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Keywords = near-infrared region

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22 pages, 4194 KiB  
Article
Grain Boundary Regulation in Aggregated States of MnOx Nanofibres and the Photoelectric Properties of Their Nanocomposites Across a Broadband Light Spectrum
by Xingfa Ma, Xintao Zhang, Mingjun Gao, Ruifen Hu, You Wang and Guang Li
Coatings 2025, 15(8), 920; https://doi.org/10.3390/coatings15080920 (registering DOI) - 6 Aug 2025
Abstract
Improving charge transport in the aggregated state of nanocomposites is challenging due to the large number of defects present at grain boundaries. To enhance the charge transfer and photogenerated carrier extraction of MnOx nanofibers, a MnOx/GO (graphene oxide) nanocomposite was [...] Read more.
Improving charge transport in the aggregated state of nanocomposites is challenging due to the large number of defects present at grain boundaries. To enhance the charge transfer and photogenerated carrier extraction of MnOx nanofibers, a MnOx/GO (graphene oxide) nanocomposite was prepared. The effects of GO content and bias on the optoelectronic properties were studied. Representative light sources at 405, 650, 780, 808, 980, and 1064 nm were used to examine the photoelectric signals. The results indicate that the MnOx/GO nanocomposites have photocurrent switching behaviours from the visible region to the NIR (near-infrared) when the amount of GO added is optimised. It was also found that even with zero bias and storage of the nanocomposite sample at room temperature for over 8 years, a good photoelectric signal could still be extracted. This demonstrates that the MnOx/GO nanocomposites present a strong built-in electric field that drives the directional motion of photogenerated carriers, avoids the photogenerated carrier recombination, and reflect a good photophysical stability. The strength of the built-in electric field is strongly affected by the component ratios of the resulting nanocomposite. The formation of the built-in electric field results from interfacial charge transfer in the nanocomposite. Modulating the charge behaviour of nanocomposites can significantly improve the physicochemical properties of materials when excited by light with different wavelengths and can be used in multidisciplinary applications. Since the recombination of photogenerated electron–hole pairs is the key bottleneck in multidisciplinary fields, this study provides a simple, low-cost method of tailoring defects at grain boundaries in the aggregated state of nanocomposites. These results can be used as a reference for multidisciplinary fields with low energy consumption. Full article
30 pages, 4444 KiB  
Article
Unveiling the Potential of Novel Ternary Chalcogenide SrHfSe3 for Eco-Friendly, Self-Powered, Near-Infrared Photodetectors: A SCAPS-1D Simulation Study
by Salah Abdo, Ambali Alade Odebowale, Amer Abdulghani, Khalil As’ham, Sanjida Akter, Haroldo Hattori, Nicholas Kanizaj and Andrey E. Miroshnichenko
Sci 2025, 7(3), 113; https://doi.org/10.3390/sci7030113 - 6 Aug 2025
Abstract
Ternary chalcogenide-based sulfide materials with distorted morphologies such as BaZrS3, CaZrS3, and SrZrS3, have recently gained much attention in optoelectronics and photovoltaics due to their high structural and thermal stability and compatibility with low-cost, earth-abundant synthesis routes. [...] Read more.
Ternary chalcogenide-based sulfide materials with distorted morphologies such as BaZrS3, CaZrS3, and SrZrS3, have recently gained much attention in optoelectronics and photovoltaics due to their high structural and thermal stability and compatibility with low-cost, earth-abundant synthesis routes. However, their relatively large bandgaps often limit their suitability for near-infrared (NIR) photodetectors. Here, we conducted a comprehensive investigation of SrHfSe3, a ternary chalcogenide with an orthorhombic crystal structure and distinctive needle-like morphology, as a promising candidate for NIR photodetection. SrHfSe3 exhibits a direct bandgap of 1.02 eV, placing it well within the NIR range. Its robust structure, high temperature stability, phase stability and natural abundance make it a compelling material for next-generation, self-powered NIR photodetectors. An in-depth analysis of the SrHfSe3-based photodetector was performed using SCAPS-1D simulations, focusing on key performance metrics such as J–V behavior, photoresponsivity, and specific detectivity. Device optimization was achieved by thoroughly altering each layer thickness, doping concentrations, and defect densities. Additionally, the influence of interface defects, absorber bandgap, and operating temperature was assessed to enhance the photoresponse. Under optimal conditions, the device achieved a short-circuit current density (Jsc) of 45.88 mA/cm2, an open-circuit voltage (Voc) of 0.7152 V, a peak photoresponsivity of 0.85 AW−1, and a detectivity of 2.26 × 1014 Jones at 1100 nm. A broad spectral response spanning 700–1200 nm confirms its efficacy in the NIR region. These results position SrHfSe3 as a strong contender for future NIR photodetectors and provide a foundation for experimental validation in advanced optoelectronic applications. Full article
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17 pages, 3360 KiB  
Article
Efficient and Selective Multiple Ion Chemosensor by Novel Near-Infrared Sensitive Symmetrical Squaraine Dye Probe
by Sushma Thapa, Kshitij RB Singh and Shyam S. Pandey
Chemosensors 2025, 13(8), 288; https://doi.org/10.3390/chemosensors13080288 - 4 Aug 2025
Abstract
A novel near-infrared (NIR) squaraine-based chemosensor, SQ-68, has been designed and synthesized for the sensitive and selective detection of Cu2+ and Ag+ ions, offering a compact solution for multi-analyte sensing. SQ-68 demonstrates high selectivity, with its performance influenced by the [...] Read more.
A novel near-infrared (NIR) squaraine-based chemosensor, SQ-68, has been designed and synthesized for the sensitive and selective detection of Cu2+ and Ag+ ions, offering a compact solution for multi-analyte sensing. SQ-68 demonstrates high selectivity, with its performance influenced by the solvent environment: It selectively detects Cu2+ in acetonitrile and Ag+ in an ethanol–water mixture. Upon binding with either ion, SQ-68 undergoes significant absorption changes in the NIR region, accompanied by visible color changes, enabling naked-eye detection. Spectroscopic studies confirm a 1:1 binding stoichiometry with both Cu2+ and Ag+, accompanied by hypochromism. The detection limits are 0.09 μM for Cu2+ and 0.38 μM for Ag+, supporting highly sensitive quantification. The sensor’s practical applicability was validated in real water samples (sea, lake, and tap water), with recovery rates ranging from 73–95% for Cu2+ to 59–99% for Ag+. These results establish SQ-68 as a reliable and efficient chemosensor for environmental monitoring and water quality assessment. Its dual-analyte capability, solvent-tunable selectivity, and visual detection features make it a promising tool for rapid and accurate detection of heavy metal ions in diverse aqueous environments. Full article
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15 pages, 3267 KiB  
Article
Monitoring and Analyzing Aquatic Vegetation Using Sentinel-2 Imagery Time Series: A Case Study in Chimaditida Shallow Lake in Greece
by Maria Kofidou and Vasilios Ampas
Limnol. Rev. 2025, 25(3), 35; https://doi.org/10.3390/limnolrev25030035 - 1 Aug 2025
Viewed by 122
Abstract
Aquatic vegetation plays a crucial role in freshwater ecosystems by providing habitats, regulating water quality, and supporting biodiversity. This study aims to monitor and analyze the dynamics of aquatic vegetation in Chimaditida Shallow Lake, Greece, using Sentinel-2 satellite imagery, with validation from field [...] Read more.
Aquatic vegetation plays a crucial role in freshwater ecosystems by providing habitats, regulating water quality, and supporting biodiversity. This study aims to monitor and analyze the dynamics of aquatic vegetation in Chimaditida Shallow Lake, Greece, using Sentinel-2 satellite imagery, with validation from field measurements. Data processing was performed using Google Earth Engine and QGIS. The study focuses on discriminating and mapping two classes of aquatic surface conditions: areas covered with Floating and Emergent Aquatic Vegetation and open water, covering all seasons from 1 March 2024, to 28 February 2025. Spectral bands such as B04 (red), B08 (near infrared), B03 (green), and B11 (shortwave infrared) were used, along with indices like the Modified Normalized Difference Water Index and Normalized Difference Vegetation Index. The classification was enhanced using Otsu’s thresholding technique to distinguish accurately between Floating and Emergent Aquatic Vegetation and open water. Seasonal fluctuations were observed, with significant peaks in vegetation growth during the summer and autumn months, including a peak coverage of 2.08 km2 on 9 September 2024 and a low of 0.00068 km2 on 28 December 2024. These variations correspond to the seasonal growth patterns of Floating and Emergent Aquatic Vegetation, driven by temperature and nutrient availability. The study achieved a high overall classification accuracy of 89.31%, with producer accuracy for Floating and Emergent Aquatic Vegetation at 97.42% and user accuracy at 95.38%. Validation with Unmanned Aerial Vehicle-based aerial surveys showed a strong correlation (R2 = 0.88) between satellite-derived and field data, underscoring the reliability of Sentinel-2 for aquatic vegetation monitoring. Findings highlight the potential of satellite-based remote sensing to monitor vegetation health and dynamics, offering valuable insights for the management and conservation of freshwater ecosystems. The results are particularly useful for governmental authorities and natural park administrations, enabling near-real-time monitoring to mitigate the impacts of overgrowth on water quality, biodiversity, and ecosystem services. This methodology provides a cost-effective alternative for long-term environmental monitoring, especially in regions where traditional methods are impractical or costly. Full article
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18 pages, 7222 KiB  
Article
Assessing Risks and Innovating Traceability in Campania’s Illegal Mussel Sale: A One Health Perspective
by Valeria Vuoso, Attilio Mondelli, Carlotta Ceniti, Iolanda Venuti, Giorgio Ciardella, Yolande Thérèse Rose Proroga, Bruna Nisci, Rosa Luisa Ambrosio and Aniello Anastasio
Foods 2025, 14(15), 2672; https://doi.org/10.3390/foods14152672 - 29 Jul 2025
Viewed by 338
Abstract
The illegal sale of mussels is a persistent problem for food safety and public health in the Campania region, where bivalve molluscs are often sold without traceability, evading regulatory controls. In this study, ten batches of mussels seized from unauthorized vendors were analyzed [...] Read more.
The illegal sale of mussels is a persistent problem for food safety and public health in the Campania region, where bivalve molluscs are often sold without traceability, evading regulatory controls. In this study, ten batches of mussels seized from unauthorized vendors were analyzed to evaluate their microbiological safety and trace their geographical origin. High loads of Escherichia coli, exceeding European regulatory limits (Regulation (EC) No 2073/2005), were detected in all samples. In addition, Salmonella Infantis strains resistant to trimethoprim-sulfamethoxazole and azithromycin were isolated, raising further concerns about antimicrobial resistance. Of the 93 Vibrio isolates, identified as V. alginolyticus and V. parahaemolyticus, 37.63% showed multidrug resistance. Approximately 68.57% of the isolates were resistant to tetracyclines and cephalosporins. The presence of resistance to last-resort antibiotics such as carbapenems (11.43%) is particularly alarming. Near-infrared spectroscopy, combined with chemometric models, was used to obtain traceability information, attributing a presumed origin to the seized mussel samples. Of the ten samples, seven were attributed to the Phlegraean area. These findings have provided valuable insights, reinforcing the need for continuous and rigorous surveillance and the integration of innovative tools to ensure seafood safety and support One Health approaches. Full article
(This article belongs to the Section Food Quality and Safety)
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22 pages, 3083 KiB  
Article
Evaluating the Effect of Thermal Treatment on Phenolic Compounds in Functional Flours Using Vis–NIR–SWIR Spectroscopy: A Machine Learning Approach
by Achilleas Panagiotis Zalidis, Nikolaos Tsakiridis, George Zalidis, Ioannis Mourtzinos and Konstantinos Gkatzionis
Foods 2025, 14(15), 2663; https://doi.org/10.3390/foods14152663 - 29 Jul 2025
Viewed by 355
Abstract
Functional flours, high in bioactive compounds, have garnered increasing attention, driven by consumer demand for alternative ingredients and the nutritional limitations of wheat flour. This study explores the thermal stability of phenolic compounds in various functional flours using visible, near and shortwave-infrared (Vis–NIR–SWIR) [...] Read more.
Functional flours, high in bioactive compounds, have garnered increasing attention, driven by consumer demand for alternative ingredients and the nutritional limitations of wheat flour. This study explores the thermal stability of phenolic compounds in various functional flours using visible, near and shortwave-infrared (Vis–NIR–SWIR) spectroscopy (350–2500 nm), integrated with machine learning (ML) algorithms. Random Forest models were employed to classify samples based on flour type, baking temperature, and phenolic concentration. The full spectral range yielded high classification accuracy (0.98, 0.98, and 0.99, respectively), and an explainability framework revealed the wavelengths most relevant for each class. To address concerns regarding color as a confounding factor, a targeted spectral refinement was implemented by sequentially excluding the visible region. Models trained on the 1000–2500 nm and 1400–2500 nm ranges showed minor reductions in accuracy, suggesting that classification is not solely driven by visible characteristics. Results indicated that legume and wheat flours retain higher total phenolic content (TPC) under mild thermal conditions, whereas grape seed flour (GSF) and olive stone flour (OSF) exhibited notable thermal stability of TPC even at elevated temperatures. These first findings suggest that the proposed non-destructive spectroscopic approach enables rapid classification and quality assessment of functional flours, supporting future applications in precision food formulation and quality control. Full article
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23 pages, 4324 KiB  
Article
Monitoring Nitrogen Uptake and Grain Quality in Ponded and Aerobic Rice with the Squared Simplified Canopy Chlorophyll Content Index
by Gonzalo Carracelas, John Hornbuckle and Carlos Ballester
Remote Sens. 2025, 17(15), 2598; https://doi.org/10.3390/rs17152598 - 25 Jul 2025
Viewed by 444
Abstract
Remote sensing tools have been proposed to assist with rice crop monitoring but have been developed and validated on ponded rice. This two-year study was conducted on a commercial rice farm with irrigation automation technology aimed to (i) understand how canopy reflectance differs [...] Read more.
Remote sensing tools have been proposed to assist with rice crop monitoring but have been developed and validated on ponded rice. This two-year study was conducted on a commercial rice farm with irrigation automation technology aimed to (i) understand how canopy reflectance differs between high-yielding ponded and aerobic rice, (ii) validate the feasibility of using the squared simplified canopy chlorophyll content index (SCCCI2) for N uptake estimates, and (iii) explore the SCCCI2 and similar chlorophyll-sensitive indices for grain quality monitoring. Multispectral images were collected from an unmanned aerial vehicle during both rice-growing seasons. Above-ground biomass and nitrogen (N) uptake were measured at panicle initiation (PI). The performance of single-vegetation-index models in estimating rice N uptake, as previously published, was assessed. Yield and grain quality were determined at harvest. Results showed that canopy reflectance in the visible and near-infrared regions differed between aerobic and ponded rice early in the growing season. Chlorophyll-sensitive indices showed lower values in aerobic rice than in the ponded rice at PI, despite having similar yields at harvest. The SCCCI2 model (RMSE = 20.52, Bias = −6.21 Kg N ha−1, and MAPE = 11.95%) outperformed other models assessed. The SCCCI2, squared normalized difference red edge index, and chlorophyll green index correlated at PI with the percentage of cracked grain, immature grain, and quality score, suggesting that grain milling quality parameters could be associated with N uptake at PI. This study highlights canopy reflectance differences between high-yielding aerobic (averaging 15 Mg ha−1) and ponded rice at key phenological stages and confirms the validity of a single-vegetation-index model based on the SCCCI2 for N uptake estimates in ponded and non-ponded rice crops. Full article
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30 pages, 13059 KiB  
Article
Verifying the Effects of the Grey Level Co-Occurrence Matrix and Topographic–Hydrologic Features on Automatic Gully Extraction in Dexiang Town, Bayan County, China
by Zhuo Chen and Tao Liu
Remote Sens. 2025, 17(15), 2563; https://doi.org/10.3390/rs17152563 - 23 Jul 2025
Viewed by 358
Abstract
Erosion gullies can reduce arable land area and decrease agricultural machinery efficiency; therefore, automatic gully extraction on a regional scale should be one of the preconditions of gully control and land management. The purpose of this study is to compare the effects of [...] Read more.
Erosion gullies can reduce arable land area and decrease agricultural machinery efficiency; therefore, automatic gully extraction on a regional scale should be one of the preconditions of gully control and land management. The purpose of this study is to compare the effects of the grey level co-occurrence matrix (GLCM) and topographic–hydrologic features on automatic gully extraction and guide future practices in adjacent regions. To accomplish this, GaoFen-2 (GF-2) satellite imagery and high-resolution digital elevation model (DEM) data were first collected. The GLCM and topographic–hydrologic features were generated, and then, a gully label dataset was built via visual interpretation. Second, the study area was divided into training, testing, and validation areas, and four practices using different feature combinations were conducted. The DeepLabV3+ and ResNet50 architectures were applied to train five models in each practice. Thirdly, the trainset gully intersection over union (IOU), test set gully IOU, receiver operating characteristic curve (ROC), area under the curve (AUC), user’s accuracy, producer’s accuracy, Kappa coefficient, and gully IOU in the validation area were used to assess the performance of the models in each practice. The results show that the validated gully IOU was 0.4299 (±0.0082) when only the red (R), green (G), blue (B), and near-infrared (NIR) bands were applied, and solely combining the topographic–hydrologic features with the RGB and NIR bands significantly improved the performance of the models, which boosted the validated gully IOU to 0.4796 (±0.0146). Nevertheless, solely combining GLCM features with RGB and NIR bands decreased the accuracy, which resulted in the lowest validated gully IOU of 0.3755 (±0.0229). Finally, by employing the full set of RGB and NIR bands, the GLCM and topographic–hydrologic features obtained a validated gully IOU of 0.4762 (±0.0163) and tended to show an equivalent improvement with the combination of topographic–hydrologic features and RGB and NIR bands. A preliminary explanation is that the GLCM captures the local textures of gullies and their backgrounds, and thus introduces ambiguity and noise into the convolutional neural network (CNN). Therefore, the GLCM tends to provide no benefit to automatic gully extraction with CNN-type algorithms, while topographic–hydrologic features, which are also original drivers of gullies, help determine the possible presence of water-origin gullies when optical bands fail to tell the difference between a gully and its confusing background. Full article
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17 pages, 1794 KiB  
Article
Detection of Cumulative Bruising in Prunes Using Vis–NIR Spectroscopy and Machine Learning: A Nonlinear Spectral Response Approach
by Lisi Lai, Hui Zhang, Jiahui Gu and Long Wen
Appl. Sci. 2025, 15(15), 8190; https://doi.org/10.3390/app15158190 - 23 Jul 2025
Viewed by 184
Abstract
Early and accurate detection of mechanical damage in prunes is crucial for preserving postharvest quality and enabling automated sorting. This study proposes a practical and reproducible method for identifying cumulative bruising in prunes using visible–near-infrared (Vis–NIR) reflectance spectroscopy coupled with machine learning techniques. [...] Read more.
Early and accurate detection of mechanical damage in prunes is crucial for preserving postharvest quality and enabling automated sorting. This study proposes a practical and reproducible method for identifying cumulative bruising in prunes using visible–near-infrared (Vis–NIR) reflectance spectroscopy coupled with machine learning techniques. A self-developed impact simulation device was designed to induce progressive damage under controlled energy levels, simulating realistic postharvest handling conditions. Spectral data were collected from the equatorial region of each fruit and processed using a hybrid modeling framework comprising continuous wavelet transform (CWT) for spectral enhancement, uninformative variable elimination (UVE) for optimal wavelength selection, and support vector machine (SVM) for classification. The proposed CWT-UVE-SVM model achieved an overall classification accuracy of 93.22%, successfully distinguishing intact, mildly bruised, and cumulatively damaged samples. Notably, the results revealed nonlinear reflectance variations in the near-infrared region associated with repeated low-energy impacts, highlighting the capacity of spectral response patterns to capture progressive physiological changes. This research not only advances nondestructive detection methods for prune grading but also provides a scalable modeling strategy for cumulative mechanical damage assessment in soft horticultural products. Full article
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13 pages, 788 KiB  
Article
Advancing Kiwifruit Maturity Assessment: A Comparative Study of Non-Destructive Spectral Techniques and Predictive Models
by Michela Palumbo, Bernardo Pace, Antonia Corvino, Francesco Serio, Federico Carotenuto, Alice Cavaliere, Andrea Genangeli, Maria Cefola and Beniamino Gioli
Foods 2025, 14(15), 2581; https://doi.org/10.3390/foods14152581 - 23 Jul 2025
Viewed by 246
Abstract
Gold kiwifruits from two different farms, harvested at different times, were analysed using both non-destructive and destructive methods. A computer vision system (CVS) and a portable spectroradiometer were used to perform non-destructive measurements of firmness, titratable acidity, pH, soluble solids content, dry matter, [...] Read more.
Gold kiwifruits from two different farms, harvested at different times, were analysed using both non-destructive and destructive methods. A computer vision system (CVS) and a portable spectroradiometer were used to perform non-destructive measurements of firmness, titratable acidity, pH, soluble solids content, dry matter, and soluble sugars (glucose and fructose), with the goal of building predictive models for the maturity index. Hyperspectral data from the visible–near-infrared (VIS–NIR) and short-wave infrared (SWIR) ranges, collected via the spectroradiometer, along with colour features extracted by the CVS, were used as predictors. Three different regression methods—Partial Least Squares (PLS), Support Vector Regression (SVR), and Gaussian process regression (GPR)—were tested to assess their predictive accuracy. The results revealed a significant increase in sugar content across the different harvesting times in the season. Regardless of the regression method used, the CVS was not able to distinguish among the different harvests, since no significant skin colour changes were measured. Instead, hyperspectral measurements from the near-infrared (NIR) region and the initial part of the SWIR region proved useful in predicting soluble solids content, glucose, and fructose. The models built using these spectral regions achieved R2 average values between 0.55 and 0.60. Among the different regression models, the GPR-based model showed the best performance in predicting kiwifruit soluble solids content, glucose, and fructose. In conclusion, for the first time, the effectiveness of a fully portable spectroradiometer measuring surface reflectance until the full SWIR range for the rapid, contactless, and non-destructive estimation of the maturity index of kiwifruits was reported. The versatility of the portable spectroradiometer may allow for field applications that accurately identify the most suitable moment to carry out the harvesting. Full article
(This article belongs to the Section Food Quality and Safety)
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25 pages, 11642 KiB  
Article
Non-Invasive Estimation of Crop Water Stress Index and Irrigation Management with Upscaling from Field to Regional Level Using Remote Sensing and Agrometeorological Data
by Emmanouil Psomiadis, Panos I. Philippopoulos and George Kakaletris
Remote Sens. 2025, 17(14), 2522; https://doi.org/10.3390/rs17142522 - 20 Jul 2025
Viewed by 448
Abstract
Precision irrigation plays a crucial role in managing crop production in a sustainable and environmentally friendly manner. This study builds on the results of the GreenWaterDrone project, aiming to estimate, in real time, the actual water requirements of crop fields using the crop [...] Read more.
Precision irrigation plays a crucial role in managing crop production in a sustainable and environmentally friendly manner. This study builds on the results of the GreenWaterDrone project, aiming to estimate, in real time, the actual water requirements of crop fields using the crop water stress index, integrating infrared canopy temperature, air temperature, relative humidity, and thermal and near-infrared imagery. To achieve this, a state-of-the-art aerial micrometeorological station (AMMS), equipped with an infrared thermal sensor, temperature–humidity sensor, and advanced multispectral and thermal cameras is mounted on an unmanned aerial system (UAS), thus minimizing crop field intervention and permanently installed equipment maintenance. Additionally, data from satellite systems and ground micrometeorological stations (GMMS) are integrated to enhance and upscale system results from the local field to the regional level. The research was conducted over two years of pilot testing in the municipality of Trifilia (Peloponnese, Greece) on pilot potato and watermelon crops, which are primary cultivations in the region. Results revealed that empirical irrigation applied to the rhizosphere significantly exceeded crop water needs, with over-irrigation exceeding by 390% the maximum requirement in the case of potato. Furthermore, correlations between high-resolution remote and proximal sensors were strong, while associations with coarser Landsat 8 satellite data, to upscale the local pilot field experimental results, were moderate. By applying a comprehensive model for upscaling pilot field results, to the overall Trifilia region, project findings proved adequate for supporting sustainable irrigation planning through simulation scenarios. The results of this study, in the context of the overall services introduced by the project, provide valuable insights for farmers, agricultural scientists, and local/regional authorities and stakeholders, facilitating improved regional water management and sustainable agricultural policies. Full article
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10 pages, 1632 KiB  
Article
An Ultra-Narrowband Graphene-Perfect Absorber Based on Bound States in the Continuum of All-Dielectric Metasurfaces
by Qi Zhang, Xiao Zhang, Zhihong Zhu and Chucai Guo
Nanomaterials 2025, 15(14), 1124; https://doi.org/10.3390/nano15141124 - 19 Jul 2025
Viewed by 329
Abstract
Enhancing light absorption in two-dimensional (2D) materials, particularly few-layer structures, is critical for advancing optoelectronic devices such as light sources, photodetectors, and sensors. However, conventional absorption enhancement strategies often suffer from unstable resonant wavelengths and low-quality factors (Q-factors) due to the inherent weak [...] Read more.
Enhancing light absorption in two-dimensional (2D) materials, particularly few-layer structures, is critical for advancing optoelectronic devices such as light sources, photodetectors, and sensors. However, conventional absorption enhancement strategies often suffer from unstable resonant wavelengths and low-quality factors (Q-factors) due to the inherent weak light–matter interactions in 2D materials. To address these limitations, we propose an all-dielectric metasurface graphene-perfect absorber based on toroidal dipole bound state in the continuum (TD-BIC) with an ultra-narrow bandwidth and stable resonant wavelength. The proposed structure achieves tunable absorption linewidths spanning three orders of magnitude (6 nm to 0.0076 nm) through critical coupling modulation. Furthermore, the operational wavelength can be flexibly extended to any near-infrared region by adjusting the grating width. This work establishes a novel paradigm for enhancing the absorption of 2D materials in photonic device applications. Full article
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18 pages, 11678 KiB  
Article
Inclusions, Chemical Composition, and Spectral Characteristics of Pinkish-Purple to Purple Spinels from Mogok, Myanmar
by Danyu Guo, Geng Li, Liqun Weng, Meilun Zhang and Fabian Dietmar Schmitz
Crystals 2025, 15(7), 659; https://doi.org/10.3390/cryst15070659 - 19 Jul 2025
Viewed by 217
Abstract
With the increasing market demand for spinels of various colors, purple spinel—long regarded as a symbol of nobility—has attracted growing attention. In this study, pinkish-purple to purple spinels from the Mogok region of Myanmar were systematically examined using conventional gemological, spectroscopic, and chemical [...] Read more.
With the increasing market demand for spinels of various colors, purple spinel—long regarded as a symbol of nobility—has attracted growing attention. In this study, pinkish-purple to purple spinels from the Mogok region of Myanmar were systematically examined using conventional gemological, spectroscopic, and chemical analytical techniques. Raman analysis reveals that these spinels commonly contain octahedral inclusions composed of calcite, dolomite, magnesite, and graphite. Chemically, the samples are primarily magnesia-alumina spinels. Color variation is influenced by trace elements: increasing Cr and V contents enhance the red hue, while higher Fe concentrations intensify the purple tone. UV–Vis spectra show that Cr3+ and V3+ jointly contribute to absorptions at 388 nm and 548 nm, with Fe2+ and Fe3+ responsible for the bands at 371 nm and 457 nm, respectively, together controlling the pink-to-purple color variation. Most samples display four Cr3+-related peaks near 700 nm; however, these are absent in deeply purple spinels. In contrast, light pink spinels show weaker absorption at 371 nm and 457 nm, attributed to Fe2+ and Fe3+. Fluorescence spectra confirm characteristic Cr3+ emission bands at 673 nm, 684 nm, 696 nm, 706 nm, and 716 nm, indicating a strong crystal field environment. Raman spectra have peaks mainly around 312 cm−1, 406 cm−1, 665 cm−1, and 768 cm−1. The peaks of the infrared spectrum mainly appear around 840 cm−1, 729 cm−1, 587 cm−1, 545 cm−1, and 473 cm−1. Full article
(This article belongs to the Collection Topic Collection: Mineralogical Crystallography)
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19 pages, 3374 KiB  
Article
The Influence of Viewing Geometry on Hyperspectral-Based Soil Property Retrieval
by Yucheng Gao, Lixia Ma, Zhongqi Zhang, Xianzhang Pan, Ziran Yuan, Changkun Wang and Dongsheng Yu
Remote Sens. 2025, 17(14), 2510; https://doi.org/10.3390/rs17142510 - 18 Jul 2025
Viewed by 225
Abstract
Hyperspectral technology has been widely applied to the retrieval of soil properties, such as soil organic matter (SOM) and particle size distribution (PSD). However, most previous studies have focused on hyperspectral data acquired from the nadir direction, and the influence of viewing geometry [...] Read more.
Hyperspectral technology has been widely applied to the retrieval of soil properties, such as soil organic matter (SOM) and particle size distribution (PSD). However, most previous studies have focused on hyperspectral data acquired from the nadir direction, and the influence of viewing geometry on hyperspectral-based soil property retrieval remains unclear. In this study, bidirectional reflectance factors (BRFs) were collected at 48 different viewing angles for 154 soil samples with varying SOM contents and PSDs. SOM and PSD were then retrieved using combinations of ten spectral preprocessing methods (raw reflectance, Savitzky–Golay filter (SG), first derivative (D1), second derivative (D2), standard normal variate (SNV), multiplicative scatter correction (MSC), SG + D1, SG + D2, SG + SNV, and SG + MSC), one sensitive wavelength selection method, and three retrieval algorithms (partial least squares regression (PLSR), support vector machine (SVM), and convolutional neural networks (CNNs)). The influence of viewing geometry on the selection of spectral preprocessing methods, retrieval algorithms, sensitive wavelengths, and retrieval accuracy was systematically analyzed. The results showed that soil BRFs are influenced by both soil properties and viewing angles. The viewing geometry had limited effects on the choice of preprocessing method and retrieval algorithm. Among the preprocessing methods, D1, SG + D1, and SG + D2 outperformed the others, while PLSR achieved a higher accuracy than SVM and CNN when retrieving soil properties. The selected sensitive wavelengths for both SOM and PSD varied slightly with viewing angle and were mainly located in the near-infrared region when using BRFs from multiple viewing angles. Compared with single-angle data, multi-angle BRFs significantly improved retrieval performance, with the R2 increasing by 11% and 15%, and RMSE decreasing by 16% and 30% for SOM and PSD, respectively. The optimal viewing zenith angle ranged from 10° to 20° for SOM and around 40° for PSD. Additionally, backward viewing directions were more favorable than forward directions, with the optimal viewing azimuth angles being 0° for SOM and 90° for PSD. These findings provide useful insights for improving the accuracy of soil property retrieval using multi-angle hyperspectral observations. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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22 pages, 10488 KiB  
Article
Morphological and Functional Evolution of Amorphous AlN Thin Films Deposited by RF-Magnetron Sputtering
by Maria-Iulia Zai, Ioana Lalau, Marina Manica, Lucia Chiriacescu, Vlad-Andrei Antohe, Cristina C. Gheorghiu, Sorina Iftimie, Ovidiu Toma, Mirela Petruta Suchea and Ștefan Antohe
Surfaces 2025, 8(3), 51; https://doi.org/10.3390/surfaces8030051 - 17 Jul 2025
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Abstract
Aluminum nitride (AlN) thin films were deposited on SiO2 substrates by RF-magnetron sputtering at varying powers (110–140 W) and subsequently subjected to thermal annealing at 450 °C under nitrogen atmosphere. A comprehensive multi-technique investigation—including X-ray reflectometry (XRR), X-ray diffraction (XRD), scanning electron [...] Read more.
Aluminum nitride (AlN) thin films were deposited on SiO2 substrates by RF-magnetron sputtering at varying powers (110–140 W) and subsequently subjected to thermal annealing at 450 °C under nitrogen atmosphere. A comprehensive multi-technique investigation—including X-ray reflectometry (XRR), X-ray diffraction (XRD), scanning electron microscopy (SEM), atomic force microscopy (AFM), optical profilometry, spectroscopic ellipsometry (SE), and electrical measurements—was performed to explore the physical structure, morphology, and optical and electrical properties of the films. The analysis of the film structure by XRR revealed that increasing sputtering power resulted in thicker, denser AlN layers, while thermal treatment promoted densification by reducing density gradients but also induced surface roughening and the formation of island-like morphologies. Optical studies confirmed excellent transparency (>80% transmittance in the near-infrared region) and demonstrated the tunability of the refractive index with sputtering power, critical for optoelectronic applications. The electrical characterization of Au/AlN/Al sandwich structures revealed a transition from Ohmic to trap-controlled space charge limited current (SCLC) behavior under forward bias—a transport mechanism frequently present in a material with very low mobility, such as AlN—while Schottky conduction dominated under reverse bias. The systematic correlation between deposition parameters, thermal treatment, and the resulting physical properties offers valuable pathways to engineer AlN thin films for next-generation optoelectronic and high-frequency device applications. Full article
(This article belongs to the Special Issue Surface Engineering of Thin Films)
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