Search Results (543)

Photodynamic therapy (PDT) exploits photoactivated materials that generate reactive oxygen species (ROS) to induce selective cancer cell death. Nitrogen-doped carbon dots (N-CDs) have emerged as promising photosensitizers owing to their favorable optical properties, while hybridization with titanium dioxide (TiO₂) may further enhance photoinduced ROS generation through improved charge separation. Here, we synthesized a series of N-CD/TiO₂ hybrid composites with varying TiO₂ content using a hydrothermal approach and systematically investigated the relationship between their physicochemical characteristics and biological activity. The hybrid materials were characterized by Fourier-transform infrared spectroscopy, X-ray diffraction, scanning electron microscopy, dynamic light scattering, and UV–visible spectroscopy. Among the formulations investigated, the composite containing 90% N-CDs and 10% TiO₂ (N-CDs10T) exhibited the smallest hydrodynamic diameter, a relatively narrow particle size distribution, favorable optical properties, and the strongest irradiation-dependent biological responses. Biological activity was evaluated in A549 lung adenocarcinoma and Kelly neuroblastoma cells. Under dark conditions, all formulations displayed relatively low intrinsic cytotoxicity. Following irradiation with 365 nm UVA light, however, N-CDs10T induced a marked increase in intracellular ROS production, activation of antioxidant response element (ARE)-dependent signaling, disruption of cell-cycle progression, apoptosis-associated cell death, and inhibition of cell proliferation and migration. Kelly cells exhibited greater sensitivity than A549 cells, with IC₅₀ values decreasing from 0.98 mg/mL under dark conditions to 0.52 mg/mL following irradiation. Collectively, these findings demonstrate that N-CD/TiO₂ hybrid composites function as photoresponsive materials that enhance ROS-mediated cytotoxicity upon light activation. Beyond demonstrating phototoxicity, this study systematically links hybrid composition with oxidative stress signaling and multiple cellular responses, providing a comprehensive biological evaluation of N-CD/TiO₂ hybrid materials. While additional studies are required to identify the predominant ROS, evaluate selectivity in non-malignant cells, and optimize activation at clinically relevant wavelengths, the present work establishes a proof of concept for the development of N-CD/TiO₂ hybrid composites for photodynamic applications. Full article

Objective: The aim of this systematic review was to evaluate the in vitro effects of LED-based photobiomodulation on fibroblasts and osteoblasts, with particular focus on cellular processes involved in soft- and hard-tissue regeneration. Methods: A comprehensive electronic search was conducted on 3 April 2026 in PubMed, Scopus, Web of Science, Embase, and WorldCat according to PRISMA guidelines. The analyzed outcomes included cell viability, proliferation, migration, collagen synthesis, oxidative stress, mitochondrial activity, and selected regeneration-related processes. A total of 745 records were initially identified, and 32 studies met the inclusion criteria and were included in the qualitative synthesis. Results: The biological effects of LED-PBM depended strongly on irradiation parameters, including wavelength, fluence, irradiance, exposure time, treatment schedule, and the initial condition of the cells. Most included studies focused on fibroblast models. Red and near-infrared light showed the most consistent beneficial effects, particularly by supporting fibroblast viability, proliferation, migration, mitochondrial activity, ATP production, collagen-related responses, and oxidative stress modulation. In osteoblast-related models, LED irradiation showed potential to influence cell number, metabolic activity, maturation markers, and mineralization-related outcomes; however, the number of studies was limited. Blue light demonstrated dose-dependent effects, with higher fluences reducing fibroblast metabolic activity, proliferation, or viability. Green light improved fibroblast proliferation and migration in one model but was associated with increased cell death in osteoblast-like cells. Conclusion: LED-PBM may positively modulate cellular processes involved in soft- and hard-tissue regeneration in vitro. However, the observed effects are strongly parameter-dependent, and further standardized studies are required to define optimal irradiation protocols and validate their potential clinical relevance. Full article

This study investigated how detection distance affects spectral models for soluble solids content (SSC) and firmness evaluation in Korla fragrant pears and provides a reference for calibrating standardized indoor non-destructive detection equipment. Two hundred visually intact fruit samples at the early-ripening stage were collected from the Korla production area in Xinjiang. An FS-640 multispectral camera system equipped with a VS-SWR fixed-focus industrial lens (16 mm focal length, F1.8 maximum aperture, 1/2-inch sensor format) was used to acquire fruit reflectance spectra at seven vertical lens-to-fruit-surface distances of 90, 100, 110, 120, 130, 140, and 150 cm. A 625-pixel region of interest (ROI) was selected using ENVI at an undamaged equatorial or near-equatorial position of each fruit, and the regional mean spectrum was used as the spectral feature of one fruit sample. The sample-set partitioning based on joint X–Y distances (SPXY) algorithm was used to divide the calibration and prediction sets at a 3:1 ratio after outlier removal via a residual-threshold method. Four preprocessing methods, namely LOESS smoothing, standardization, vector normalization, and Savitzky–Golay (SG) smoothing, were compared. Competitive adaptive reweighted sampling (CARS) was performed with 50 Monte-Carlo sampling runs, a maximum of 30 principal components, and 10-fold cross-validation, yielding 99 characteristic wavelengths. Partial least squares regression (PLSR), support vector regression (SVR), random forest (RF), and artificial neural network (ANN) models were then established using identical input variables and sample partitions. Model performance was evaluated using the coefficient of determination for calibration ($R_c^2$), coefficient of determination for prediction ($R_P^2$), root-mean-square error of calibration (RMSEC), root-mean-square error of prediction (RMSEP), relative prediction deviation (RPD), and ratio of performance to interquartile distance (RPIQ). Under the static laboratory acquisition conditions in this work, the SSC model achieved the best prediction performance at 110 cm with SG smoothing ($R_P^2$) = 0.8949, RPD = 3.0633, RPIQ = 5.8661), whereas the firmness model obtained optimal prediction performance at 140 cm with standardization ($R_P^2$) = 0.7460, RPD = 1.9425, RPIQ = 3.2867). Changes in detection distance altered illumination uniformity, effective reflected signal, photon-scattering paths, and background-noise proportion. These effects may partially explain why the chemical-absorption-dominated SSC index and the tissue-scattering-dominated firmness index responded differently to detection distance. The results provide a reference for setting spectral detection parameters for Korla fragrant pears; however, samples were obtained from only a single producing region, harvest season, and maturity stage, and no independent external validation dataset was used. Therefore, the generalization ability of the developed models needs to be further verified using cross-season and cross-orchard sample sets. Full article

A hybrid metasurface absorber (MSA) based on a central-bar-coupled split-disk dimer is proposed and numerically investigated for high-resolution refractive-index sensing in the near-infrared spectral region. The metasurface consists of silicon nitride dielectric resonators integrated with a gold plasmonic layer, enabling strong electromagnetic confinement, enhanced light–matter interaction, and ultra-narrow resonant features within the 1000–1400 nm wavelength range. The optimized structure supports multiple resonant modes under both x- and y-polarized excitation, producing sharp reflection dips with full-width-at-half-maximum values as low as 0.58 nm and quality factors reaching 2007. Refractive-index sensing performance was evaluated by varying the aqueous superstrate refractive index from 1.33 to 1.35, resulting in bulk sensitivities up to 860 nm/RIU under normal incidence. The angular response was further analyzed for incidence angles up to 5°, revealing polarization-dependent resonance splitting and the emergence of additional high-Q resonant branches under oblique excitation. Several angularly induced resonances exhibit narrower linewidths than those observed at normal incidence while preserving high refractive-index sensitivity up to 870 nm/RIU. Electric-field distributions confirm strong field localization near the dielectric boundaries and coupling regions, validating the hybrid resonant mechanism responsible for the enhanced spectral selectivity and sensing performance. The proposed MSA provides a promising platform for compact and ultrasensitive biosensing applications. Full article

Mobile near-infrared (NIR) detection of wheat moisture is susceptible to random noise, scattering effects, baseline variations, and local spectral misalignment under dynamic acquisition conditions. In this study, a mobile online NIR detection platform was developed to collect wheat spectra over 660–1080 nm. A total of 169 modeling samples were divided into a calibration set (118 samples) and a prediction set (51 samples), while 50 samples from a different source were used for external validation. Savitzky–Golay (SG) smoothing was used to suppress random noise, extended multiplicative scatter correction (EMSC) was applied to correct scattering effects and baseline variations, and correlation optimized warping (COW) was employed for wavelength alignment. CARS–VIP was subsequently used to select informative wavelength variables, and an RF model was developed for moisture prediction. Among the evaluated strategies, SG–EMSC–COW–CARS–VIP–RF achieved the best overall performance and outperformed the corresponding full-spectrum RF model. The optimal model retained 17 wavelength variables, accounting for 6.8% of the original 250 variables. It achieved an R²_p of 0.9923, an RMSE_p of 0.3678, and an MAE_p of 0.2323 on the prediction set. For the external validation set, the corresponding R², RMSE, and MAE values were 0.9803, 0.4428, and 0.3682, respectively. The proposed method effectively mitigated spectral interference and enhanced prediction stability, providing a technical basis for the online determination of moisture content in stored grain. Full article

This review analyzes how Zinc(II) coordination alters the electronic configuration of porphyrin-based photosensitizers to optimize reactive oxygen species (ROS) generation and subcellular targeting in photodynamic therapy (PDT). By focusing on the structural design principles that govern excited-state behavior, the work moves beyond clinical descriptions to provide a mechanistic understanding of how engineered metalloporphyrins can achieve precise tumor destruction. When these engineered metal porphyrins are exposed to specific wavelengths of light, they transfer energy to create ROS, such as singlet oxygen, which directly damages and kills tumor tissue. The review evaluates structural modifications that drive selective accumulation within critical subcellular organelles, notably the mitochondria, to maximize cytotoxic efficiency. By analyzing the impact of the tumor microenvironment on hypoxia, the work outlines strategies for maintaining efficacy in oxygen-deprived zones and highlights how the biocompatible, redox-inactive nature of Zinc(II) minimizes systemic toxicity, providing a blueprint for the design of targeted, translation-ready photosensitizers. Full article

Soil organic carbon (SOC) is a key indicator of soil quality, farmland productivity, and the terrestrial carbon cycle. Visible and near-infrared (Vis-NIR) spectroscopy offers a rapid approach for SOC estimation, but wavelength point selection may disrupt continuous spectral structures, whereas conventional wavelength interval selection may fail to fully exploit complementary information across spectral regions. In this study, a synergistic interval-constrained Ridge regression framework, termed sicRidge, was developed for SOC prediction. Continuous candidate intervals were generated using a sliding-window strategy, and a dual-interval synergistic search with an overlap constraint was applied to identify complementary and low-redundancy interval combinations. The selected intervals were then used to construct Ridge regression models. Using Vis-NIR spectra from 168 soil samples, sicRidge was compared with full-spectrum Ridge regression, five wavelength point selection-based Ridge models, and several wavelength interval selection-related benchmark models. sicRidge achieved the best prediction performance using 140 selected bands, with an R²_P of 0.834, RMSE_P of 2.010 g kg⁻¹, RPD of 2.483, and RPIQ of 3.777. The optimal intervals were 570~649 nm and 1880~1939 nm. These results indicate that sicRidge can improve SOC prediction by preserving continuous spectral structures while exploiting complementary cross-region information. 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

This study presents the development and validation of the nationwide ground motion monitoring system of InSAR.Hungary, which is designed to produce deformation monitoring products harmonized with the European Ground Motion Service. The proposed workflow integrates PSI results with GNSS-derived deformation models within a consistent framework. As a methodological contribution, it introduces an optimization-based spatial reference point selection method, which combines kernel density estimation with global optimization, and its extended formulation permitting subsequent utilization of a virtual reference. In addition, a simplified calibration strategy is also implemented, reducing the calibration of InSAR with GNSS data to a superimposing step, under the assumption that large-scale deformation components are introduced only by GNSS to the calibrated results. The system is validated through cross-comparisons, first against the European Ground Motion Service. The results reveal low-amplitude, spatially heterogeneous large-scale residual patterns between the products, which are potentially attributable to differences in the handling of long-wavelength phase and deformation components between the models. After accounting for these effects, the residual differences exhibit no significant bias and remain consistent with random spatial variability, indicating statistical agreement between the models. This finding is also supported by the outcome of the cross-comparison of InSAR.Hungary and observed GNSS-based deformation measurements. These findings confirm the reliability of the proposed workflow and establish InSAR.Hungary as a consistent framework for wide-area ground motion monitoring with practical applicability in geodetic and operational contexts. Full article

Traditional methods for determining starch content in corn kernels are labor-intensive, destructive, and inefficient. To overcome these challenges, this work developed a rapid, non-destructive approach based on near-infrared hyperspectral imaging, applied to 58 rainfed corn varieties. A spectral preprocessing scheme combining wavelet transform, multiplicative scatter correction, and standard normal variate transformation was employed to enhance spectral quality. A two-stage wavelength selection framework was established using competitive adaptive reweighted sampling and sparrow search algorithm optimization. From the selected optimal wavelengths, four predictive models, namely partial least squares regression, artificial neural network (ANN), convolutional neural networks, and gradient boosting decision tree, were established, implemented, and systematically compared. The results identify 14 key wavelengths (1020.65–1647.71 nm) strongly correlated with starch content, with clear assignments to specific chemical bonds and good physical interpretability. Among these models, the ANN exhibited the best performance. The R², RMSE, and RPD of the test set were 0.826, 0.759%, and 2.40, respectively, indicating favorable prediction accuracy and generalization ability. These key wavelengths provide a foundation for developing portable detection instruments. This work supports corn quality grading, breeding of high-starch varieties, and rapid raw material screening, thereby enhancing the quality and efficiency of the corn industry. Full article

Surface-enhanced Raman spectroscopy (SERS) is a powerful technique for detecting, identifying, and quantifying analytes of interest in both environmental and clinical contexts. A key factor in SERS is the choice of substrate, which directly influences the enhancement factor and measurement reproducibility. A wide range of commercial SERS substrates is currently available, featuring tailored nanostructures and surface patterns designed to optimize signal enhancement. Recently, SERS has also been applied to the development of detection strategies for phenolic compounds. Within this framework, we aimed to evaluate several commercial SERS substrates and one homemade SERS substrate using catechol as a molecular probe. Each substrate was initially assessed by acquiring spectra of the bare substrate using the laser excitation wavelengths recommended by the manufacturers. Raman spectra of catechol solutions at relatively high concentrations were also acquired using the same wavelengths. These preliminary measurements guided the selection of experimental conditions for subsequent substrate performance evaluations. Hyperbola and linear function fitting were performed to quantitatively characterize the tested substrates in catechol detection. The proposed approach allowed for the identification of a parameter that can be used for estimating a substrate’s overall efficiency, along with the main sensing figures of merit. 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

Hyperspectral imaging (HSI), which integrates spatial and continuous spectral information, has shown considerable potential for nondestructive citrus quality assessment. However, HSI data inherently suffer from high dimensionality, strong inter-band correlation, and substantial redundancy. Consequently, extracting reliable quality evaluation results from such complex spectral information relies heavily on effective algorithm design. This review summarizes recent advances in data-analysis algorithms for HSI-based nondestructive citrus quality assessment, focusing on three major tasks: maturity assessment; disease, pest damage, and bruising detection; and internal physicochemical and nutritional attribute prediction. Representative approaches, including chemometrics, machine learning, deep learning, transfer learning, and multimodal fusion, are reviewed and compared from the perspective of task-specific challenges. Existing studies indicate that maturity assessment has developed relatively mature algorithmic pathways, whereas disease and bruise detection require effective enhancement of weak abnormal signals and robust suppression of environmental and structural interference. Internal physicochemical and nutritional attribute prediction, especially for titratable acidity (TA) and vitamin C (VC), remains challenging because of weak spectral responses, complex nonlinear relationships, and limited cross-scenario stability. Future research should emphasize standardized datasets, informative wavelength selection, lightweight model design, interpretable learning, and multi-task collaborative modeling. This review provides a systematic reference for algorithm design and system optimization in HSI-based citrus quality assessment. Full article

Long wave infrared metalenses provide a promising route toward compact multispectral optical systems, including spaceborne imaging and sensing payloads. However, achromatic focusing at separated LWIR wavelength channels remains challenging because the required phase relation must be maintained together with sufficient dispersion control and optical throughput. Here, we propose a robust four-wavelength achromatic metalens operating at 8, 10, 12, and 14 $\mathsf{\mu }$m based on a meta-atom library controlled by nanopillar radius and height. The library feasibility is evaluated before layout optimization to verify whether the required phase and dispersion responses are accessible within the selected unit cell space. By introducing nanopillar height as an additional degree of freedom, the library achieves a phase coverage ratio above 0.92 at all four wavelengths. The optimized metalens achieves a mean absolute focusing efficiency (AFE) of 69.5% with suppressed chromatic focal shift. Monte Carlo perturbation analysis and full device FDTD simulations further confirm the robustness and focusing stability of the design. This work provides a feasibility-driven strategy for robust multispectral LWIR achromatic metalenses. Full article

Short-wave near-infrared (SW-NIR) spectroscopy provides a rapid and nondestructive sensing route for monitoring bread staling, but formulation-dependent moisture redistribution and starch retrogradation can make pooled spectral regression unstable. This study investigated a stratified SW-NIR modeling strategy for bread staling prediction using 324 spectra from control bread (CR) and two maltogenic $\alpha$-amylase treatments (EZ1 and EZ2). A global full-spectrum partial least squares (PLS) model was compared with bread-type-specific PLS models; competitive adaptive reweighted sampling (CARS), support vector machine recursive feature elimination (SVM-RFE), and multiple feature-spaces ensemble LASSO (MFE-LASSO) were then each coupled with PLS and evaluated within each bread type. The pooled benchmark achieved a root mean square error of prediction (RMSEP) of 2.28 days, whereas stratified full-spectrum PLS reduced this to 1.86, 2.14, and 2.15 days for CR, EZ1, and EZ2, respectively. In repeated wavelength-selection runs, MFE-LASSO was the most consistently competitive method across bread types. In the representative best-model comparison, MFE-LASSO-PLS yielded the strongest performance for CR (RMSEP = 1.71 days) and EZ1 (RMSEP = 1.43 days), while CARS-PLS gave the lowest RMSEP for EZ2 (2.00 days). An exploratory position-specific analysis within the CR subset further suggested that the middle crumb region carried stronger staling-related spectral information than the top and bottom regions. These results indicate that formulation-aware SW-NIR spectroscopic sensing is a practical strategy for nondestructive bread-staling assessment and that the optimal wavelength-selection method is bread-type-dependent. Full article