Search Results (4,739)

Rising volumes of textile waste necessitate the development of more efficient recycling systems, with a primary focus on the optimization of sorting technologies. Near-infrared (NIR) spectroscopy is a state-of-the-art method for fiber identification; however, its accuracy in quantifying textile blends, particularly common polyester/cotton blend textiles, still requires refinement. This study explores the potential and limitations of NIR spectroscopy for quantifying cotton content in post-consumer textiles. A lab-scale NIR sorter and a handheld NIR spectrometer in complementary wavelength ranges were applied to a diverse range of post-consumer textile samples to test model accuracies. Results show that the commonly assumed 10% accuracy threshold in industrial sorting can be exceeded, especially when excluding textiles with <35% cotton content. Identifying and excluding the range of non-linearity significantly improved the model’s performance. The final models achieved an RMSEP of 6.6% and bias of −0.9% for the NIR sorter and an RMSEP of 3.1% and bias of −0.6% for the handheld NIR spectrometer. This study also assessed how textile characteristics—such as color, structure, product type, and alkaline treatment—affect spectral behavior and model accuracy, highlighting their importance for refining quantification when high-purity inputs are needed. By identifying current limitations and potential sources of errors, this study provides a foundation for improving NIR-based models. Full article

The Junggar Basin contains a large amount of coal resources and is an important coal production base in China. The coal seam in Zhundong coalfield has a large single-layer thickness and high content of inertinite, but large particle Fe-sulphide minerals are associated with coal seams in some mining areas. A series of economic and environmental problems caused by the combustion of large-grained Fe-sulphide minerals in coal have seriously affected the economic, clean and efficient utilization of coal. In this paper, the ultra-thick coal seam of the Xishanyao formation in the Yihua open-pit mine of the Zhundong coalfield is taken as the research object. Through the analysis of coal quality, X-ray fluorescence spectrometer test of major elements in coal, inductively coupled plasma mass spectrometry test of trace elements, SEM-Raman identification of Fe-sulphide minerals in coal and LA-MC-ICP-MS test of sulfur isotope of marcasite, the coal quality characteristics, main and trace element characteristics, macro and micro occurrence characteristics of Fe-sulphide minerals and sulfur isotope characteristics of marcasite in the ultra-thick coal seam of the Xishanyao formation are tested. On this basis, the occurrence state and genesis of large particle Fe-sulphide minerals in the ultra-thick coal seam of the Xishanyao formation are clarified. The main results and understandings are as follows: (1) the occurrence state of Fe-sulphide minerals in extremely thick coal seams is clarified. The Fe-sulphide minerals in the extremely thick coal seam are mainly marcasite, and concentrated in the YH-2, YH-3, YH-8, YH-9, YH-14, YH-15 and YH-16 horizons. Macroscopically, Fe-sulphide minerals mainly occur in three forms: thin film Fe-sulphide minerals, nodular Fe-sulphide minerals, and disseminated Fe-sulphide minerals. Microscopically, they mainly occur in four forms: flake, block, spearhead, and crack filling. (2) The difference in sulfur isotope of marcasite was discussed, and the formation period of marcasite was preliminarily divided. The overall variation range of the δ³⁴S value of marcasite is wide, and the extreme values are quite different. The polyflake marcasite was formed in the early stage of diagenesis and the δ³⁴S value was negative, while the fissure filling marcasite was formed in the late stage of diagenesis and the δ³⁴S value was positive. (3) The coal quality characteristics of the thick coal seam were analyzed. The organic components in the thick coal seam are mainly inertinite, and the inorganic components are mainly clay minerals and marcasite. (4) The difference between the element content in the thick coal seam of the Zhundong coalfield and the average element content of Chinese coal was compared. The major element oxides in the thick coal seam are mainly CaO and MgO, followed by SiO₂, Al₂O₃, Fe₂O₃ and Na₂O. Li, Ga, Ba, U and Th are enriched in trace elements. (5) The coal-accumulating environment characteristics of the extremely thick coal seam are revealed. The whole thick coal seam is formed in an acidic oxidation environment, and the horizon with Fe-sulphide minerals is in an acidic reduction environment. The acidic reduction environment is conducive to the formation of marcasite and is not conducive to the formation of pyrite. (6) There are many matrix vitrinite, inertinite content, clay content, and terrigenous debris in the extremely thick coal seam. The good supply of peat swamp, suitable reduction environment and pH value, as well as groundwater leaching and infiltration, together cause the occurrence of large-grained Fe-sulphide minerals in the extremely thick coal seam of the Xishanyao formation in the Zhundong coalfield. Full article

Background/Objective: In patients with acute lymphoblastic leukemia (ALL), it has been demonstrated that the treatment has a negative effect on bone health. The n-3 polyunsaturated fatty acids (LCPUFAs-ω3) may attenuate bone resorption. We evaluated the effects of LCPUFAs-ω3, vitamin D, and calcium supplementation on bone turnover markers and changes in vitamin D concentrations during 6 weeks of supplementation and during 6 weeks of post-intervention follow-up in pediatric patients with ALL. Methods: Thirty-six pediatric patients with ALL were randomly assigned to the ω-3VDCa group (100 mg/kg/d LCPUFAs-ω3 + 4000 IU vitamin D + 1000 mg calcium) or the VDCa group (4000 IU vitamin D + 1000 mg calcium) for 6 weeks. Blood samples were collected to determine 25(OH)D, PTH, ICTP, and TRAP-5b (biomarkers of bone resorption) and osteocalcin (OC, a biomarker of bone production) levels at baseline, 6 weeks, and 12 weeks after supplementation. The 25(OH)D analysis was performed using ultra-high-performance liquid chromatography coupled to a mass spectrometer, and PTH and bone turnover markers were measured by ELISA. Results: The 25(OH)D concentration increased in both groups (ω3VDCa group: 19.4 ng/mL vs. 44.0 ng/mL, p < 0.0001; VDCa group: 15.3 ng/mL vs. 42.8 ng/mL, p = 0.018) and remained significantly higher at 12 weeks. At 12 weeks, ICTP showed lower concentrations in the ω-3VDCa group than in the VDCa group (0.74 ng/mL vs. 1.05 ng/mL, p = 0.024). Conclusions: Combined omega-3 and 4000 IU vitamin D supplementation for 6 weeks had a positive effect on bone health, as indicated by serum ICTP, with no effect on serum 25(OH)D levels over vitamin D supplementation alone. Full article

To prolong the service life of asphalt pavement and reduce its maintenance cost, a fiber Bragg grating (FBG) sensor encapsulated in carboxylated carbon nanotube (CNT-COOH)-modified gel material suitable for strain monitoring of asphalt pavement was developed. Through tensile and bending tests, the effects of carboxylated carbon nanotubes on the mechanical properties of gel materials under different dosages were evaluated and the optimal dosage of carbon nanotubes was determined. Infrared spectrometer and scanning electron microscopy were used to compare and analyze the infrared spectra and microstructure of carbon nanotubes before and after carboxyl functionalization and modified gel materials. The results show that the incorporation of CNTs-COOH increased the tensile strength, elongation at break, and tensile modulus of the gel material by 36.2%, 47%, and 17.2%, respectively, and increased the flexural strength, flexural modulus, and flexural strain by 89.7%, 7.5%, and 63.8%, respectively. Through infrared spectrum analysis, it was determined that carboxyl (COOH) and hydroxyl (OH) were successfully introduced on the surface of carbon nanotubes. By analyzing the microstructure, it can be seen that the carboxyl functionalization of CNTs improved the agglomeration of carbon nanotubes. The tensile section of the modified gel material is rougher than that of the pure epoxy resin, showing obvious plastic deformation, and the toughness is improved. According to the data from the calibration experiment, the strain and temperature sensitivity coefficients of the packaged sensor are 1.9864 pm/μm and 0.0383 nm/°C, respectively, which are 1.63 times and 3.61 times higher than those of the bare fiber grating. The results of an applicability study show that the internal structure strain of asphalt rutting specimen changed linearly with the external static load, and the fitting sensitivity is 0.0286 με/N. Combined with ANSYS finite element analysis, it is verified that the simulation analysis results are close to the measured data, which verifies the effectiveness and monitoring accuracy of the sensor. The dynamic load test results reflect the internal strain change trend of asphalt mixture under external rutting load, confirming that the encapsulated FBG sensor is suitable for the long-term monitoring of asphalt pavement strain. Full article

Imaging techniques have revolutionized cultural heritage analysis, particularly for objects that cannot be sampled. This study investigated the utilization of spectral imaging for the identification of pigments in artifacts from the Arxiu Valencià del Disseny, in conjunction with other portable spectroscopy techniques such as XRF, Raman, FT-NIR, and FT-MIR. Four early 1930s watercolors were examined using point-wise elemental and molecular spectroscopic data for pigment classification. Initially, the data cubes obtained with the spectral camera were processed using various methods. The spectral behavior was analyzed pixel-point, and the reflectance curves were qualitatively compared with a set of standards. Subsequently, a computational approach was applied to the data cube to produce RGB, false-color infrared (IRFC), and principal component (PC) images. Algorithms, such as the Vector Angle (VA) mapper, were also employed to map the pigment spectra. Consequently, 19th-century pigments such as Prussian blue, chrome yellow, and alizarin red were distinguished according to their composition, combining the spatial and spectral dimensions of the data. Elemental analysis and infrared spectroscopy supported these findings. In this context, the use of reflectance imaging spectroscopy (RIS), despite its technical limitations, emerged as an essential tool for the documentation and conservation of design heritage. Full article

Background: Melanoma metastasis, driven by tumor microenvironment (TME)-mediated crosstalk facilitated by extracellular vesicles (EVs), remains a major therapeutic challenge. A critical barrier to clinical translation is the overlap in protein cargo between tumor-derived and healthy cell EVs. Objective: To address this, we developed Scaffold-free Functional Deconvolution (SFD), a novel computational approach that leverages a comprehensive healthy cell EV protein database to deconvolute non-oncogenic background signals. Methods: Beginning with 1915 proteins (identified by MS/MS analysis on an Orbitrap Fusion Lumos Mass Spectrometer using the IonStar workflow) from melanoma EVs isolated using REIUS, SFD applies four sequential filters: exclusion of normal melanocyte EV proteins, prioritization of metastasis-linked entries (HCMDB), refinement via melanocyte-specific databases, and validation against TCGA survival data. Results: This workflow identified 21 high-confidence targets implicated in metabolic-associated acidification, immune modulation, and oncogenesis, and were analyzed for reduced disease-free and overall survival. SFD’s versatility was further demonstrated by surfaceome profiling, confirming enrichment of H7-B3 (CD276), ICAM1, and MIC-1 (GDF-15) in metastatic melanoma EV via Western blot and flow cytometry. Meta-analysis using Vesiclepedia and STRING categorized these targets into metabolic, immune, and oncogenic drivers, revealing a dense interaction network. Conclusions: Our results highlight SFD as a powerful tool for identifying clinically relevant biomarkers and therapeutic targets within melanoma EVs, with potential applications in drug development and personalized medicine. Full article

Based on the theory of optical sensing, we propose a high-precision plasmonic refractive index nanosensor, which consists of a symmetric rectangular waveguide and a circular ring containing a rectangular cavity. The designed novel tunable micro-resonant circular cavity filter based on surface plasmon excitations is able to confine light to sub-wavelength dimensions. The data show that different geometrical factors have different effects on sensing, with the geometry of the rectangular cavity and the radius of the circular ring being the key factors affecting the Fano resonance. Furthermore, the resonance bifurcation enables the structure to achieve a tunable dual Fano resonance system. The structure was tuned to obtain optimal sensitivity (S) and figure of merit values up to 3066 nm/RIU and 78. The designed structure has excellent sensing performance with sensitivities of 0.4767 nm·(mg/d${\mathrm{L}}^{-1}$) and 0.6 nm·(mg/d${\mathrm{L}}^{-1}$) in detecting Na⁺ and K⁺ concentrations in the electrolyte solution, respectively, and can be easily achieved by the spectrometer. The wavelength accuracy of 0.001 nm can be easily achieved by a spectrum analyzer, which has a broad application prospect in the field of optical integration. Full article

The spatial heterodyne spectrometer is an interferometric spectrometer specifically designed for particular detection targets, capable of achieving ultra-high spectral resolution within a designated spectral range. As the demand for signal detection accuracy continues to increase, the extraction of accurate target spectra from spatial heterodyne interferograms has become increasingly important. This paper applies a deep neural network to the spectral reconstruction of specific spatial heterodyne interferograms. The spectral reconstruction model, SRDNN, was trained using CO₂ data simulated by the SCIATRAN radiative transfer model and the principles of spatial heterodyne spectroscopy. The results indicate that SRDNN has excellent CO₂ spectral reconstruction performance, with an evaluation index $R^2$ of 0.9943 and an $MSE$ of 0.00021. The average difference between the reconstructed spectra and the target spectra is only 0.371%. Furthermore, the method was further validated using experimental data obtained from a spatial heterodyne spectrometer. The remarkable spectral reconstruction results and excellent evaluation indicators once again demonstrated the universality and effectiveness of the method. Finally, the robustness of the method was studied using noisy experimental data. The results demonstrate that the method can accurately reconstruct spectra from interferograms with slight noise without requiring additional processing, simplifying the spectral reconstruction process. This work is expected to provide novel methods and effective solutions for the spectral reconstruction of specific targets detected by spatial heterodyne spectrometers. Full article

This study evaluated fecal near-infrared spectroscopy (fNIRS) potential to predict three external markers (Yb, Ti, and polyethylene glycol (PEG)) and dry matter digestibility (DMD) calculated from these markers and fiber fractions. A total of 192 fecal samples were collected from 576 Ross 308 male chicks supplemented with TiO₂ (2 g/kg), Yb₂O₃ (50 mg/kg), and PEG (5 g/kg) for 8 d. Reference values for Ti and Yb were obtained using an inductively coupled plasma–optical emission spectrometer, for fiber fractions via ANKOM, and for PEG content using an ad hoc fNIRS model. Prediction models were developed in external validation with 25% of the samples. Good and fair prediction models were built for Ti and Yb, respectively, and considered adequate for rough screening. The DMD models based on Yb and ADF were unreliable, whereas the model based on Ti was suitable for rough screening. The PEG prediction model built during the adaptation period performed exceptionally well; however, the DMD prediction based on PEG highlighted limitations due to diet differences during both the adaptation and experimental periods. In conclusion, fNIRS shows promise for screening Ti and Yb fecal content and DMD using Ti. However, tailored PEG prediction equations need to be developed for each specific diet. Full article

Conventional detectors based on ionization chambers, semiconductors, or thermoluminescent materials generally cannot be used to verify the in vivo dose delivered during brachytherapy treatments with γ-ray sources. However, certain adaptations and alternative methods, such as the use of miniaturized detectors or other specialized techniques, have been explored to address this limitation. One approach to solving this problem involves the use of dosimetric materials based on efficient scintillation crystals, which can be placed in the patient’s body using a long optical fiber inserted intra-cavernously, either in front of or next to the tumor. Scintillation crystals with a density close to that of tissue can be used in any location, including the respiratory tract, as they do not interfere with dose distribution. However, in many cases of radiation therapy, the detector may need to be positioned behind the target. In such cases, the use of heavy, high-density, and high-Z_eff scintillators is strongly preferred. The delivered radiation dose was registered using the radioluminescence response of the crystal scintillator and recorded with a compact luminescence spectrometer connected to the scintillator via a long optical fiber (so-called fiber-optic dosimeter). This proposed measurement method is completely non-invasive, safe, and can be performed in real time. To complete the abovementioned task, scintillation detectors based on YAG:Ce (ρ = 4.5 g/cm³; Z_eff = 35), LuAG:Ce (ρ = 6.75 g/cm³; Z_eff = 63), and GAGG:Ce (ρ = 6.63 g/cm³; Z_eff = 54.4) garnet crystals, with different densities ρ and effective atomic numbers Z_eff, were used in this work. The results obtained are very promising. We observed a strong linear correlation between the dose and the scintillation signal recorded by the detector system based on these garnet crystals. The measurements were performed on a specially prepared phantom in the brachytherapy treatment room at the Oncology Center in Bydgoszcz, where in situ measurements of the applied dose in the 0.5–8 Gy range were performed, generated by the ¹⁹²Ir (394 keV) γ-ray source from the standard Fexitron Elektra treatment system. Finally, we found that GAGG:Ce crystal detectors demonstrated the best figure-of-merit performance among all the garnet scintillators studied. Full article

Computational imaging spectrometers using broad-bandpass filter arrays with distinct transmission functions are promising implementations of miniaturization. The number of filters is limited by the practical factors. Compressed sensing is used to model the system as linear underdetermined equations for hyperspectral imaging. This paper proposes the following method: parallel dictionary reconstruction and fusion for spectral recovery in computational imaging spectrometers. Orthogonal systems are the dictionary candidates for reconstruction. According to observation of ground objects, the dictionaries are selected from the candidates using the criterion of incoherence. Parallel computations are performed with the selected dictionaries, and spectral recovery is achieved by fusion of the computational results. The method is verified by simulating visible-NIR spectral recovery of typical ground objects. The proposed method has a mean square recovery error of ≤1.73 × 10⁻⁴ and recovery accuracy of ≥0.98 and is both more universal and more stable than those of traditional sparse representation methods. Full article

The anodic oxidation of titanium implants is a practical, cost-effective method to enhance implant success, especially due to rising hypersensitivity concerns. This study investigated the electrochemical behavior, surface characteristics, and biocompatibility of anodized commercially pure titanium (cpTi, grade IV). Anodization is performed on polished, cleaned cpTi sheet samples in 1 M H₂SO₄ using a constant voltage of 15 V for 15 and 45 min. The color of the oxide layer is evaluated using the CIELab color space, while composition is analyzed by a scanning electron microscope (SEM) equipped with an energy dispersive spectrometer (EDS). Additionally, X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS) are performed to identify and monitor the phase transformations of the formed titanium oxides. Corrosion measurements are performed in 9 g L⁻¹ NaCl, pH = 7.4, and show the excellent corrosion stability of the anodized samples in comparison with pure titanium. The biological response is assessed by determining mitochondrial activity and gene expression in human fibroblasts. Anodized surfaces, particularly Ti-45, promote higher mitochondrial activity and the upregulation of adhesion-related genes (N-cadherin and Vimentin) in human gingival fibroblasts, indicating improved biocompatibility and the potential for enhanced early soft tissue integration. Full article

The quality of wine largely depends on the quality of wine grapes, which is determined by their chemical composition. Therefore, measuring parameters related to grape ripeness, such as soluble solids content (SSC), is crucial for harvesting high-quality grapes. Visible–Near-Infrared (Vis/NIR) spectroscopy enables effective, non-destructive detection of SSC in grapes. However, commercial Vis/NIR spectrometers are often expensive, bulky, and power-consuming, making them unsuitable for on-site applications. This article integrated the AS7265X sensor to develop a low-cost handheld IoT multispectral detection device, which can collect 18 variables in the wavelength range of 410–940 nm. The data can be sent in real time to the cloud configuration, where it can be backed up and visualized. After simultaneously removing outliers detected by both Monte Carlo (MC) and principal component analysis (PCA) methods from the raw spectra, the SSC prediction model was established, resulting in an ${\mathrm{R}}_V^2$ of 0.697. Eight preprocessing methods were compared, among which moving average smoothing (MAS) and Savitzky–Golay smoothing (SGS) improved the ${\mathrm{R}}_V^2$ to 0.756 and 0.766, respectively. Subsequently, feature wavelengths were selected using UVE and SPA, reducing the number of variables from 18 to 5 and 6, respectively, further increasing the ${\mathrm{R}}_V^2$ to 0.809 and 0.795. The results indicate that spectral data optimization methods are effective and essential for improving the performance of SSC prediction models. The IoT Vis/NIR Spectroscopic System proposed in this study offers a miniaturized, low-cost, and practical solution for SSC detection in wine grapes. Full article

Accurate plant species identification in desert grasslands using hyperspectral data is a critical prerequisite for large-scale, high-precision grassland monitoring and management. However, due to prolonged overgrazing and the inherent ecological vulnerability of the environment, sericite–Artemisia desert grassland has experienced significant ecological degradation. Therefore, in this study, we obtained spectral images of the grassland in April 2022 using a Soc710 VP imaging spectrometer (Surface Optics Corporation, San Diego, CA, USA), which were classified into three levels (low, medium, and high) based on the level of participation of Seriphidium transiliense (Poljakov) Poljakov and Ceratocarpus arenarius L. in the community. The optimal index factor (OIF) was employed to synthesize feature band images, which were subsequently used as input for the DeepLabv3p, PSPNet, and UNet deep learning models in order to assess the influence of species participation on classification accuracy. The results indicated that species participation significantly impacted spectral information extraction and model classification performance. Higher participation enhanced the scattering of reflectivity in the canopy structure of S. transiliense, while the light saturation effect of C. arenarius was induced by its short stature. Band combinations—such as Blue, Red Edge, and NIR (BREN) and Red, Red Edge, and NIR (RREN)—exhibited strong capabilities in capturing structural vegetation information. The identification model performances were optimal, with a high level of S. transiliense participation and with DeepLabv3p, PSPNet, and UNet achieving an overall accuracy (OA) of 97.86%, 96.51%, and 98.20%. Among the tested models, UNet exhibited the highest classification accuracy and robustness with small sample datasets, effectively differentiating between S. transiliense, C. arenarius, and bare ground. However, when C. arenarius was the primary target species, the model’s performance declined as its participation levels increased, exhibiting significant omission errors for S. transiliense, whose producer’s accuracy (PA) decreased by 45.91%. The findings of this study provide effective technical means and theoretical support for the identification of plant species and ecological monitoring in sericite–Artemisia desert grasslands. Full article

Chestnut (Castanea sativa Mill.) is an edible nut recognized for its nutritional attributes, particularly its elevated levels of carbohydrates (starch) and proteins. Chestnuts are popular for their health-promoting properties and hold significant environmental and economic importance in Europe. During this study, after the characterization of the fruit, attention was directed toward the valorization of chestnut shells, a predominant by-product of industrial chestnut processing that is typically discarded. Valuable bioactive compounds were extracted from the shells using Pressurized Liquid Extraction (PLE), a green, efficient, scalable method. Response surface methodology (RSM) was utilized to determine optimal extraction conditions, identified as 40% v/v ethanol as the solvent at a temperature of 160 °C for 25 min under a constant pressure of 1700 psi. High total polyphenol content (113.68 ± 7.84 mg GAE/g dry weight) and notable antioxidant activity—determined by FRAP (1320.28 ± 34.33 μmol AAE/g dw) and DPPH (708.65 ± 24.8 μmol AAE/g dw) assays—were recorded in the optimized extracts. Ultrahigh-performance liquid chromatography coupled with a hybrid trap ion mobility-quadrupole time-of-flight mass spectrometer (UHPLC-TIMS-QTOF-MS) was applied to further characterize the compound profile, enabling the identification of phenolic and antioxidant compounds. These findings highlight the possibility of using chestnut shell residues as a long-term resource to make valuable products for the food, medicine, cosmetics, and animal feed industries. This study contributes to the advancement of waste valorization strategies and circular bioeconomy approaches. Full article