty in the validation process. The results reveal prominent errors when directly validating remote sensing ISTs with the in situ ISTs, which can be attributed to incorrect cloud detection due to the similar spectral characteristics of cloud and snow. Hence, cloud pixels are misclassified as clear pixels in the satellite cloud mask during IST validation, which emphasizes the severe cloud contamination of remote sensing IST products. By using a cloud index (n) to remove the cloud contamination pixels in the remote sensing IST products, the overall uncertainties for the four products are about 2 to 3 K, with the maximum uncertainty (RMSE) reduced by 3.51 K and the bias decreased by 1.26 K. Furthermore, a progressive cold bias in the validation process was observed with decreasing temperature, likely due to atmospheric radiation between the radiometer and the snow surface being neglected in previous studies. Lastly, this study found that the cloud mask errors of satellites are more pronounced during the winter compared to that in summer, highlighting the need for caution when directly using remote sensing IST products, particularly during the polar night. Full article

This study proposes a method for estimating the junction temperature of power semiconductors, particularly IGBTs (Insulated Gate Bipolar Transistors) and diodes. Traditional temperature measurement methods using NTC (Negative Temperature Coefficient) sensors have limitations in reflecting dynamic conditions in real time, as temperature changes take time to reach the sensors. To address this, this study proposes a junction temperature estimation method using RC curve fitting and a thermal impedance model. This model represents the thermal behavior of IGBTs and diodes using a Foster thermal network that considers the resistance and capacitance of the heat transfer path. In particular, transient temperature estimation considering thermal coupling enables the prediction of temperature changes in IGBTs and diodes. To verify the proposed temperature estimation method, experiments were conducted to build the model based on data measured with an infrared thermal camera and NTC sensors. The model’s estimated results were compared with actual values across 25 operating regions, achieving a maximum MAE (Mean Absolute Error) of 2.26 °C. A comparative analysis of first-, second-, third-, and fourth-order Foster networks revealed that, while higher orders improve accuracy, gains beyond the second order are minimal relative to computational demands. This study contributes to enhancing not only the reliability of power semiconductor modules but also minimizing the temperature margin for inverters by estimating the junction temperature with better dynamic performance than that achieved by NTC sensors. Full article

In this research, we investigated the influence of Mn²⁺ ions on the packing in stearic acid (SA) crystals through the use of Raman spectroscopy, X-ray diffraction (XRD), and Fourier transform infrared (FT-IR) spectroscopy. The crystals investigated were obtained utilizing the slow evaporation methodology in a hexane solution under varying manganese (Mn) concentrations sourced from MnSO₄ 5H₂O (0.5, 1.0, 1.5, 2.0, 4.0, and 6.0%). XRD studies indicated that all SA crystals were grown in the B_m form (monoclinic), favoring the gauche conformation in molecular packing. Additionally, crystalline lattice modifications were observed through Raman spectral changes in the low-vibrational energy region. Variations in the intensities and Raman shifts in two lattice vibrational modes, centered at approximately 59 and 70 cm⁻¹, revealed that two types of hydrogen bonds are distinctly affected within the crystalline lattice. Furthermore, the unit cell parameters (a, b, c, and β) were determined via Rietveld refinement, and their behavior was analyzed as a function of Mn concentration. The results indicated that Mn²⁺ ions exert a strain and deformation effect on the unit cell. Lastly, differential scanning calorimetry (DSC) was employed to evaluate the thermal stability of the B_m form of SA crystals. Full article

Soil moisture (SM) monitoring in farmland at a regional scale is crucial for precision irrigation management and ensuring food security. However, existing methods for SM estimation encounter significant challenges related to accuracy, generalizability, and automation. This study proposes an integrated data fusion method to systematically assess the potential of three automated machine learning (AutoML) frameworks—tree-based pipeline optimization tool (TPOT), AutoGluon, and H2O AutoML—in retrieving SM. To evaluate the impact of input variables on estimation accuracy, six input scenarios were designed: multispectral data (MS), thermal infrared data (TIR), MS combined with TIR, MS with auxiliary data, TIR with auxiliary data, and a comprehensive combination of MS, TIR, and auxiliary data. The research was conducted in a winter wheat cultivation area within the People’s Victory Canal Irrigation Area, focusing on the 0–40 cm soil layer. The results revealed that the scenario incorporating all data types (MS + TIR + auxiliary) achieved the highest retrieval accuracy. Under this scenario, all three AutoML frameworks demonstrated optimal performance. AutoGluon demonstrated superior performance in most scenarios, particularly excelling in the MS + TIR + auxiliary data scenario. It achieved the highest retrieval accuracy with a Pearson correlation coefficient (R) value of 0.822, root mean square error (RMSE) of 0.038 cm³/cm³, and relative root mean square error (RRMSE) of 16.46%. This study underscores the critical role of input data types and fusion strategies in enhancing SM estimation accuracy and highlights the significant advantages of AutoML frameworks for regional-scale SM retrieval. The findings offer a robust technical foundation and theoretical guidance for advancing precision irrigation management and efficient SM monitoring. Full article

This study addresses the challenge of monitoring oxide layer formation in 1045 steel, a critical issue affecting mechanical properties and phase stability during high-temperature processes (900 °C). To tackle this, an image processing algorithm was developed to detect and segment regions of interest (ROIs) in oxidized steel surfaces, utilizing infrared thermography as a non-contact, real-time measurement technique. Controlled heating experiments ensured standardized data acquisition, and the algorithm demonstrated exceptional accuracy with performance metrics such as 96% accuracy and a Dice coefficient of 96.15%. These results underscore the algorithm’s capability to monitor oxide scale formation, directly impacting surface quality, thermal uniformity, and material integrity. The integration of thermography with machine learning techniques enhances steel manufacturing processes by enabling precise interventions, reducing material losses, and improving product quality. This work highlights the potential of advanced monitoring systems to address challenges in industrial steel production and contribute to the sustainability of advanced steel materials. Full article

International efforts are being made to reduce environmental pollution caused by microplastics (MPs). Microplastics are released into the environment through sewage treatment sludge, and the use of sludge as a soil improvement agent is increasing rapidly, emphasising the importance of controlling microplastics in sewage treatment facilities. The release of microplastics into the environment is an increasingly significant concern, with sources including sewage treatment sludge. This study focuses on the analysis of microplastics in sewage sludge using optical (Fourier-transform infrared spectroscopy, FTIR) and thermal (Thermo Extraction Desorption–Gas Chromatograph–Mass Spectroscopy, TED-GC-MS) processing-based analytical equipment. The average amount of MPs in the sewage sludge analysed using FTIR was 228.5 microplastics/g of sludge (MPs/g), primarily of the polypropylene type. Approximately 75% of the MPs were 0.1 mm in size or smaller. However, the average amount of MPs in the sewage sludge determined using TED-GC-MS was 95.79 µg-MPs/g. For the systematic management of microplastics, it is important to estimate the amount of microplastics generated by sewage treatment plants. Therefore, a microplastic generation calculation formula was proposed and used to estimate the potential microplastic generation in sewage treatment plants. The total amount of MPs generated from sewage treatment plants in South Korea, calculated using the equation, was approximately 364 ton/yr; we further divided the total amount by administrative regions. The findings of this study can be applied to assess global trends in MP research. Full article

This study investigates the innovative use of natural porous clays from the Bejaad Region in Morocco as a support matrix for the encapsulation and controlled release of lemon essential oil (Citrus limonum, EOCL), a natural compound with well-documented insecticidal properties. The research aims to address the inherent challenges of essential oils, particularly their high volatility and rapid degradation, by improving their stability and insecticidal efficiency against the grain pest Sitophilus granarius. By anchoring EOCL onto clay matrices, this study seeks to achieve a sustained and controlled release of the active components, thereby enhancing their practical application as biopesticides. The clays were comprehensively characterized using advanced analytical techniques, including X-ray diffraction (XRD), X-ray fluorescence (XRF), Fourier-transform infrared spectroscopy (FT-IR), scanning electron microscopy with energy-dispersive X-ray analysis (SEM-EDX), and thermogravimetric analysis (TGA). These techniques revealed the mineralogical composition, thermal properties, and morphology of the clays, demonstrating their suitability for effectively adsorbing and retaining EOCL. The insecticidal performance of the clay/EOCL composites was thoroughly tested under controlled conditions, revealing a marked improvement in efficacy, with significantly lower lethal doses required to achieve high mortality rates in Sitophilus granarius. The diffusion of EOCL through the clay matrix was modeled using Fick’s law of diffusion, and the results were further refined through statistical optimization to identify key parameters that influence the release and effectiveness of the active compounds. Complementing the experimental approach, a bioinformatics analysis was conducted to explore the molecular interactions between limonene, the primary active component of EOCL, and target proteins in insects. This theoretical investigation provided insights into the potential mechanisms of action, reinforcing the empirical findings. This study concludes that encapsulating EOCL within porous clay matrices not only enhances the stability and controlled release of the oil but also significantly boosts its insecticidal effectiveness. This approach presents a promising, environmentally sustainable strategy for crop protection, integrating material science, theoretical modeling, and bioinformatics to develop more efficient and durable biopesticides. Full article