Search Results (321)

This study investigated the effects of progressive step-heating (300–500 °C) and direct heating (500 °C) on pinkish-orange and red tourmalines using colorimetric, spectroscopic, and chemical analyses. Heating progressively modified visible absorption and color, with treatment at 500 °C generally increasing lightness (L*) and substantially decreasing chroma (C*), accompanied by weakening of the broad absorption near 520 nm while strong pleochroism was retained. Chemical analyses revealed substantial compositional variability, particularly in Mn and Fe, although elemental abundance alone did not account for the observed optical responses. Color evolution was associated with changes in overlapping electronic absorption features, but the underlying microscopic processes could not be uniquely assigned to specific transition-metal ions or oxidation-state changes. Fourier transform infrared (FTIR) spectroscopy showed preservation of the principal framework-related vibrational features up to 500 °C, with no evidence of major structural disruption. Within the present step-heating series, 400 °C produced an intermediate outcome characterized by measurable lightening and desaturation while retaining more of the original pink-to-red chromatic component than after treatment at 500 °C, which produced substantially greater desaturation. Full article

Afghanistan’s Panjshir Valley is an important emerald-producing region in Asia. In this study, emeralds from Kamar Safid in Southeastern Panjshir were investigated by Fourier-transform infrared (FTIR), Raman spectroscopy, ultraviolet–visible–near-infrared (UV-Vis-NIR) spectroscopy, and laser ablation–inductively coupled plasma–mass spectrometry (LA-ICP-MS). These Kamar Safid emeralds are generally small, light-green-to-green crystals. Microscopic observations revealed abundant acicular and tubular three- or two-phase fluid inclusions, with transparent feldspar-group mineral inclusions. Solid phases in the fluid inclusions commonly consist of carbonate crystals or several transparent halite daughter crystals. FTIR spectra of samples indicated that the absorption of type II H₂O was higher than type I H₂O in the emeralds from Kamar Safid. The UV-Vis-NIR spectra are characterized by Cr- and V-related absorption bands, which are stronger than Fe-related absorptions. LA-ICP-MS results indicate slightly higher V contents and lower Cr contents than emeralds from other Panjshir mining areas. The relatively low total Cr and V contents of Kamar Safid emeralds account for the overall lighter color, suggesting that Cr and V are the principal chromophores, whereas Fe secondarily modifies hue. Rb, Cs, and Sc contents are 6.2–24.3 ppm, 11.9–141.6 ppm, and 92–1461 ppm, with total alkali contents of 4903.10–14,257.18 ppm. Cs-Rb, Cs-Sc, Li-Cs, and Li-Sc binary logarithmic diagrams indicate enrichment in Sc and Rb and depletion in Li and Cs. Full article

Oxygen-related absorption features provide fundamental constraints for passive atmospheric remote sensing in the reflected-solar spectrum. Because molecular oxygen is well-mixed in the dry atmosphere, O₂ absorption links measured radiance to atmospheric mass, pressure, and effective photon path length, while O₂-O₂ (O₄) collision-induced absorption provides complementary sensitivity to lower-tropospheric photon paths. This review synthesizes the spectroscopic basis, radiative-transfer mechanisms, satellite implementations, retrieval algorithms, and atmospheric applications of O₂ and O₄ measurements from the ultraviolet to the shortwave infrared. Particular emphasis is placed on the O₂ B-band near 687 nm, the O₂ A-band near 760 nm, O₄ bands in the UV–visible range, and the O₂ band near 1.27 µm. These features support retrievals of cloud fraction, cloud pressure, optical centroid pressure, aerosol layer height, surface pressure, dry-air column abundance, and light-path corrections for greenhouse gas observations. We review major algorithmic approaches, including cloud-as-reflecting-boundary models, cloud-as-layer models, DOAS-based retrievals, optimal-estimation frameworks, photon path-length distribution methods, and machine learning or hybrid techniques. Key applications include cloud climatology, aerosol vertical characterization, air mass factor correction, XCO₂ and XCH₄ retrievals, carbon-cycle studies, and multi-mission data integration. Remaining challenges include spectroscopic uncertainty, aerosol and cloud scattering degeneracy, surface bidirectional reflectance, three-dimensional radiative-transfer effects, wavelength-dependent path mismatch, and inconsistent uncertainty characterization. Future progress will depend on improved spectroscopy, active–passive validation, multi-angle polarimetry, physically constrained machine learning, and harmonized multi-mission retrieval frameworks. Full article

Accurate identification of mango varieties holds substantial significance for the elevation of product added value and the facilitation of market differentiation through quality-based pricing. Near-infrared (NIR) spectral analysis offers a rapid, non-destructive solution for mango variety identification. To address the challenges in fine-grained classification of NIR spectra, namely, high spectral similarity and severe overlap of absorption peaks, which make it difficult to extract nonlinear features using chemometrics, as well as the excessive complexity of existing deep learning models, a lightweight multi-scale spatial global context network is proposed. One-dimensional NIR spectra are converted into two-dimensional images through the Gramian angular difference field. Multi-scale partial convolution, coordinate-aware global context, efficient multi-scale attention, and structural re-parameterization are integrated to capture local spectral features and long-range band correlations effectively. Evaluated on two mango spectral datasets with different distributions, the proposed model achieves variety identification accuracies of 99.46% and 97.83%, with only 19.08 M parameters. Computational complexity, throughput, and latency reach 120.29 M FLOPs, 2848.5 FPS, and 0.351 ms, respectively, realizing a balance between classification accuracy and computational speed. Ablation and robustness experiments demonstrate that the accuracy of the model is improved by 5.91% and 2.15% compared with one-dimensional convolutional neural network and FasterNet, respectively. Important wavelengths obtained by threshold screening of activation maps exhibit consistency with the majority of conclusions from analysis of variance and VIP methods, while the remainder represent newly identified important bands. Validation across different temperature and batch scenarios reveals strong generalization capability. Future refinement will be pursued through increased sample diversity. Overall, high-precision identification is attained by the model at comparatively low computational overhead, indicating potential for advancing the practical application of NIR spectroscopy in agricultural quality inspection. Full article

Plant secondary metabolites play important roles in plant defense, environmental adaptation, and ecosystem functioning, yet large-scale monitoring of foliar phenolics remains limited because of the high cost and restricted spatial coverage of airborne imaging spectroscopy and the limited spectral resolution of multispectral satellites. This study explored a cross-scale remote sensing framework to map foliar phenolics through the synergy of airborne imaging spectroscopy and Sentinel-2 multispectral imagery. Foliar samples were collected from 634 plots across seven National Ecological Observatory Network (NEON) ecological domains in the United States, representing six plant functional types. Community-weighted mean foliar phenolic concentrations were linked with NEON Airborne Observation Platform (AOP) imaging spectroscopy to develop phenolic retrieval models using partial least squares regression (PLSR) and Gaussian process regression (GPR). The optimized airborne-derived phenolics were subsequently aggregated across multiple spatial windows and used as reference data to train Sentinel-2 models using PLSR, random forest regression (RFR), and GPR. Both airborne hyperspectral models achieved strong predictive performance, with comparable accuracy between PLSR (R² = 0.770, RMSE = 16.11 mg·g⁻¹) and GPR (R² = 0.771, RMSE = 16.16 mg·g⁻¹). However, PLSR showed substantially lower predictive uncertainty (4.62 mg·g⁻¹) than GPR (12.58 mg·g⁻¹), indicating more stable predictions across NEON samples. Spectral importance analysis identified consistent phenolic-sensitive wavelength regions in the visible and shortwave infrared domains, particularly near previously reported absorption features. For Sentinel-2 upscaling, prediction accuracy increased consistently with larger spatial aggregation windows, indicating improved agreement between Sentinel-2 observations and airborne-derived phenolics through reduced spatial scale mismatch and geolocation misalignment. Among the evaluated approaches, RFR achieved the best performance, improving from R² = 0.479 at the 10-pixel window to R² = 0.776 (NRMSE = 7.0%) at the 100-pixel window. Feature importance analysis showed increasing contributions of red-edge and shortwave infrared information at larger aggregation scales. Spatial comparisons demonstrated that Sentinel-2 successfully reproduced major phenolic distribution patterns observed by airborne imaging spectroscopy. These results demonstrate that airborne imaging spectroscopy can effectively bridge field observations and satellite multispectral imagery for foliar phenolics estimation and highlight the potential of Sentinel-2 as a scalable approach for monitoring vegetation chemical traits across heterogeneous ecosystems. Full article

Near-infrared (NIR) spectroscopy with full-spectrum chemometric modeling is widely used in food, agricultural, and pharmaceutical analysis, but calibrations resting on hundreds to thousands of spectral variables are difficult to audit and require full-spectrum instrumentation to deploy. The Spectral Contrast Feature (SCF) framework constructs predictive features as differences between the mean intensities of paired spectral bins, with bin positions, widths, and feature count optimized by a genetic algorithm. SCF-PLSR was evaluated on cocoa bean moisture (n = 72), barley adulteration in roasted coffee (n = 158), wheat grain protein (n = 496), and the IDRC 2002 pharmaceutical tablet shoot-out dataset, against full-spectrum PLSR and four established wavelength-selection methods under repeated evaluation. Using three to seven contrast features in place of 601 to 1559 spectral variables, SCF-PLSR matched or exceeded every comparator on same-instrument prediction. Test-set RMSE fell by 25% for coffee–barley and 15% for wheat protein. On the tablet dataset under second-derivative preprocessing, zero-shot transfer to a second instrument gave RMSE 17% lower than full-spectrum PLSR. Selected features mapped onto established NIR absorption regions, indicating that a calibration built on a few chemically assignable contrasts is both auditable and compatible with targeted, reduced-cost instrumentation. Full article

Copper-deficient copper selenide (Cu_2−xSe) nanocrystals possess strong near-infrared (NIR) absorption and efficient photothermal conversion, making them attractive candidates for photoacoustic imaging. In this study, Cu_2−xSe nanocrystals with distinct morphologies were synthesized using different selenium precursors and evaluated as photoacoustic contrast agents. Se–oleylamine precursors produced predominantly disk-shaped nanocrystals with average dimensions of approximately 20 nm in diameter and 5 nm in thickness, while Se–TOP/TOPO precursors yielded smaller spherical nanocrystals. Structural characterization by transmission electron microscopy, high-resolution TEM, and selected-area electron diffraction confirmed the formation of highly crystalline copper-deficient Cu_2−xSe nanocrystals with a face-centered cubic crystal structure. UV–Vis–NIR spectroscopy revealed broad optical absorption extending into the NIR region, with morphology-dependent spectral characteristics. Multispectral optoacoustic tomography demonstrated strong photoacoustic signal generation from both nanodisks and nanospheres over a broad wavelength range. In vivo studies using PEGylated Cu_2−xSe nanospheres showed successful lymphatic uptake following hind paw injection and enabled visualization of the draining popliteal lymph node through spectral unmixing of nanoparticle and hemoglobin signals. These results demonstrate that Cu_2−xSe nanocrystals are promising photoacoustic contrast agents for lymphatic imaging and other biomedical imaging applications. Full article

Mid-infrared spectroscopy provides molecular fingerprint information for bacterial biofilm analysis, but the absorption signal of a thin biofilm layer is usually weak. In this work, a metasurface-enhanced mid-infrared biosensor was designed to enhance the fingerprint response of Staphylococcus aureus biofilms. The biofilm transmission spectrum was measured by Fourier-transform infrared spectroscopy, and the film thickness was obtained by atomic force microscopy using an edge step-height method. Based on these measurements, an effective extinction coefficient was extracted and used in finite-difference time-domain simulations. A metal–insulator–metal metasurface was then optimized to cover the main biofilm absorption bands in the mid-infrared region. Two resonator designs were studied: a polarization-dependent structure and a polarization-insensitive structure. The polarization-dependent design showed a strong response under x-polarized incidence and weak coupling under y-polarized incidence. The polarization-insensitive design provided a more balanced response for orthogonal polarizations. At the selected biofilm fingerprint wavelengths, the highest enhancement factors reached 8.57 and 7.24 for the polarization-dependent and polarization-insensitive structures, respectively. Near-field distributions confirmed that the enhancement mainly originated from localized electric fields at the metal resonator edges. These results provide a proof-of-concept design strategy for enhancing weak mid-infrared fingerprint signals from S. aureus biofilms. Full article

In this study, a series of xFe₂O₃–Na₂O–CaO–SiO₂ glass-ceramics containing 0–28 wt.% Fe₂O₃ were prepared via the conventional melt-quenching technique. The resulting samples, designated S1–S4, were subsequently subjected to thermal treatment and investigated with respect to their structural, thermal, and optical characteristics. Differential scanning calorimetry analysis revealed the influence of the Fe₂O₃ concentration on the glass transition temperature and crystallization behavior of the samples. Structural analysis of the samples revealed that crystalline silicate and iron oxide phases constituted the predominant crystalline phases, with their overall crystallinity being strongly dependent on the Fe₂O₃ content and thermal treatment. Fourier transform infrared spectroscopy evidenced structural modifications of the silicate network induced by iron incorporation, while ultraviolet–visible–near infrared spectroscopy highlighted the presence of Fe²⁺/Fe³⁺ ions and their associated electronic transitions. The results indicate that increasing the Fe₂O₃ content significantly affects the network structure, redox state, and thermal behavior of the glass-ceramic system, leading to enhanced absorption properties. Full article

The development of transparent multifunctional coatings capable of combining optical properties with mechanical durability remains a significant challenge in advanced materials engineering. In this study, novel hybrid coatings based on a poly(vinyl butyral)/epoxy resin (PVB/epoxy) matrix reinforced with tungsten disulfide (WS₂) nanostructures were developed and examined for potential application in camouflage protection of glass surfaces. Camouflage aims to reduce the detectability of an object by minimizing the optical contrast between the object and its surrounding environment. For transparent substrates such as glass, this objective is particularly demanding because the transparency must be preserved while reducing unwanted surface reflection and optical signatures over relevant spectral ranges. For this purpose, in this research two types of nanostructures were investigated: fullerene-like nanoparticles (IF-WS₂) and inorganic nanotubes (INT-WS₂). The coatings were fabricated via ultrasonically assisted solution dispersion followed by casting over the glass plates and Teflon molds, and solvent evaporation. Structural, thermal, optical, and mechanical properties were systematically evaluated using SEM, FTIR, DSC, UV-Vis-NIR spectroscopy, gloss measurements, hardness testing, and cavitation wear resistance analysis. The incorporation of WS₂ nanostructures led to improved mechanical performance, with increased hardness and enhanced resistance to cavitation-induced wear. Optical characterization showed moderate reductions in reflectance and controlled transmittance in the visible and near-infrared regions, while overall transparency was maintained. The results indicate that WS₂ nanostructures contribute to both light scattering and absorption, leading to reduced specular reflection and improved optical masking potential. The findings demonstrate that hybrid PVB/epoxy/WS₂ coatings offer a promising approach for designing transparent, mechanically resistant coatings with tunable optical properties, with potential applications in protective glass systems and advanced functional surfaces. Full article

Traditional methods for wine bottle packaging leakage detection often suffer from low efficiency, high false-positive rates, or an inability to detect micro-leakages. This paper proposes a near-infrared laser leakage detection system based on tunable diode laser absorption spectroscopy at 1392 nm, combined with a LightGBM machine learning model. The system detects gaseous ethanol vapor escaping from leaking bottles, addressing the spectral interference caused by ambient water vapor. A total of 1410 samples were collected, and each raw 2000-point spectral contour was compressed into a 200-dimensional feature vector through baseline correction, Z-score normalization, and uniform down-sampling. A two-stage hyperparameter optimization strategy yielded the optimal LightGBM configuration with a 5-fold cross-validation. For the binary classification task, the model achieved an AUC of 0.9949 and an inference speed of 0.0058 ms per sample on a CPU, outperforming Random Forest, PLS, and four deep learning models. For the regression task, the model achieved an R² of 0.5854 ± 0.0919. An anti-interference experiment on 422 samples under varying flow rates, temperatures, and commercial wine types confirmed the model’s robustness, achieving an overall accuracy of 0.94 and an alcohol recall of 0.99. To further validate the system under realistic conditions, a simulated micro-leakage test was conducted using a negative-pressure extraction method: 320 samples were collected from artificially damaged commercial wine bottles placed in a custom-built acrylic vacuum chamber that replicates the production line enclosure. The model achieved an accuracy of 0.95 with zero false negatives. The complete detection cycle takes no more than 5 s per bottle, enabling non-destructive, rapid, and online packaging integrity assessment. The results demonstrate that the proposed system provides a low-cost and reliable solution for wine bottle leakage detection suitable for industrial deployment. Full article

Dendrobium crepidatum Lindl. et Paxt. is a medicinal Dendrobium species whose quality and market value may vary with geographic origin, making rapid origin traceability important for batch management, market supervision, and application promotion. This study used near-infrared spectroscopy (NIRS) combined with multivariate analysis and machine learning to discriminate the origin of D. crepidatum. Fifty batches of stem samples from Yunnan, Guangxi, and Guizhou, China, were analyzed after Savitzky-Golay smoothing, standard normal variate transformation, and first-derivative preprocessing. Principal component analysis (PCA) showed origin-related spectral variation, and a three-class partial least squares-discriminant analysis (PLS-DA) model achieved a mean cross-validated accuracy of 70.2% with a significant permutation-test result (p = 0.0020). Six machine learning algorithms, including KNN, CART, RF, NB, LDA, and ANN, were further compared using repeated nested cross-validation. KNN performed best, with an accuracy of 0.811 ± 0.029 and a macro F1-score of 0.813 ± 0.029, followed by RF (0.804 ± 0.038 and 0.805 ± 0.037, respectively). Key spectral variables were mainly located at 4231–4235 and 5523–5624 cm⁻¹ corresponding mainly to C-H-dominated overtone or combination absorptions with possible C-O/O-H-related contributions from carbohydrates, polysaccharides, phenolics, flavonoids, and other organic constituents. These results demonstrate the feasibility of NIRS combined with machine learning for preliminary origin traceability of D. crepidatum and provide spectral clues for future investigation of origin-related chemical variation and quality discrimination. Full article

Photothermal-assisted electrocatalysis is an effective approach to enhance the efficiency of the oxygen evolution reaction (OER), but the synergistic mechanism between the photothermal effect and the regulation of catalyst electronic structure remains unclear. This work reports the construction of NiFeS/Cs_0.32WO₃ heterostructures, which integrate interfacial electron transfer and localized surface plasmon resonance (LSPR)-induced photothermal effects to enhance OER performance. The Cs_0.32WO₃ component with hexagonal tungsten bronze structure exhibits strong absorption in the near-infrared region, attributed to LSPR (1100 nm to 2500 nm) and small polaron transition (780 nm to 1100 nm), endowing the NiFeS/Cs_0.32WO₃ composite with excellent photothermal conversion capability. Under 808 nm laser irradiation, the steady-state surface temperature of the heterostructure reaches 65.1 °C. X-ray photoelectron spectroscopy and ultraviolet photoelectron spectroscopy analyses reveal that spontaneous electron transfer from NiFeS to Cs_0.32WO₃ occurs at the heterostructure interface, thereby optimizing the electronic structure of active sites. Electrochemical measurements demonstrate that at a current density of 50 mA cm⁻², the NiFeS/Cs_0.32WO₃ composite exhibits an overpotential of 301 mV under near-infrared irradiation, representing a reduction of 53 mV compared to NiFeS under dark conditions. At a current density of 50 mA cm⁻², the photothermal enhancement effect of the NiFeS/Cs_0.32WO₃ composite is identified as the predominant contributor to the overall performance improvement. Nevertheless, the intrinsic interfacial effect associated with the heterojunction also plays a crucial role and makes a non-negligible contribution to the enhanced electrocatalytic activity. The Tafel slope decreases from 57.8 mV dec⁻¹ to 44.5 mV dec⁻¹ under near-infrared illumination, indicating accelerated OER kinetics. This work elucidates the mechanism of synergistic enhancement between heterostructure construction and photothermal effects, providing insights for the design of advanced photothermal electrocatalysts. Full article

Near-infrared spectroscopy (NIRS) is widely used for rapid and non-destructive evaluation of feed nutritional quality, but robust calibration remains challenging for heterogeneous multi-product feed datasets. This study evaluated convolutional neural network (CNN)-based models for predicting crude protein (CP) and acid detergent fiber (ADF) using a previously published NIR database containing forage and grain-based feeds. A one-dimensional CNN and two hybrid models, CNN combined with partial least squares regression (CNN+PLS) and XGBoost (CNN+XGBoost), were developed and compared with conventional PLSR calibration models based on either the pooled multi-product dataset or product-specific subsets. Model performance was assessed using an independent internal hold-out test set generated within the same database. For CP prediction, CNN-based models achieved strong performance on the hold-out test set, with testing R² values of 0.98 and RMSEP values of 0.60–0.62, showing a clear reduction in prediction error compared with the global PLSR model. For ADF, CNN and CNN+PLS provided only modest improvements over global PLSR, whereas CNN+XGBoost showed weaker generalization for ADF. Product-wise results further indicated that ADF prediction was more strongly affected by feed matrix and product category than CP prediction. Grad-CAM examples suggested that CNN activation patterns were broadly consistent with known protein- and fiber-related absorption regions, although this interpretation should be regarded as illustrative evidence of spectral coherence rather than direct chemical causality. Overall, CNN-based models, particularly CNN+PLS, showed promise for improving NIRS prediction of CP in heterogeneous feed datasets, while their advantage for ADF was limited. Further validation using independent external datasets and multi-instrument conditions is required before routine implementation. Full article

Zinc germanium phosphide (ZnGeP₂) is an important nonlinear crystal for mid-infrared conversion, but its performance is limited by residual absorption and intrinsic impurity phases. In this study, polycrystalline ZnGeP₂ was synthesized by a modified two-temperature method, purified by inclined directional recrystallization for up to three cycles, and then grown into single crystals by the vertical Bridgman method. The resulting material was examined by shadow-projection imaging, transmission spectroscopy in the 650–2500 nm range, absorption measurements at 2.097 µm, laser-induced damage threshold (LIDT) testing, and powder X-ray diffraction. Repeated purification improved optical homogeneity and near-infrared transparency, while the absorption coefficient at 2.097 µm decreased from 0.45 to 0.30 cm⁻¹ after three purification cycles. Semi-quantitative PXRD analysis showed progressive suppression of intrinsic impurity phosphides, with phase purity increasing from 86.31% after the first cycle to 95.995% after the second and reaching 100% after the third within the detection limit of the method. However, the LIDT decreased with increasing purification number, indicating a trade-off between lower optical losses and damage resistance. These results demonstrate that inclined directional recrystallization is an effective pre-growth purification route for ZnGeP₂ and that the optimal number of purification cycles should be selected according to the intended application. Full article