Search Results (540)

Hexavalent chromium (Cr(VI)), a common contaminant in industrial wastewater, poses severe health risks due to its carcinogenic and mutagenic properties. Consequently, the development of efficient and environmentally friendly methods to reduce Cr(VI) to the less toxic trivalent chromium (Cr(III)) is of great importance. In this study, we present a cost-effective photocatalytic approach using graphitic carbon nitride (g-C₃N₄) modified with 1,3,5-trihydroxybenzene via one-step thermal condensation. The modified photo-catalyst exhibited improved surface area, porosity, visible-light absorption, and a narrowed band gap, all of which contributed to enhanced charge separation. As a result, nearly complete reduction in Cr(VI) was achieved within 90 min under visible-light irradiation. Further optimization of catalyst dosage and EDTA concentration gave even higher reduction efficiency. This work offers a promising strategy for the design of high-performance photocatalysts for environmental remediation. Full article

This work investigates on how Fe incorporation influences the surface, microstructural, and optical properties of solution precursor plasma-sprayed TiO₂ coatings. The Fe-TiO₂ coatings were prepared using titanium isopropoxide and iron acetylacetonate as precursors, with ethanol as the solvent. X-ray diffraction analysis revealed the existence of both anatase and rutile TiO₂ phases, with a predominant rutile phase, also confirmed by Raman spectroscopy. There was an increase in the anatase crystals upon the addition of Fe ions. A longer spray distance further enhanced the anatase content and reduced the average TiO₂ crystallite sizes present in the Fe-added coatings. SEM cross-sectional images displayed finely grained, densely packed deposits in the Fe-added coatings. UV-Vis spectroscopy showed visible-light absorption by the Fe-TiO₂ coatings, with reduced band gap energies ranging from 2.846 ± 0.002 eV to 2.936 ± 0.003 eV. Photoluminescence analysis showed reduced emission intensity at 356 nm (3.48 eV) for the Fe-TiO₂ coatings. These findings confirm solution precursor plasma spray to be an effective method for developing Fe-TiO₂ coatings with potential application as visible-light-active photocatalysts. Full article

This study assessed the applicability of visible–near-infrared (vis-NIR) spectroscopy to predicting the water retention characteristics of forest soils in Japan, which vary widely owing to the presence of volcanic ash. Soil samples were collected from 34 sites, and the volumetric water content was measured at eight levels of matric suction. Spectral data were processed by using the second derivative of the absorbance, and regression models were developed by using explainable boosting machine (EBM), which is an interpretable machine learning method. Although the prediction accuracy was limited owing to the small sample size and soil heterogeneity, EBM performed better under saturated conditions (R² = 0.30), which suggests that vis-NIR spectroscopy can capture water-related features, especially under wet conditions. Importance analysis consistently selected wavelengths that were associated with organic matter and hydrated clay minerals. The important wavelengths clearly shifted from free-water bands in wet soils to mineral-related absorption bands in dry soils. These findings highlight the potential of coupling vis-NIR spectroscopy with interpretable models like EBM for estimating the hydraulic properties of forest soils. Improved accuracy is expected with larger datasets and stratified models by soil type, which can facilitate more efficient soil monitoring in forests. Full article

Visible and near-infrared (Vis–NIR) spectroscopy enables the rapid prediction of soil properties but faces three limitations with conventional machine learning: information loss and overfitting from high-dimensional spectral features; inadequate modeling of nonlinear soil–spectra relationships; and failure to integrate multi-scale spatial features. To address these challenges, we propose ReSE-AP Net, a multi-scale attention residual network with spatial pyramid pooling. Built on convolutional residual blocks, the model incorporates a squeeze-and-excitation channel attention mechanism to recalibrate feature weights and an atrous spatial pyramid pooling (ASPP) module to extract multi-resolution spectral features. This architecture synergistically represents weak absorption peaks (400–1000 nm) and broad spectral bands (1000–2500 nm), overcoming single-scale modeling limitations. Validation on the LUCAS2009 dataset demonstrated that ReSE-AP Net outperformed conventional machine learning by improving the R² by 2.8–36.5% and reducing the RMSE by 14.2–69.2%. Compared with existing deep learning methods, it increased the R² by 0.4–25.5% for clay, silt, sand, organic carbon, calcium carbonate, and phosphorus predictions, and decreased the RMSE by 0.7–39.0%. Our contributions include statistical analysis of LUCAS2009 spectra, identification of conventional method limitations, development of the ReSE-AP Net model, ablation studies, and comprehensive comparisons with alternative approaches. Full article

Soil organic carbon (SOC) is a critical indicator of soil health and carbon sequestration potential. Accurate, efficient, and scalable SOC estimation is essential for sustainable orchard management and climate-resilient agriculture. However, traditional visible–near-infrared (Vis–NIR) spectroscopy often suffers from limited chemical specificity and weak adaptability in heterogeneous soil environments. To overcome these limitations, this study develops a five-stage modeling framework that systematically integrates Fourier Transform Infrared (FTIR) spectroscopy with hybrid machine learning techniques for non-destructive SOC prediction in citrus orchard soils. The proposed framework includes (1) FTIR spectral acquisition; (2) a comparative evaluation of nine spectral preprocessing techniques; (3) dimensionality reduction via three representative feature selection algorithms, namely the Successive Projections Algorithm (SPA), Competitive Adaptive Reweighted Sampling (CARS), and Principal Component Analysis (PCA); (4) regression modeling using six machine learning algorithms, namely the Random Forest (RF), Support Vector Regression (SVR), Gray Wolf Optimized SVR (SVR-GWO), Partial Least Squares Regression (PLSR), Principal Component Regression (PCR), and the Back-propagation Neural Network (BPNN); and (5) comprehensive performance assessments and the identification of the optimal modeling pathway. The results showed that second-derivative (SD) preprocessing significantly enhanced the spectral signal-to-noise ratio. Among feature selection methods, the SPA reduced over 300 spectral bands to 10 informative wavelengths, enabling efficient modeling with minimal information loss. The SD + SPA + RF pipeline achieved the highest prediction performance (R² = 0.84, RMSE = 4.67 g/kg, and RPD = 2.51), outperforming the PLSR and BPNN models. This study presents a reproducible and scalable FTIR-based modeling strategy for SOC estimation in orchard soils. Its adaptive preprocessing, effective variable selection, and ensemble learning integration offer a robust solution for real-time, cost-effective, and transferable carbon monitoring, advancing precision soil sensing in orchard ecosystems. Full article

One-dimensional TiO₂ nanotube arrays (TNAs) were vertically aligned and obtained via the electrochemical anodization method. In this study, Bi₂S₃-TiO₂-SiO₂/TNA heterojunction photocatalysts were successfully prepared with different amounts of Bismuth(III) sulfide (Bi₂S₃) loading on the TNAs by the successive ionic layer adsorption and reaction (SILAR) method and characterized by X-ray diffraction (XRD) patterns, field-emission scanning electron microscope–energy-dispersive spectroscopy (FESEM-EDS), Fourier transform infrared (FTIR) spectra, ultraviolet-visible diffuse reflectance spectra (UV–Vis/DRS), and electrochemical impedance spectroscopy (EIS) techniques. The photocatalytic performances of the samples were investigated by degrading Basic Yellow 28 (BY 28) under visible-light irradiation. Optimization of the condition using the response surface methodology (RSM) and central composite rotatable design (CCRD) technique resulted in the degradation of BY 28 dye, showing that the catalyst with 9.6 mg/cm² (designated as Bi₂S_3(9.6)-TiO₂-SiO₂/TNA) showed the maximum yield in the degradation process. The crystallite size of about 17.03 nm was estimated using the Williamson–Hall method. The band gap energies of TiO₂-SiO₂/TNA and Bi₂S_3(9.6)-TiO₂-SiO₂/TNA were determined at 3.27 and 1.87 eV for the direct electronic transitions, respectively. The EIS of the ternary system exhibited the smallest arc diameter, indicating an accelerated charge transfer rate that favors photocatalytic activity. Full article

Tea is one of the most widely consumed non-alcoholic beverages globally, yet its yield and quality are significantly impacted by herbivory from tea geometrids. To accurately detect herbivory stress in tea leaves, this study integrated metabolomics with visible-near-infrared spectroscopy (VIS-NIRS) to explore its in situ capabilities and underlying mechanisms. The results demonstrated that metabolomic data, combined with PCA-based linear dimensionality reduction, could effectively distinguish between tea leaves subjected to herbivory by different densities of tea geometrids. VIS-NIRS successfully identified herbivore-damaged leaves, achieving an optimal average classification accuracy of 0.857. Furthermore, VIS-NIRS was able to differentiate leaves subjected to herbivory on different days. The application of appropriate preprocessing techniques significantly enhanced temporal classification, achieving the highest average classification accuracy of 0.773. By integrating metabolomics and spectral band analysis, the spectral range of 800–2500 nm was found to more accurately identify leaves exposed to herbivory for a prolonged period. Compared to using the full spectrum, the model built within this wavelength range improved classification accuracy by 10%. In conclusion, this study provides a solid theoretical foundation for the in situ, rapid detection of tea geometrid herbivory stress in the field using VIS-NIRS, offering key technical support for future applications. Full article

This study systematically investigates for the first time the effects of dual-site co-cultivation on spectral characteristics and trace element enrichment in marine-cultured Akoya pearls from Beihai, China. Akoya pearls were cultured over a one-year period, with the final 40-day stage designated as the terminal phase. During this period, two experimental groups of pearl oysters were established: Group Y remained in Beihai for continued local cultivation and harvest, while Group B was transferred to Weihai, Shandong Province, for terminal-stage farming under different thermal conditions. A series of comparative analyses were performed using Fourier-transform infrared (FTIR) spectroscopy, ultraviolet-visible (UV-Vis) spectroscopy, Raman spectroscopy, X-ray fluorescence (XRF), and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). The FTIR results revealed distinct differences between the two groups in the distribution of amide and polysaccharide functional groups, particularly around 1643 cm⁻¹ and 1100 cm⁻¹. The UV-Vis spectra of Group B displayed characteristic absorption bands at 430 nm and 460 nm, associated with the organic matrix of the nacre. Raman spectroscopy further indicated a higher abundance of organic-related vibrational features in Group B. Additionally, both XRF and LA-ICP-MS analyses consistently showed significant differences in the concentrations and distributions of trace elements, particularly copper (Cu), cobalt (Co), and zinc (Zn). The findings demonstrate that the dual-site co-cultivation mode significantly impacts both the organic composition and trace element enrichment patterns in seawater Akoya pearls. This research provides valuable references for optimizing environmental parameters in pearl cultivation processes. Full article

Peroxymonosulfate (PMS)-based advanced oxidation technology has emerged as an effective means for removing organic pollutants from water due to its strong oxidizing ability. However, enhancing the activation efficiency of PMS represents a key challenge at present. SrTiO₃, a typical perovskite metal oxide, holds potential in the field of the photocatalytic degradation of pollutants, yet its application is limited by the wide bandgap and fast carrier recombination rates. This study optimized the photocatalytic performance of SrTiO₃ by regulating its electronic structure and optical properties through cobalt (Co) doping. Experimental results (TRPL, TPV, UV–Vis DRS, ESR, etc.) and DFT calculations (GGA-PBE) demonstrated that Co doping shifted the d-band center of SrTiO₃ upwards, optimized the adsorption energy of SO₄⁻, enhanced the sunlight response range, and significantly improved carrier extraction efficiency. Under visible light irradiation, 2,4-dichlorophenol (2,4-DCP) could be effectively degraded within 60 min in a wide pH range. Through Fukui function calculation (B3LYP/6-31G*) and experimental characterization analysis (HPLC-MS and IC), the possible degradation pathways of 2,4-DCP and the mechanism for photocatalysis were investigated. The toxicity analysis (T.E.S.T) confirmed the reduced toxicity of the degradation products of 2,4-DCPs. This study provides a reference for the catalyst design and optimization strategy of PMS-based advanced oxidation technology. Full article

Eucalyptus scab disease (Elsinoë) is a harmful plant fungus that can disrupt various ecological and economic services provided by commercial forests. To effectively control and monitor the occurrence of forest pathogens, it is important to understand their spatial distribution within the infected area. Consistent monitoring, together with high-resolution imagery obtained from unmanned aerial vehicles (UAVs), has become important in forest management. Therefore, this study focuses on detecting and mapping the spatial distribution of E. masingae within commercial forests using image texture and vegetation indices (VIs) computed from a UAV sensor with machine learning (ML) and deep learning (DL) models. The fast large margin (FLM), random forest (RF), and deep learning (DL) models were used to determine which model effectively mapped the spatial distribution of the disease. The results indicated that image texture significantly increased the model accuracies (FLM = 94.8%; RF = 98.9%; DL = 98.9%) as opposed to the results without the use of image texture (FLM = 84.4%; RF = 76.1%; DL = 81.7%). Since the DL model obtained the fastest model run time and was proven to be the most significant model, it selected the mean, homogeneity, second moment, and correlation texture parameters which were predominantly determined from the red and blue bands of the UAV sensor containing visible bands. Additionally, the 3 × 3 moving window size was ideal for detecting E. masingae since the estimation of texture parameters was reduced efficiently. Overall, this study showcases the ability of UAVs to effectively map forest disease. Together with that, it has proven that the DL model outperformed the conventional ML models. Full article

Low-cost unmanned aerial vehicle (UAV) visible light remote sensing provides new opportunities for plant community monitoring, but its practical deployment in different ecosystems is still limited by the lack of standardized vegetation index (VI) optimization for multi-species coverage extraction. This study developed a universal method integrating four VIs—Excess Green Index (EXG), Visible Band Difference Vegetation Index (VDVI), Red-Green Ratio Index (RGRI), and Red-Green-Blue Vegetation Index (RGBVI)—to bridge UAV imagery with plant communities. By combining spectral separability analysis with machine learning (SVM), we established dynamic thresholds applicable to crops, trees, and shrubs, achieving cross-species compatibility without multispectral data. The results showed that all VIs achieved robust vegetation/non-vegetation discrimination (Kappa > 0.84), with VDVI being more suitable for distinguishing vegetation from non-vegetation. The overall classification accuracy for different vegetation types exceeded 92.68%, indicating that the accuracy is considerable. Crop coverage extraction showed a minimum segmentation error of 0.63, significantly lower than that of other vegetation types. These advances enable high-resolution vegetation monitoring, supporting biodiversity assessment and ecosystem service quantification. Our research findings track the impact of plant communities on the ecological environment and promote the application of UAVs in ecological restoration and precision agriculture. Full article

Bismuth oxide (β-Bi₂O₃) is a promising visible-light-driven photocatalyst due to its narrow direct bandgap, but its practical application is hindered by rapid electron–hole recombination and limited surface active sites. This study demonstrates a sol-gel synthesis approach to tailor β-Bi₂O₃ nanoparticles through magnesium (Mg) doping, achieving remarkable enhancements in the photocatalytic degradation of organic pollutants and hydrogen evolution. The structural analysis through XRD, SEM, and EDX confirmed Mg-doping concentrations of 0.025 to 0.1 M led to crystallite size reduction from 79 nm to 13 nm, while the UV–Vis bandgap measurement showed it decreased from 3.8 eV to 3.08–3.3 eV. The photodegradation efficiency increased through Mg doping at a 0.1 M concentration, with the highest rate constant value of 0.0217 min⁻¹. The doping process led to VB potential reduction between 3.37 V (pristine) and 2.78–2.91 V across the doped samples when referenced to SCE. The photocatalytic performance of Mg_0.075Bi_1.925O₃ improved with its 3.2 V VB potential because the photoelectric band arrangement enhanced both light absorption and charge separation. The combination of modifications through Mg doping yielded an enhanced photocatalytic performance, which proves that magnesium doping is a pivotal approach to modifying β-Bi₂O₃ suitable for environmentally and energy-related applications. Full article

Vanadium-bearing beryl is a vanadium-bearing variety of green beryl (distinct from emerald) that exhibits an “electro-optical” green (blue-green) color, which has led to its commercial popularity. However, the underlying coloration mechanism remains unclear. The present study adopted standard gemological tests and non-destructive spectroscopic tests, such as X-ray fluorescence, UV-visible-near infrared (UV-Vis-NIR), infrared and Raman spectroscopy, to analyze the vanadium-bearing beryl from Nigeria. The results of these tests indicated the presence of Fe as the predominant chromogenic element of vanadium-bearing beryl, followed by V, at a level exceeding that of Cr. Furthermore, the samples displayed lower levels of alkali and magnesium when compared to other beryls, accompanied by lower refractive indices and specific gravities. Spectroscopic analysis indicates that the structural channels are dominated by type I H₂O, with CO₂, HDO, and D₂O molecules also present. The inclusions observed in vanadium-bearing beryl bear a resemblance to those found in typical aquamarines, which are raindrop-shaped inclusions, and to those found in emeralds of various origins, which are irregular, jagged, gas–liquid two-phase/three-phase inclusions. The broad UV-Vis-NIR absorption bands at 427 and 610 nm are characteristic of V³⁺ (and a minor amount of Cr³⁺). Charge transfer between Fe²⁺ and Fe³⁺ may also contribute to the 610 nm band, which is superimposed on the absorption bands of V³⁺ and Cr³⁺. These factors primarily contribute to the blue-green coloration of beryl. The absorption induced by V³⁺ in the visible violet-blue region exhibits stronger intensity and a greater tendency towards the blue region compared to Cr³⁺. Consequently, the resultant vanadium-bearing beryl acquires the yellow-green hue (induced by V) overlaid with the light blue (induced by charge transfer between Fe²⁺-Fe³⁺ pairs), resulting in the so-called “electro-optical” green (blue-green) beryl. Full article

The combination of the catalytic properties of Al₂O_3/TiO₂ formed an efficient system to degrade the ubiquitous pollutants TPA and PET. The coating (Al₂O₃)_0.75TiO₂ was characterized by X-ray diffraction. Stainless steel disks with photo-catalyst coating were placed transversely in a 3.0 L vertical glass reactor with ascending airflow for supplying oxygen to the reaction medium and visible light lamps for photo-activation. The analysis of the coating homogeneity, morphology and particle size distribution of the TiO₂ coatings and (Al₂O₃)_0.75TiO₂ system were confirmed by SEM. Optical properties and band-gap energy were calculated by using the Tauc equation. UV–Vis spectrophotometry (UV–Vis) and chemical oxygen demand (COD) were the quantitative techniques to measure the reduction in the initial TPA and PET concentrations. Full article

Conductive polymers play a crucial role in the advancement of modern technologies, particularly in the field of organic photovoltaics (OPVs). Due to advantages such as flexibility, low specific weight, ease of processing, and low production costs, polymeric materials present an attractive alternative to traditional photovoltaic materials. This study investigates the properties of a polymer blend composed of PCPDTBT (donor) and PNDI(2HD)2T (acceptor), used as the active layer in bulk heterojunction (BHJ) solar cells. The motivation behind this research was the search for a novel n-type polymer material with potentially better properties than the commonly used P(NDI2OD-T2). Comprehensive characterization of thin films made from the individual polymers and their blend was conducted using Fourier Transform Infrared Spectroscopy (FTIR), Atomic Force Microscopy (AFM), Scanning Electron Microscopy (SEM), Ultraviolet-Visible Spectroscopy (UV-Vis), four-point probe conductivity measurements, and photovoltaic testing. The prepared films were continuous, uniform, and exhibited low surface roughness (R_a < 2.5 nm). Spectroscopic analysis showed that the blend absorbs light in a broad range of the spectrum, with slight bathochromic shifts compared to individual polymers. Electrical measurements indicated that the blend’s conductivity (9.1 µS/cm) was lower than that of pure PCPDTBT but higher than that of PNDI(2HD)2T, with an optical band gap of 1.34 eV. Photovoltaic devices fabricated using the blend demonstrated an average power conversion efficiency (PCE) of 6.45%, with a short-circuit current of 14.37 mA/cm² and an open-circuit voltage of 0.89 V. These results confirm the feasibility of using PCPDTBT:PNDI(2HD)2T blends as active layers in BHJ solar cells and provide a promising direction for further optimization in terms of polymer ratio and processing conditions. Full article