Search Results (239)

A simple and fast analytical method was developed and applied to assess the effect of two forms of zinc fertilization on a pecan tree cultivar in Uruguay: fertigation and foliar application with a specially formulated fertilizer. Zinc content was determined in 36 leaf samples from two crop cycles: 2020–2021 and 2021–2022. Fresh samples were dried, ground, and sieved. Analytical determinations were performed by flame atomic absorption spectrometry (FAAS, considered a standard method) and energy dispersive X-ray spectrometry (EDXRF, the proposed method). In the first case, sample preparation was carried out by microwave-assisted digestion using 4.5 mol L⁻¹ HNO₃. In the second case, pellets (Φ 13 mm, 2–3 mm thick) were prepared by direct mechanical pressing. Figures of merit of both methodologies were adequate for the purpose of zinc monitoring. The results obtained from both methodologies were statistically compared and found to be equivalent (95% confidence level). Based on the principles of Green Analytical Chemistry, both procedures were evaluated using the Analytical Greenness Metric Approach (AGREE and AGREEprep) tools. It was concluded that EDXRF was notably greener than FAAS and can be postulated as an alternative to the standard method. The information emerging from the analyses aided decision-making at the agronomic level. Full article

The intensification of olive growing has raised environmental concerns, particularly regarding nutrient loss from excessive fertiliser use. In line with the European Union’s Farm to Fork strategy, which aims to halve the soil nutrient losses by 2030, this study evaluates the effectiveness of two sensor-based approaches—proximal sensing with a FLAME spectrometer and remote sensing via UAV-mounted multispectral imaging—compared with foliar chemical analyses as the reference standard, for diagnosing the nutritional status of olive trees. The research was conducted in Elvas, Portugal, between 2022 and 2023, across three olive cultivars (‘Azeiteira’, ‘Arbequina’, and ‘Koroneiki’) subjected to different fertilisation regimes. Machine learning (ML) models showed strong correlations between sensor data and nutrient levels: the multispectral sensor performed best for phosphorus (P) (determination coefficient [$R^2$] = 0.75) and potassium (K) ($R^2$ = 0.73), while the FLAME spectrometer was more accurate for nitrogen (N) ($R^2$ = 0.64). These findings underscore the potential of sensor-based technologies for non-destructive, real-time nutrient monitoring, with each sensor offering specific strengths depending on the target nutrient. This work contributes to more sustainable and data-driven fertilisation strategies in precision agriculture. Full article

Timely and accurate detection of forest fires is crucial for protecting forest ecosystems. However, traditional monitoring methods face significant challenges in effectively detecting forest fires, primarily due to the dynamic spread of flames and smoke, irregular morphologies, and the semi-transparent nature of smoke, which make it extremely difficult to extract key visual features. Additionally, deploying these detection systems to edge devices with limited computational resources remains challenging. To address these issues, this paper proposes a lightweight hybrid receptive field model (LHRF-YOLO), which leverages deep learning to overcome the shortcomings of traditional monitoring methods for fire detection on edge devices. Firstly, a hybrid receptive field extraction module is designed by integrating the 2D selective scan mechanism with a residual multi-branch structure. This significantly enhances the model’s contextual understanding of the entire image scene while maintaining low computational complexity. Second, a dynamic enhanced downsampling module is proposed, which employs feature reorganization and channel-wise dynamic weighting strategies to minimize the loss of critical details, such as fine smoke textures, while reducing image resolution. Furthermore, a scale weighted Fusion module is introduced to optimize multi-scale feature fusion through adaptive weight allocation, addressing the issues of information dilution and imbalance caused by traditional fusion methods. Finally, the Mish activation function replaces the SiLU activation function to improve the model’s ability to capture flame edges and faint smoke textures. Experimental results on the self-constructed Fire-SmokeDataset demonstrate that LHRF-YOLO achieves significant model compression while further improving accuracy compared to the baseline model YOLOv11. The parameter count is reduced to only 2.25M (a 12.8% reduction), computational complexity to 5.4 GFLOPs (a 14.3% decrease), and mAP50 is increased to 87.6%, surpassing the baseline model. Additionally, LHRF-YOLO exhibits leading generalization performance on the cross-scenario M4SFWD dataset. The proposed method balances performance and resource efficiency, providing a feasible solution for real-time and efficient fire detection on resource-constrained edge devices with significant research value. Full article

Wildfire detection in power transmission corridors is essential for providing timely warnings and ensuring the safe and stable operation of power lines. However, this task faces significant challenges due to the large number of smoke-like samples in the background, the complex and diverse target morphologies, and the difficulty of detecting small-scale smoke and flame objects. To address these issues, this paper proposed an improved Oriented R-CNN model enhanced with metric learning for wildfire detection in power transmission corridors. Specifically, a multi-center metric loss (MCM-Loss) module based on metric learning was introduced to enhance the model’s ability to differentiate features of similar targets, thereby improving the recognition accuracy in the presence of interference. Experimental results showed that the introduction of the MCM-Loss module increased the average precision (AP) for smoke targets by 2.7%. In addition, the group convolution-based network ResNeXt was adopted to replace the original backbone network ResNet, broadening the channel dimensions of the feature extraction network and enhancing the model’s capability to detect flame and smoke targets with diverse morphologies. This substitution led to a 0.6% improvement in mean average precision (mAP). Furthermore, an FPN-CARAFE module was designed by incorporating the content-aware up-sampling operator CARAFE, which improved multi-scale feature representation and significantly boosted performance in detecting small targets. In particular, the proposed FPN-CARAFE module improved the AP for fire targets by 8.1%. Experimental results demonstrated that the proposed model achieved superior performance in wildfire detection within power transmission corridors, achieving a mAP of 90.4% on the test dataset—an improvement of 6.4% over the baseline model. Compared with other commonly used object detection algorithms, the model developed in this study exhibited improved detection performance on the test dataset, offering research support for wildfire monitoring in power transmission corridors. Full article

Surface erosion of the coaxial thermocouple probe initiates continuous bridging of thermoelectric materials on the insulation layer surface, forming new temperature measurement junctions. This inherent ability to measure continuous self-erosion ensures the operational reliability of the coaxial thermocouples in high-temperature ablative environments. However, the fabrication of a high-temperature electrical insulation layer and a high-adhesion insulating layer in the coaxial thermocouples remains a challenge. Inspired by calcium carbonate/oxalate crystals in jujube leaves that strengthen the leaves, a bioinspired structural ceramic (BSC) mimicking these needle-like crystals is designed. This BSC demonstrates excellent high-temperature insulation (with insulation impedance of 2.55 kΩ at 1210 °C) and adhesion strength (35.3 Newtons). The BSC is successfully used as the insulating layer in a K-type coaxial thermocouple. The generation rules for surface junctions are systematically studied, revealing that stable and reliable measurement junctions can be created when the sandpaper grit does not exceed 600#. Static test results show that the K-type coaxial thermocouple ranges from 200 °C to 1200 °C with an accuracy of 1.1%, a drift rate better than 0.0137%/h, and hysteresis better than 0.81%. Dynamic test results show that the response time is 1.08 ms. The K-type coaxial thermocouple can withstand a high-temperature flame impact for 300 s at 1200 °C, as well as over forty cycles of high-power laser thermal shock, while maintaining good response characteristics. Therefore, the K-type coaxial thermocouple designed in this study provides an ideal solution for long-term temperature monitoring of the thermal components of aerospace engines under extremely high-temperature, high-speed, and strong thermal shock conditions. Full article

Honey is increasingly recognized not only as a functional food but also as a potential bioindicator of environmental pollution. This study assessed the concentrations of four potentially toxic elements (PTEs)—lead (Pb), cadmium (Cd), copper (Cu), and zinc (Zn)—in 48 multifloral honey samples collected in 2023 from seven locations across a historically polluted agro-industrial region in Romania. Samples were analyzed using Flame Atomic Absorption Spectrometry (FAAS) and Graphite Furnace AAS (GFAAS), with quality control ensured through certified reference materials. Results revealed that Pb (0.72–1.69 mg/kg) and Cd (0.02–0.37 mg/kg) levels consistently exceeded international safety thresholds, while Cu (0.62–2.22 mg/kg) and Zn (0.91–1.93 mg/kg), although essential nutrients, were found in elevated concentrations. Spatial analysis indicated a general trend of higher contamination in sites located closer to former industrial facilities, influenced by factors such as altitude and atmospheric transport. These findings confirm the persistent environmental burden in post-industrial landscapes and support the use of honey as a cost-effective tool for pollution monitoring. The study underscores the need for targeted environmental policies, sustainable apicultural practices, and continued surveillance to protect ecosystem health and food safety. Full article

Calcium ion sensors are essential in clinical diagnosis, particularly in the management of chronic kidney disease. Multiple approaches have been developed to measure calcium ions, including flame photometry and ion chromatography. However, these devices are bulky and require specialized staff for operation and evaluation. The integration of all-solid-state ion-selective determination allows the design of miniaturized and low-cost sensing that can be used for the continuous monitoring of electrolytes. However, clinical use has been limited due to the low electrochemical stability and selectivity and high noise rate. This manuscript reports for the first time a novel miniaturized Ca²⁺ ion-selective sensor, developed by using a two-layer nanocomposite thin film (5 µm thick). The device consists of functionalized silica nanoparticles embedded in a poly(vinyl chloride) (PVC) film, which was deposited onto a nanoporous zirconium silicate nanoparticle layer that served as the sensing surface. Systematic evaluation revealed that perfluoroalkane-functionalized silica nanoparticles enhanced Ca²⁺ selectivity by minimizing K⁺ diffusion, confirmed by both potentiometric measurements and quartz microbalance studies. The final sensor demonstrated a super-Nernstian sensitivity of 37 mV/Log[Ca²⁺], a low signal drift of 28 µV/s, a limit of detection of 1 µM, and exceptional selectivity against Na⁺, K⁺, and Mg²⁺ ions. Long-term testing showed stable performance over three months of continuous operation. Clinical testing was conducted on patients with chronic kidney disease. An accurate real-time monitoring of electrolyte dynamics in dialysate samples was observed, where final concentrations matched those observed in physiological conditions. Full article

Accurate quantification of metallic contaminants in rocket exhaust plumes serves as a critical diagnostic indicator for engine wear monitoring. This paper develops a hybrid method combining atomic emission spectroscopy (AES) theory with a genetic algorithm (GA) optimized backpropagation (BP) network to quantify the metallic element concentrations in liquid-propellant rocket exhaust plumes. The proposed method establishes linearized intensity–concentration mapping through the introduction of a photon transmission factor, which is derived from radiative transfer theory and experimentally calibrated via AES measurement. This critical innovation decouples the inherent nonlinearities arising from self-absorption artifacts. Through the use of the transmission factor, the training dataset for the BP network is systematically constructed by performing spectral simulations of atomic emissions. Finally, the trained network is employed to predict the concentration of metallic elements from the measured atomic emission spectra. These spectra are generated by introducing a solution containing metallic elements into a CH₄-air premixed jet flame. The predictive accuracy of the method is rigorously evaluated through 32 independent experimental trials. Results show that the quantification error of metallic elements remains within 6%, and the method exhibits robust performance under conditions of spectral self-absorption, demonstrating its reliability for rocket engine health monitoring applications. Full article

This study explores sustainable foamed polyester materials derived from natural or bio-based building blocks, including succinic, glutaric, and adipic acids, combined with trimethylolpropane and pentaerythritol. By precisely tuning the ratio of functional groups, the resulting polymers contain minimal free functionalities, leading to lower hygroscopicity and enhanced stability. The reaction is monitored by tracking the mass loss associated with water formation, the primary condensation by-product, which reveals a first-order kinetic behaviour. Infrared spectroscopy indicates that foaming occurs in a narrow time window, while esterification begins earlier and continues afterwards. Thermogravimetric analysis confirms thermal stability up to ~400 °C, with complete decomposition at 500 °C and no residue. Scanning electron microscopy images of test specimens with varying densities reveal dense, microporosity-free cell walls in both materials, indicating a homogeneous polymer matrix that contributes to the overall stabilisation of the foam structure. In flammability tests, the foams resist ignition during two 10 s methane flame exposures and, under prolonged flame, burn 40 times more slowly than conventional foams. These results demonstrate a modular system for creating bio-based foams with tunable properties—from soft and elastic to rigid—suitable for diverse applications. The materials offer a sustainable alternative to petrochemical foams while retaining excellent mechanical and thermal properties. Full article

A flash smelting furnace operation is based on the exothermic reduction of copper concentrates in the combustion shaft, and these reactions occur at high temperatures (1250–1350 °C), where flame control is fundamental to optimizing copper reduction. Furthermore, inherent physicochemical reactions of the reduction process have been shown to emit spectral lines in the visible-near infrared spectrum (250–900 nm). Thus, an optoelectronic sensor prototype is proposed and developed for flame measurements of an industrial copper concentrate flash smelting furnace. The sensor system is composed of a high-temperature optical fiber probe, which functions as a waveguide to capture the emitted flame radiation and a visible-near infrared spectrometer. From the measured radiation, flame temperature and flame dynamics are analyzed. Flame temperature is estimated using the two-wavelength temperature estimation method, and flame dynamics are defined as variations in the total emissive power, which are studied in the time and frequency domain via the Fourier Transform method. These combustion dynamics are then used to create a flame instability index, which is used to characterize the flame combustion quality. The combination of this index and sensor platform provides a powerful tool to aid in proper flame control. Full article

Polybrominated diphenyl ethers (PBDEs) are synthetic halogen compounds, industrially used as flame retardants in many flammable products. PBDEs are environmentally persistent and bioaccumulative substances that were used from the 1970s and discontinued in the 1990s. PBDEs are present in air, soil, water, and food, where they remain stable for a long time. Chronic exposure to PBDEs is associated with adverse human health effects, including cancer, immunotoxicity, hepatotoxicity, reproductive and metabolic disorders, motor and hormonal impairments, and neurotoxicity, especially in children. It has been demonstrated that PBDE exposure can cause mitochondrial and DNA damage, apoptosis, oxidative stress, epigenetic modifications, and changes in calcium and neurotransmitter levels. Here, we conduct a comprehensive review of the molecular mechanisms of the neurotoxicity of PBDEs using different approaches. We discuss the main neurotransmitter pathways affected by exposure to PBDEs in vitro and in vivo in different mammalian models. Excitatory and inhibitory signaling pathways are the putative target where PBDEs carry out their neurotoxicity. Based on this evidence, environmental PBDEs are considered a risk to human public health and a hazard to biota, underscoring the need for environmental monitoring to mitigate exposure to PBDEs. Full article

Decoding the aroma composition plays a key role in designing and producing foods that consumers prefer. Due to the complex matrix and diverse aroma compounds of foods, isolation and quantitative analytical methods were systematically reviewed. Selecting suitable and complementary aroma extraction methods based on their characteristics can provide more complete aroma composition information. Multiple mass spectrometry detectors (MS, MS/MS, TOF-MS, IMS) and specialized detectors, including flame ionization detector (FID), electron capture detector (ECD), nitrogen–phosphorus detector (NPD), and flame photometric detector (FPD), are the most important qualitative technologies in aroma identification and quantification. Furthermore, the real-time monitoring of aroma release and perception is an important developing trend in the aroma perception of future food. A combination of artificial intelligence for chromatographic analysis and characteristic databases could significantly improve the qualitative analysis efficiency and accuracy of aroma analysis. External standard method and stable isotope dilution analysis were the most popular quantification methods among the four quantification methods. The combination with flavoromics enables the decoding of aroma profile contributions and the identification of characteristic marker aroma compounds. Aroma analysis has a wide range of applications in the fields of raw materials selection, food processing monitoring, and products quality control. Full article

In this study, fumed silica (FS) was used as a support material and infused with the hydrated salt sodium hydrogen phosphate dodecahydrate (DHPD) to create shape-stabilized constant phase change materials (CPCMs). These CPCMs were integrated into a polyurethane matrix as a functional filler, resulting in low-exothermic polyurethane composite foams (CPCM-RPUFs) that demonstrate thermoregulation and flame-retardant properties. Recent findings show that CPCM-RPUF excels in thermal stability compared to pure polyurethane, with a melt phase transition enthalpy of 115.8 J/g. The use of fumed silica allows for the encapsulation of hydrated salts up to 87%, ensuring the structural integrity of the vesicles. As FS content in CPCMs increased, the internal temperature of the composite foam significantly decreased, showing excellent thermal regulation. Thermogravimetric analysis showed that the synergistic effect of DHPD and FS improved the thermal stability and flame retardancy of the composites. By monitoring the internal and surface temperature changes in the foam, it was verified that CPCMs can effectively alleviate heat accumulation during the curing process and reduce the core temperature (56.9 °C) and surface warming rate, thus realizing the thermal buffering effect. With the increase in FS content in CPCMs, the compressive strength of CPCM-RPUF can be maintained or even enhanced. This study provides a theoretical basis and technical support for the development of polyurethane composite foams with integrated thermal regulation and flame-retardant properties, which can have broad application prospects in the fields of building energy conservation, energy storage equipment, and thermal mine insulation. Full article

We present a low-cost prototype of a visible and near-infrared (VIS-NIR) remote sensing platform, optimized to detect and characterize natural flaming fire fronts from airborne nighttime light (NTL) observations, and its radiometric calibration. It uses commercially available CMOS sensor cameras and filters with roughly 100 nm bandwidths to effectively discriminate burning biomass from other sources of NTL, a critical ability for wildfire monitoring near populated areas. Our filter choice takes advantage of the strong potassium line emission near 770 nm present in natural flaming. The calibrated cameras operate at 20 ms of exposure time and boast radiance measurements with a sensitivity floor, depending on the filter, in the range 3–5 $\times {10}^{-6}$ W m⁻² sr⁻¹ nm⁻¹ with uncertainties lower than 5% and dynamic ranges near 3000–4000. An additional exposure time with a tenth of the duration is calibrated and extends the dynamic range by a factor of 10. We show images of a spatially resolved fire front from an airborne observation of flaming biomass within this radiance range. Full article

As novel pollutants, flame retardants (FRs) are prone to accumulating in soil and might increase human health risks. It is advisable to emphasize the biomagnification of FRs within the terrestrial food chain, particularly concerning mammals occupying higher trophic levels. Exposure to soil particles laden with FRs may result in numerous health complications. These findings offer significant insights into FR pollutant profiles, tracing origins and recognizing health risks associated with soil samples. Reports have revealed that exposure to FRs can pose serious health risks, including neurodevelopmental impairments, endocrine system disruption, and an increased likelihood of cancer. Nanomaterials, with their high surface area and flexible properties, possess the ability to utilize light for catalytic reactions. This unique capability allows them to effectively degrade harmful contaminants, such as FRs, in soil. Additionally, biological degradation, driven by microorganisms, offers a sustainable method for breaking down these pollutants, providing an eco-friendly approach to soil remediation. These approaches, combined with optimum remediation strategies, hold great potential for effectively addressing soil contamination in the future. Further research should prioritize several key areas, including ecological behavior, contaminant monitoring, biological metabolomics, toxicity evaluation, and ecological impact assessment. Full article