Search Results (4,149)

In recent years, hybrid materials have been widely applied in the cultural heritage conservation field, especially to preserve color pigments. Among these, one of the most problematic (in terms of conservation science) is the red pigment cinnabar/vermilion. The challenge of this work was to prepare a hybrid coating consisting of a fluorinated polymer (known to protect cinnabar/vermilion), further modified with an inorganic filler based on anatase TiO₂. The latter is suitable because it is functionalized with quenchers, the particles are well above the nanoscale (≥200 nm in diameter), and it was added to the polymer matrix in small quantities. These characteristics made it suitable as a hybrid coating for protecting natural cinnabar, as demonstrated by the results obtained through a multi-analytical approach, based on multispectral imaging, Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM) coupled with energy-dispersive X-ray analysis (EDX), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), X-ray fluorescence (XRF), contact angle, spectrophotometry and mechanical tests, which were applied to evaluate the performances of the hybrid coating on laboratory specimens (after aging) and original samples. The experimental results provide insight into both the physicochemical decomposition mechanism of natural cinnabar under laboratory-simulated aging conditions and the benefits of the coating. In particular, the treatment did not induce electrochemical changes in the mercury, which remained in its oxidized state (+2) rather than being further reduced to elemental mercury (Hg⁰), the species responsible for the blackening of cinnabar/vermilion (also combined with meta-cinnabar). In the oxidized form (Hg²⁺), the protein binder was altered, yet the application of the hybrid coating did not cause further physicochemical changes (i.e., red shift) to the Hg²⁺/egg-based binder system. This was also reflected in the color properties, which underwent no significant alteration. Finally, the mechanical tests yielded satisfactory results, particularly regarding water vapor permeability and treatment efficiency (even eight months after the initial application, although studies on the same samples are still ongoing). The hybrid coating was ultimately applied to original samples collected at Poggio Spaccasasso (Tuscany, Italy), which could be representative of prehistoric artworks based on natural cinnabar and traces of prehistoric adhesives made from beeswax, natural oils, and plant resins. Full article

Background: Effective management of bone defects remains a major clinical challenge, driving continuous efforts to develop bioactive, localized delivery systems that support bone regeneration. Gold nanoparticles (AuNPs) have gained attention in regenerative medicine for their capacity to modulate cellular activity. Yet, their application in functional delivery systems for bone repair is still limited. Chitosan (CS), a naturally derived biopolymer, exhibits notable osteoinductive properties, particularly when used to modify nanoparticulate carriers. Objectives: In this study, AuNPs and chitosan-coated gold nanoparticles (CS-AuNPs) were formulated, characterized, and incorporated into gel preparations to evaluate their physicochemical properties and therapeutic potential in a rat tibial bone defect model. Methods: AuNPs were synthesized and either left uncoated or coated with CS to enhance biological activity. Both formulations were examined for particle size, zeta potential, X-ray diffraction, and Fourier-transform infrared spectroscopy (FTIR). The resulting nanoparticles were integrated into gel bases, which were assessed for gel strength, swelling index, viscosity, and pH. The in vivo study involved surgically induced bone defects in the tibias of albino rats treated with either formulation. Healing outcomes were assessed via histological analysis, quantification of newly formed bone, immunohistochemical staining, radiographic imaging, and measurement of bone-related markers using RT-qPCR. Results: The CS-AuNP gel formulation demonstrated significantly improved bone regeneration compared to the uncoated counterpart, as evidenced by histological findings, increased bone volume in radiographs, stronger immunohistochemical expression of the VEGF angiogenic protein marker, and increased genetic expression of osteogenic markers. Conclusions: Incorporating CS-AuNPs into gel formulations offers a promising approach for enhancing bone healing. The superior performance of the CS-coated system highlights its potential as a promising localized therapy for managing bone defects. Full article

This study presents the first technical examination of Ring Around the Rosie (ca. 1908), an easel painting by the Greek artist Symeon Savvidis (1859–1927). Despite Savvidis’ significance within Greek modernism, little is known about his materials and working practices from a scientific perspective. To address this gap, the painting was analysed using VNIR hyperspectral imaging (HSI), portable Raman spectroscopy, and handheld energy dispersive X ray fluorescence (h-EDXRF). The integration of imaging, molecular, and elemental data enabled the identification of several pigment classes, including cobalt blue, ultramarine, lead chromates, ochres, lead white, and a tentative Cu-As green pigment, while also providing information on their spatial distribution across the surface. HSI classification and the examination of a representative NIR image revealed relationships among the coloured passages and suggested the widespread use of a cobalt-containing background paint. The results further indicate that several compositional elements may have been applied directly over previously, although the absence of stratigraphic information prevents definitive conclusions regarding paint layering and mixtures. Overall, the study demonstrates the value of combining HSI, Raman spectroscopy, and h-EDXRF for the non-invasive investigation of artists’ materials and highlights the contribution of technical studies to a broader understanding of Savvidis’ artistic practice and modern Greek painting traditions. Full article

Nitric oxide (NO) is a small, highly reactive gaseous signaling molecule that plays diverse and context-dependent roles in microbial physiology and host–microbe interactions. Over the past decade, increasing evidence has revealed the dual nature of NO as both an antimicrobial effector and a signaling mediator involved in microbial stress responses, metabolism, biofilm dynamics, quorum sensing, virulence regulation, and symbiotic interactions. In microbial systems, NO influences adaptation to environmental stress and contributes to mechanisms associated with persistence and antimicrobial resistance. In host organisms, NO functions as a key component of innate immunity while also participating in beneficial interactions involving rhizobia, mycorrhizal fungi, and probiotic microorganisms. Despite its biological significance, accurate detection and quantification of NO remain challenging because of its transient nature, high reactivity, low physiological concentrations, and interference from related reactive oxygen and nitrogen species. Recent advances in biosensing technologies have substantially improved NO detection capabilities through the development of electrochemical, optical, enzyme-based, microfluidic, wearable, and implantable sensing platforms. These innovations are complemented by analytical techniques including electron paramagnetic resonance spectroscopy, mass spectrometry, fluorescence-based imaging, and advanced microscopy, which enhance sensitivity, specificity, and spatiotemporal resolution in complex biological environments. Concurrently, statistical and computational approaches—including sensor calibration models, multivariate analyses, machine learning algorithms, and bioinformatics pipelines—have become increasingly important for extracting biologically meaningful information from NO-related datasets. Unlike previous reviews that primarily focus on either NO biology or sensing technologies, this review integrates current knowledge of NO-mediated microbial physiology and host–microbe interactions with recent developments in biosensor engineering, analytical methodologies, statistical frameworks, and emerging artificial intelligence (AI)-driven data interpretation. We further highlight applications of NO detection in infectious disease diagnostics, antimicrobial screening, probiotic and biofertilizer evaluation, environmental microbiome monitoring, and real-time studies of symbiosis and infection. Finally, future directions including miniaturized sensing platforms, multi-omics integration, AI-assisted analytics, and sensor standardization are discussed. By unifying molecular, analytical, and computational perspectives, this review provides a multidisciplinary framework and roadmap for advancing NO-based research and translational applications across microbial, environmental, and host-associated systems. Full article

Lignite-derived activated carbon (L-AC) was fabricated via a microwave-assisted KOH activation process using a low-rank Mae Moh lignite and explored its potential as an adsorbent solid for post-combustion CO₂ capture. Optimization of the KOH ratio, microwave irradiation power, and activation time gave rise to a product with a BET surface area of 1349 m² g⁻¹ and total pore volume of 0.78 cm³ g⁻¹, which represented 165 times and 78 times enhancement compared with that of the initial lignite, respectively. Scanning electron microscope (SEM) images proved the formation of a hierarchical macropore–mesopore–micropore structure, whereas Raman (Iᴰ/Iᴳ = 1.83) and Fourier-transform infrared spectroscopy analyses revealed a graphitic-like structure rich in defects with the existence of C=O and C–O–C functional groups involved in the Lewis acid–base interaction between L-AC and CO₂ molecules. Dynamic fixed-bed breakthrough tests performed at temperatures of 298, 328, and 353 K under post-combustion relevant conditions (CO₂ concentration: 15%, pressure: 1 atm) yielded CO₂ equilibrium uptake capacities of 47.34, 34.37, and 21.34 mg g⁻¹, respectively, with outstanding cyclic stability achieved after six consecutive adsorption–desorption cycles of temperature swing adsorption–desorption at 393 K. Among the seven nonlinear kinetic models, the Avrami, FL-PFO, and general-order models exhibited the highest fitting accuracy (R² = 0.9994–0.9998), suggesting that CO₂ adsorption onto L-AC proceeds through heterogeneous, multi-stage adsorption kinetics. A Weber–Morris intra-particle diffusion analysis identified a three-stage sequential transport mechanism in which mesopore diffusion constitutes the primary rate-limiting step. Thermodynamic parameters confirmed spontaneous (ΔG° = −24.20 to −26.87 kJ mol⁻¹), exothermic (ΔH° = −9.42 kJ mol⁻¹), and entropy-assisted adsorption (ΔS° = +49.93 J mol⁻¹ K⁻¹) consistent with a physisorption mechanism, corroborated by a low activation energy of 9.11 kJ mol⁻¹. These findings demonstrate the viability of low-rank lignite as a low-cost precursor for the scalable synthesis of high-performance carbonaceous CO₂ adsorbents for post-combustion capture applications. Full article

The electron gun is a critical component of the X-ray source in X-ray photoelectron spectroscopy (XPS) systems, responsible for generating and directing an electron beam to the target from which X-rays emit. Precise alignment of the electron gun is fundamental for XPS analysis because misalignment can lead to electron beam position shift and reduce signal intensity when the objective lens current (OLC) changes slightly. This work proposes a new electron beam gun alignment method utilizing a Faraday cup fitted with two sharp tungsten edges. Following mechanical alignment, the electrical alignment parameters are optimized incrementally, while the corresponding positions and shapes of beam spots at different OLCs are measured concurrently. The electron beam alignment is considered completed when all the measured beam spots maintain a consistent center position and exhibit regular circular shapes. Employing this method, precise alignment of the electron gun can be accomplished in two types of scenarios: when the beam spot is about 52 μm or 8 μm. The alignment is also verified by a series of images captured by a camera when the electron beam bombards tungsten. This is a promising alignment method for other electron or ion guns with a similar beam size. Full article

Hyperspectral remote sensing technology, with its ability to acquire continuous and fine-scale spectral information, shows potential in mineral classification but faces technical challenges such as spectral variability within the same material and spectral similarity among different materials. In previous work, we explored rock classification using various hyperspectral classification methods; however, many minerals were found to exhibit inherently similar spectral signatures, which limited the classification performance of these methods. In this study, we aim to address this issue by constructing a preliminary mineral classification system using laboratory hyperspectral data to redefine categories from a spectral perspective. Specifically, minerals are reorganized into new classes based on their spectral curve characteristics rather than traditional mineralogical definitions, thereby establishing a spectrally driven framework for evaluating separability among spectral groups. A total of 250 mineral specimens were imaged in a darkroom environment using shortwave infrared bands to obtain high-resolution spectral data. After preprocessing, minimum noise fraction (MNF) transformation was applied for dimensionality reduction. Based on their spectral curve characteristics, the 250 mineral specimens were reclassified into 33 spectrally defined preliminary groups. Then, support vector machine (SVM) and random forest (RF) were applied to compare the classification performance of the original individual mineral identification task and the spectrally defined group task, using accuracy, Kappa, and metrics calculated for each class. The experiments demonstrate that the classification scheme based on 33 spectral groups improves classification performance among groups and partially reduces the spectral confusion occurring in the original individual mineral identification task. This framework offers new insights for laboratory-scale mineral research and provides a preliminary experimental basis for future testing under field and airborne/spaceborne conditions. Full article

Spaceborne imaging spectroscopy has created new opportunities for monitoring soil properties at regional scales. Its use for predicting soil heavy metal concentrations in mountainous environments, however, remains insufficiently tested, especially when EMIT hyperspectral data are used. In this study, EMIT Level-2A surface reflectance data were integrated with DEM-derived terrain variables to estimate soil arsenic (As), copper (Cu), and zinc (Zn) concentrations in Renhuai, Guizhou Province, Southwest China. Only soil samples falling within valid EMIT coverage were used for element-specific modeling, resulting in 139 samples for As, 136 for Cu, and 130 for Zn. To reduce redundancy among predictors, EMIT spectral variables and terrain factors were screened before model construction. Random forest and XGBoost models were then tested using repeated spatial cross-validation. The best-performing model for As combined EMIT predictors with elevation and achieved a validation R² of 0.460. Model performance was considerably weaker for Cu, with a validation R² of 0.188. For Zn, the model failed to outperform the mean-based benchmark, producing a negative validation R² of −0.028. The spatial prediction maps and residual patterns suggested that the EMIT-based prediction showed moderate potential for As, limited predictive value for Cu, and exploratory rather than reliable mapping capability for Zn under the current sample and predictor conditions. Full article

Raman spectroscopy is a non-destructive, label-free analytical technique that can probe biochemical alterations in tissues and cells. Raman spectroscopy, being sensitive to biochemical perturbations, can potentially provide early and real-time identification of changes preceding morphological changes, allowing early diagnosis as well as disease monitoring. Recent research has demonstrated its broad utility across diverse clinical domains, including cancers, neurological conditions, and infections. Raman spectroscopy combined with machine learning algorithms allows rapid assessment and automated pipelines and can act as a clinical adjunct, enhanced by integrating tools like principal component analysis (PCA), linear discriminant analysis (LDA), random forests, and deep-learning architectures. These models allow interpretation of complex spectra, and decipher meaningful biomarkers in heterogeneous clinical samples. This review highlights the earliest and most recent progress in Raman-based non-destructive diagnosis, underscoring advances in cancer diagnosis and challenges faced in clinical settings. Full article

Aluminum corrosion in acidic media leads to accelerated material degradation and significant economic losses. This study evaluated the aqueous extract of yerba mate (Ilex paraguariensis) as a green inhibitor for aluminum corrosion in 0.5 M HCl at temperatures (298–323 K) and extract concentrations (1%, 2.5%, and 5% v/v). The extract was characterized by FTIR, and its inhibitory performance was assessed using weight loss measurements, potentiodynamic polarization, electrochemical impedance spectroscopy (EIS), scanning electron microscopy (SEM), and contact angle analysis. Gravimetric results showed a decrease in corrosion rate with increasing extract concentration, reaching a maximum inhibition efficiency of 94% at 308 K and 5% v/v. The increase in activation energy in the presence of the inhibitor suggested the formation of an energy barrier associated with adsorption on the aluminum surface. Polarization studies indicated that the extract behaves as a mixed-type inhibitor, while EIS revealed an increase in charge transfer resistance and the formation of a protective adsorbed film. SEM images confirmed reduced corrosion damage, and contact angle measurements indicated increased surface hydrophobicity. The inhibition mechanism followed Langmuir adsorption behavior, suggesting adsorption of organic species at the aluminum–solution interface. These findings demonstrate that yerba mate extract is an effective corrosion inhibitor. Full article

Non-invasive Electrical Impedance Tomography (EIT) and Electrical Impedance Spectroscopy (EIS) are emerging as promising techniques for real-time monitoring and quality assessment in food processing and agri-food applications. This study reviews recent advances in impedance-based sensing for fruit characterization and investigates the experimental implementation of multi-electrode impedance measurements for tomographic imaging. Particular attention is devoted to electrode configurations, electrode polarization effects, and equivalent circuit modeling. Experimental measurements were performed on yellow honeydew melon samples using a four-electrode configuration and a impedance analyzer Keysight E4990 (Keysight Technologies, Santa Rosa, USA) over the frequency range from 20 Hz to 1 MHz. The impedance spectra were validated through Kramers–Kronig consistency tests and interpolated using several fractional-order equivalent circuit models, including single-Cole, double-Cole, and Hayden-based models. The results show that four-electrode measurements are less sensitive to electrode-sample interface artifacts than conventional two-electrode approaches, thereby providing a more reliable estimate of the sample impedance, particularly at low frequencies. Among the tested models, the double-Cole model provided the best interpolation accuracy, while the fractional Hayden models effectively described the temporal evolution of extracellular resistance and membrane-related parameters. Preliminary EIT reconstructions further demonstrate the feasibility of non-destructive tomographic imaging for fruit monitoring. These findings support the potential of EIS and EIT as low-cost, portable, and non-invasive tools for smart food quality assessment and precision agriculture applications. Full article

Lithium metal batteries (LMBs) incorporating solid-state electrolytes (SSEs) promise high energy density and safety, yet their practical deployment is hindered by poor interfacial stability between SSEs and lithium metal anodes. Here we show that a simple incorporation of LiBF₄ into the sulfohalide (Li₃SCl) framework forms a mixture Li₃SCl@LiBF₄ (LSC@BF) SSE via a two-step solid-state synthesis, preserving a high room-temperature ionic conductivity of 4.32 × 10⁻⁴ S cm⁻¹ with a low activation energy of 0.22 eV while fundamentally altering the interface. X-ray photoelectron spectroscopy and electron microscopy reveal that LiBF₄ promotes the in situ formation of a mechanically robust, LiF-rich solid-electrolyte interphase at the SSE|Li interface. This LiF-rich layer effectively suppresses lithium dendrite growth and stabilizes the interface, enabling symmetric Li|LSC@BF|Li cells to achieve stable lithium plating/stripping for over 800 h at 0.2 mA cm⁻². Cross-sectional post-mortem imaging confirms a dense, void-free interface without dendrite penetration. Our work demonstrates that LiBF₄ incorporation offers a simple, scalable strategy to simultaneously maintain high ionic conductivity and resolve interfacial instability in sulfohalide SSEs for high-performance LMBs. Full article

For image reconstruction in diffuse optical tomography (DOT), both accurate mathematical modeling of light propagation in biological tissue and robust inverse modeling are essential. This study evaluates the validity of the radiative transfer equation (RTE) as a forward model for DOT of the thyroid gland, which surrounds the air-filled trachea anteriorly, by comparing it with the photon diffusion equation (PDE). Distributions of photon time-of-flight (DTOFs) were obtained from numerical solutions of the RTE and the PDE in a homogeneous phantom and in a phantom containing four cylindrical holes. The refractive-index mismatch at the cylindrical hole walls (refractive index 1.511) was explicitly modeled by incorporating boundary conditions into the RTE solver, where refraction angles were determined using Snell’s law and the reflection coefficient was calculated based on Fresnel’s law. These simulated DTOFs were compared with experimental measurements acquired using a time-domain near-infrared spectroscopy (TD-NIRS) system. The results demonstrate that the RTE describes light propagation in media containing hollow regions more accurately than the PDE. Future work will apply this RTE framework to model light propagation in the human thyroid gland and improve the diagnostic accuracy of thyroid nodules. Full article

Grassland foliar functional traits are closely linked to ecosystem functioning, biodiversity, and plant responses to environmental change. Hyperspectral remote sensing provides an efficient and non-destructive approach for mapping foliar traits, yet direct comparisons between UAV-based and airborne imaging spectroscopy remain limited. In this study, we evaluated the performance of UAV-based Nano and airborne Hyspex hyperspectral imagery for predicting ten foliar functional traits across experimental grassland plots at the Cedar Creek Ecosystem Science Reserve, USA. We further assessed the contributions of visible-to-near-infrared (VNIR) and shortwave infrared (SWIR) spectral regions, as well as the effects of spectral preprocessing approaches for minimizing confounding effects from canopy structure, illumination/viewing geometry, and soil background. Random Forest regression models were developed using plot-level average spectra derived from Nano and Hyspex imagery. Both UAV- and airborne-based imaging spectroscopy achieved moderate to high prediction accuracies for most foliar traits. High accuracies were obtained for non-structural carbohydrates (NSC), carotenoids, β-carotene, hemicellulose, and cellulose (R² = 0.66–0.82; NRMSE = 6–10%), while moderate accuracies were achieved for nitrogen, chlorophyll, and xanthophylls (R² = 0.51–0.74; NRMSE = 8–12%). In contrast, carbon and lignin consistently exhibited lower predictive performance (R² = 0.32–0.59; NRMSE = 9–15%). Despite covering only the VNIR spectral range, the UAV-based Nano imagery achieved accuracies comparable to those obtained using the airborne full-spectrum Hyspex imagery, indicating that high spatial resolution can partially compensate for limited spectral coverage by reducing soil background effects. The VNIR spectral region alone provided trait estimation accuracies comparable to those obtained using the full visible-to-shortwave infrared (VSWIR) spectrum, whereas SWIR wavelengths contributed only marginal improvements for a subset of structural traits. Among preprocessing approaches, vector normalization generally improved prediction performance by reducing the confounding effects of canopy structure and illumination/viewing geometry, whereas NIR_v-adjusted spectra provided limited benefits. Our findings demonstrate that UAV-based VNIR imaging spectroscopy can provide accurate and cost-effective estimation of grassland foliar functional traits. The results also highlight important trade-offs between spectral and spatial resolution in hyperspectral remote sensing and provide practical guidance for selecting imaging spectroscopy platforms and preprocessing approaches for grassland ecosystem monitoring. Full article

Background/Objectives: Multiple sclerosis (MS) can cause neurodegeneration leading to accelerated brain atrophy. Brain-predicted age (BA) is an emerging neuroimaging biomarker for neurodegeneration but remains underexplored in MS. This study examines the pathophysiological substrates associated with the brain age gap in MS compared with healthy controls (HCs) through a combination of volumetric, spectroscopic, and diffusion imaging. Methods: This retrospective cross-sectional study included 33 HCs and 124 MS patients. Participants underwent 3T MRI including 3D-T1, MR spectroscopy, magnetization transfer, and diffusion imaging. BA and volumes were estimated from T1-weighted scans using brainageR. Metabolic integrity (total N-acetylaspartate to total creatine ratio, tNAA/tCr) and microstructural damage (magnetization transfer ratio [MTR], fractional anisotropy [FA]) were evaluated independently in normal-appearing tissues. Multivariate linear regression assessed MS diagnosis as an independent predictor of BA metrics, controlling for age, sex, and race. Results: MS patients showed significantly higher predicted brain age (53.3 vs. 31.8 years) and a markedly larger age gap (10.2 vs. −0.1 years) compared to HCs. Beyond macroscopic volume loss, accelerated aging paralleled profound subclinical degradation, including lower neuronal integrity (tNAA/tCr: 2.0 vs. 2.4) and widespread microstructural damage, evidenced by reduced MTR and FA across both normal-appearing gray and white matter. Linear regression confirmed MS diagnosis as an independent predictor of both BA and Age Gap (15.09 and 13.50 years) after adjusting for confounders. Conclusions: MS patients exhibit accelerated biological brain aging, characterized by a significant age gap and concurrent tissue volume loss. The brain age gap in MS extends beyond macroscopic atrophy, capturing underlying subclinical metabolic failure and widespread microstructural degradation in normal-appearing tissues. This positions BA as a robust, multi-dimensional proxy for neuroaxonal pathology. Full article