Search Results (8,216)

The isomerization of geraniol using natural, acid-modified minerals such as vermiculite presents a promising approach aligned with the principles of green chemistry. Vermiculite, a naturally abundant layered silicate mineral, was subjected to the acid activation and thoroughly characterized using X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM). These methods allowed the evaluation of crystallinity, structural stability, and surface morphology, which are critical parameters in the heterogeneous catalysis. The catalytic performance of the modified vermiculite was examined in the transformation of geraniol under mild conditions. The study systematically investigated the influence of key process parameters—temperature, reaction time, and catalyst content—on the conversion of geraniol and products selectivities. Optimization using the response surface methodology (RSM), enabled the identification of conditions leading to high conversion of geraniol (up to 85%) and allowing us to obtain favorable selectivities toward linalool, thunbergol, and 6,11-dimethyl-2,6,10-dodecatrien-1-ol. The results indicate that the acid-treated vermiculite exhibits sufficient surface acidity to effectively catalyze isomerization and cyclization reactions, without requiring additional promoters or metal-based systems. Moreover, the use of RSM provided the efficient framework for optimization reaction conditions, reducing experimental workload, and enhancing process efficiency. This study demonstrates the viability of natural, low-cost minerals as environmentally friendly catalysts and supports their integration into sustainable and “green” chemical technologies. Full article

Plastic pollution, particularly from polyethylene terephthalate (PET), poses significant environmental concerns due to ecosystem persistence and extensive packaging use. Conventional recycling methods face inefficiencies, high costs, and limited scalability, necessitating sustainable alternatives. Biodegradation via PET hydrolases offers promising eco-friendly solutions, although most natural PET-degrading enzymes are thermophilic and require energy-intensive high temperatures. In contrast, psychrophilic enzymes function efficiently at extremely low temperatures but often lack stability under moderate conditions. Therefore, this study aimed to enhance the ability of the Mors1 enzyme from Moraxella TA144 to operate effectively under mesophilic conditions, which is closer to the optimal conditions for environmental application. Three strategic hydrophobic substitutions (K93I, E221I, and R235F) were introduced in loop regions, generating the mutant variant Mors1^MUT. Comparative characterization revealed that Mors1^MUT retained 98% of its activity at pH 9 and displayed greater resilience across both acidic and alkaline conditions than did the wild-type enzyme. Thermal stability assays revealed that Mors1^MUT preserved 61% of its activity at 40 °C and 14% at 50 °C, whereas the wild-type enzyme was fully inactivated at these temperatures. The enzymatic hydrolysis of PET films significantly improved with Mors1^MUT. Gravimetric analysis revealed weight losses of 0.83% for Mors1^WT and 3.46% for Mors1^MUT after a 12-day incubation period. This corresponds to a 4.16-fold increase in hydrolysis efficiency, confirming the enhanced catalytic performance of the mutant variant. The improvement was further validated by scanning electron microscopy (SEM), atomic force microscopy (AFM), and attenuated total reflectance–Fourier transform infrared (ATR-FTIR) analysis. Optimization of the reaction parameters through response surface methodology (enzyme load, time, pH, temperature, and agitation) confirmed increased PET hydrolysis under mild mesophilic conditions. These findings establish Mors1^MUT as a robust mesophilic PETase with enhanced catalytic efficiency and thermal stability, representing a promising candidate for sustainable PET degradation under environmentally relevant conditions. Full article

Deicing salt effectively melts ice and snow to maintain traffic flow in seasonal freezing zones, but its erosion effect compromises the water stability and structural integrity of asphalt pavements. To comprehensively explore the impacts of salt erosion on the interfacial behaviors of rubber asphalt–aggregate systems, this study developed a multiscale characterization method integrating a macroscopic mechanical test, microscopic tests, and molecular dynamics (MD) simulations. Firstly, laboratory-controlled salt–freeze–thaw cycles were employed to simulate field conditions, followed by quantitative evaluation of interfacial bonding properties through pull-out tests. Subsequently, the atomic force microscopy (AFM) and Fourier transform infrared spectrometer (FTIR) tests were conducted to characterize the microscopic morphology evolution and chemical functional group transformations, respectively. Moreover, by combining the diffusion coefficients of water molecules, salt solution ions, and asphalt components, the mechanism of interfacial salt erosion was elucidated. The results demonstrate that increasing NaCl concentration and freeze–thaw cycles progressively reduces interfacial pull-out strength and fracture energy, with NaCl-induced damage becoming limited after twelve salt–freeze–thaw cycles. In detail, with exposure to 15 freeze–thaw cycles in 6% NaCl solution, the pull-out strength and fracture energy of the rubber asphalt–limestone aggregate decrease by 50.47% and 51.57%, respectively. At this stage, rubber asphalt exhibits 65.42% and 52.34% increases in carbonyl and sulfoxide indexes, respectively, contrasted by 49.24% and 42.5% decreases in aromatic and aliphatic indexes. Long-term exposure to salt–freeze–thaw conditions promotes phase homogenization, ultimately reducing surface roughness and causing rubber asphalt to resemble matrix asphalt morphologically. At the rubber asphalt–NaCl solution–aggregate interface, the diffusion of Na⁺ is faster than that of Cl⁻. Meanwhile, compared with other asphalt components, saturates exhibit notably enhanced mobility under salt erosion conditions. The synergistic effects of accelerated aging, salt crystallization pressure, and enhanced ionic diffusion jointly induce the deterioration of interfacial bonding, which accounts for the decrease in macroscopic pull-out strength. This multiscale investigation advances understanding of salt-induced deterioration while providing practical insights for developing durable asphalt mixtures in cold regions. Full article

When the spaceborne satellite target rotates non-uniformly relative to the ladar, the high-order space-variant phase will be introduced into the echo phase along both the range and azimuth direction, which will cause the degree of defocusing of the scatterers on the target to rely on their locations. Traditional imaging algorithms usually assume that the target is in uniform motion and only compensate for second-order phase errors, ignoring spatial phase variations caused by higher-order non-uniform rotation. Consequently, these algorithms are ineffective in accurately focusing on edge scatterers, leading to image blurring at the target boundaries. To solve this problem, an ISAL imaging algorithm for spaceborne non-uniformly rotating targets based on matched Fourier transform (MFT) and a genetic algorithm is proposed in this paper. First, the echo signal model of the non-uniform rotation target is established. Second, the corresponding higher-order space-variant phase compensation method based on the estimated parameters is proposed, with time-domain higher-order phase compensation along the range direction and MFT algorithm along the azimuth direction. Then, the genetic algorithm is employed for parameter estimation. Finally, the results obtained from both simulation experiments and real data experiments verify that the proposed algorithm has good compensation accuracy and robustness. Full article

Additive manufacturing via selective laser sintering (SLS) enables the rapid production of geometrically complex polyamide 12 (PA12) components. However, conventional pointwise analysis techniques often overlook the full depth of continuous experimental datasets, thus limiting the interpretation of structure–function relationships that are essential to high-performance design. This study employs functional data analysis (FDA) to elucidate the microstructural, chemical, thermal, and mechanical behaviours of SLS-fabricated PA12, focusing on the effects of build orientation (horizontal, transverse, vertical) and wall thickness (2.0–3.0 mm). The samples were produced via a commercial SLS platform and characterised via X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), and tensile testing. The FDA was applied to raw, normalised, and first derivative datasets via Python’s Scikit-FDA package, increasing the sensitivity to latent material variations. The findings demonstrate that the build orientation has a marked influence on the crystallinity and mechanical performance: horizontal builds yield narrower gamma-phase XRD peaks, greater structural order, and enhanced tensile properties, whereas vertical builds exhibit broader peak dispersion and greater thermal sensitivity. The wall thickness effects were minor, with only isolated flux-related anomalies. The FTIR spectra confirmed the consistent chemical stability across all the conditions. The FDA successfully identified subtle transitions and anisotropies that eluded traditional methods, underscoring its methodological strength for advanced polymer characterisation. These insights offer practical guidance for refining SLS process parameters and improving predictive design strategies in polymer-based additive manufacturing. Full article

Classifying brain tumour transcriptomic data is crucial for precision medicine but remains challenging due to high dimensionality and limited interpretability of conventional models. This study benchmarks three image-based deep learning approaches, DeepInsight, Fotomics, and a novel saliency-guided convolutional neural network (CNN), for transcriptomic classification. DeepInsight utilises dimensionality reduction to spatially arrange gene features, while Fotomics applies Fourier transforms to encode expression patterns into structured images. The proposed method transforms each single-cell gene expression profile into an RGB image using PCA, UMAP, or t-SNE, enabling CNNs such as ResNet to learn spatially organised molecular features. Gradient-based saliency maps are employed to highlight gene regions most influential in model predictions. Evaluation is conducted on two biologically and technologically different datasets: single-cell RNA-seq from glioblastoma GSM3828672 and bulk microarray data from medulloblastoma GSE85217. Outcomes demonstrate that image-based deep learning methods, particularly those incorporating saliency guidance, provide a robust and interpretable framework for uncovering biologically meaningful patterns in complex high-dimensional omics data. For instance, ResNet-18 achieved the highest accuracy of 97.25% on the GSE85217 dataset and 91.02% on GSM3828672, respectively, outperforming other baseline models across multiple metrics. Full article

The present research aims to investigate the impact of various mixing techniques (centrifugal planetary mixing, ultrasonication, and high-temperature magnetic stirring) on the properties of nanocomposite epoxy resins using expanded graphite particles. Differential scanning calorimetry reveals that the curing behavior and glass transition temperature are influenced by the selected method, indicating that a suitable choice allows increasing curing degree (C.D.) and glass transition temperature up to 10% and 12%, respectively. Morphological analysis performed via Scanning Electron Microscopy and Tunneling Atomic Force Microscopy offers detailed insights into the dispersion characteristics of fillers within polymer matrices, which sensitively affect the properties of the materials. The electrical conductivity values vary by more than five orders of magnitude among the various mixing methods. Centrifugal mixing leads to a decrease in the equilibrium concentration of water (C_eq) by up to 23% compared to that of the unfilled matrix, thanks to the chemical interactions that occur between the graphitic particles and the epoxy matrix (detectable via Fourier Transform Infrared Spectroscopy). Such a reduction is strongly desired in strategic fields such as the transport sector. The analysis of the obtained results suggests choosing the dispersion method of the filler in the matrix by considering the required performance for the specific planned application. Full article

This study explored the feasibility of using a plastic vacuum chamber for the Filament-Assisted Chemical Vapor Deposition (FACVD) of polymer thin films. Traditional chemical vapor deposition (CVD) methods often require high vacuum and elevated temperatures, which limit their use for heat-sensitive and flexible substrates. FACVD enables polymer deposition under mild vacuum and temperature conditions, providing an opportunity to utilize plastic vacuum chambers as cost-effective and easily machinable alternatives to metallic chambers. In this study, a custom-designed acrylic chamber was fabricated and integrated into an FACVD system. Glycidyl methacrylate (GMA) and tert-butyl peroxide (TBPO) were considered as the monomer and initiator, respectively, for creating thin films under a low-temperature and moderate-vacuum deposition process. Polymeric film (pGMA) contains reactive epoxy groups that allow versatile post-polymerization modifications and are widely applied in coatings and biomedical fields. Preliminary experiments demonstrated the successful growth of pGMA thin films, with Fourier-transform infrared spectroscopy (FT-IR) and X-ray photoelectron spectroscopy (XPS) confirming the characteristic polymer features, including the disappearance of the C=C stretching band as direct evidence of polymerization. Ellipsometry determines a uniformity of film thickness of approximately 85% for the 4-inch wafers’ area, with deposition rates in the range of 18–26 nm/h. These results highlight the potential of polymer-based chambers as cost-effective and versatile alternatives to advanced vapor-phase polymerization processes. Full article

Ensuring the authenticity of meat products is a critical issue for consumer protection, regulatory compliance, and the integrity of local food systems. In this study, attenuated total reflectance Fourier-transform infrared (ATR-FTIR) spectroscopy combined with chemometric and machine learning models was applied to differentiate Maltese from non-Maltese pork. Spectral datasets were subjected to a range of preprocessing techniques, including Savitzky–Golay first and second derivatives, detrending, orthogonal signal correction (OSC), and standard normal variate (SNV). Linear methods such as principal component analysis–linear discriminant analysis (PCA-LDA), the soft independent modeling of class analogy (SIMCA), and partial least squares regression (PLSR) were compared against nonlinear approaches, namely support vector machine regression (SVMR) and artificial neural networks (ANNs). The results revealed that derivative preprocessing consistently enhanced spectral resolution and model robustness, with the fingerprint region (1800–600 cm⁻¹) yielding the highest discriminative power. While PCA-LDA, SIMCA, and PLSR achieved high accuracy, SVMR and ANN models provided a superior predictive performance, with accuracies exceeding 0.99 and lower misclassification rates under external validation. These findings highlight the potential of FTIR spectroscopy combined with nonlinear chemometrics as a rapid, non-destructive, and cost-effective strategy for meat authentication, supporting both consumer safety and sustainable food supply chains. Full article

High-precision identification of dynamic parameters is crucial for the on-orbit performance of space manipulators. This paper investigates dynamic modeling and parameter identification under special environmental conditions such as microgravity and vacuum. First, a dynamic model of the manipulator incorporating a nonlinear friction term is established using the Newton-Euler method, and an improved Stribeck friction model is proposed to better characterize high-speed conditions and space environmental effects. On this basis, a hybrid parameter identification method combining Particle Swarm Optimization (PSO) and Levenberg–Marquardt (LM) algorithms is proposed to balance global search capability and local convergence accuracy. To enhance identification performance, Fourier series are used to design excitation trajectories, and their harmonic components are optimized to improve the condition number of the observation matrix. Experiments conducted on a ground test platform with a six-degree-of-freedom (6-DOF) manipulator show that the proposed method effectively identifies 108 dynamic parameters. The correlation coefficients between predicted and measured joint torques all exceed 0.97, with root mean square errors below 5.1 N·m, demonstrating the high accuracy and robustness of the method under limited data samples. The results provide a reliable model foundation for high-precision control of space manipulators. Full article

Ensuring the structural integrity of mechanical components is a key challenge in industries such as automotive, aerospace, and energy. Conventional techniques for defect identification, including non-destructive testing (NDT) and the Finite Element Method (FEM), offer reliable solutions—yet FEM often requires intensive modeling work and high computational cost. To streamline the detection process, this study proposes a method based on orthogonal moment transforms applied to digital images. This fast and automated technique is particularly suited for integration into industrial vision systems. The approach consists in encoding the visual features of a component using continuous orthogonal moments (e.g., Zernike, Chebyshev, or Fourier), and analyzing the extracted descriptors to identify irregularities associated with surface cracks or structural flaws. Full article

Thermoelectric (TE) materials represent a critical frontier in sustainable energy conversion technologies, providing direct thermal-to-electrical energy conversion with solid-state reliability. The optimizations of TE performance demand a nuanced comprehension of structure–property relationships across diverse length scales. This review summarizes established and emerging spectroscopic and microscopic techniques used to characterize inorganic and polymer TE materials, specifically poly(3,4-ethylenedioxythiophene): poly(styrenesulfonate) (PEDOT:PSS). For inorganic TE, ultraviolet–visible (UV–Vis) spectroscopy, energy-dispersive X-ray (EDX) spectroscopy, and X-ray photoelectron spectroscopy (XPS) are widely applied for electronic structure characterization. For phase analysis of inorganic TE materials, Raman spectroscopy (RS), electron energy loss spectroscopy (EELS), and nuclear magnetic resonance (NMR) spectroscopy are utilized. For analyzing the surface morphology and crystalline structure, chemical scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray diffraction (XRD) are commonly used. For polymer TE materials, ultraviolet−visible–near-infrared (UV−Vis−NIR) spectroscopy and ultraviolet photoelectron spectroscopy (UPS) are generally employed for determining electronic structure. For functional group analysis of polymer TE, attenuated total reflectance–Fourier-transform infrared (ATR−FTIR) spectroscopy and RS are broadly utilized. XPS is used for elemental composition analysis of polymer TE. For the surface morphology of polymer TE, atomic force microscopic (AFM) and SEM are applied. Grazing incidence wide-angle X-ray scattering (GIWAXS) and XRD are employed for analyzing the crystalline structures of polymer TE materials. These techniques elucidate electronic, structural, morphological, and chemical properties, aiding in optimizing TE properties like conductivity, thermal stability, and mechanical strength. This review also suggests future research directions, including in situ methods and machine learning-assisted multi-dimensional spectroscopy to enhance TE performance for applications in electronic devices, energy storage, and solar cells. Full article

Background/Objectives: Hypericum perforatum L. (H. perforatum), commonly known as St. John’s wort, has been widely used in clinical practice to treat mental disorders. Previous studies and clinical applications have primarily focused on its alcohol-soluble ingredients. Our research was designed to investigate the physicochemical properties, antidepressant-like effects, and anti-post-traumatic stress disorder (PTSD)-like effects of the alcohol-insoluble polysaccharide-rich ingredients from H. perforatum. Meanwhile, the underlying mechanisms were elucidated. Methods: The physicochemical properties of two polysaccharide-rich ingredients, designated as HPP1 and HPP2, were characterized using colorimetric assay, capillary electrophoresis, high-performance gel permeation chromatography, and fourier transform infrared spectroscopy. Behavioral despair tests were conducted to rapidly assess and compare their antidepressant-like effects in mice. Subsequently, behavioral despair mice and foot-shock mice were established to thoroughly explore the impact of HPP2 on depression-like and PTSD-like symptoms. The effects of HPP2 on cerebral pathological changes, neurotrophic factors, and gut microbiota in foot-shock mice were detected through hematoxylin & eosin staining, immunofluorescence staining, and 16S rDNA (V3 + V4 regions) gene sequencing. Results: HPP1 and HPP2 are predominantly composed of arabinose, glucose, galactose, mannose, and galacturonic acid. The molecular weight distribution of HPP1 ranges from 1133 to 67,278 Da, whereas that of HPP2 extends from 1493 to 38,407 Da. Acute pre-treatment with HPP1 or HPP2 (200 mg/kg, i.g.) could reduce mice’s immobility in behavioral despair tests, with HPP2 exhibiting superior efficacy. Additionally, both acute and sub-chronic pre-treatment with HPP2 (50, 200, and 800 mg/kg, i.g.) effectively alleviated depression-like symptoms in behavioral despair mice. Prolonged pre-treatment with HPP2 (200 mg/kg, i.g.) also mitigated the slow increase in body weight and behavioral abnormalities in foot-shock mice. Furthermore, HPP2 (200 mg/kg) successfully restored hippocampal histomorphological abnormalities, neurotrophic disturbance, and dysregulation of the gut microbiota in foot-shock mice. Conclusions: HPP2 exerts noteworthy antidepressant-like and anti-PTSD-like impact in mouse models via multiple targets, indicating a potential therapeutic candidate in depression and PTSD therapy. Full article

Nectar composition varies across plant species and environments, influencing pollinator interactions and honey quality. Reliable methods for nectar discrimination, however, remain limited. Here, we demonstrate the use of Fourier-transform infrared (FTIR) spectroscopy combined with chemometric analysis to differentiate nectar samples of Echium vulgare (E. vulgare) and Hedera helix (H. helix) collected in urban locations. Among eight tested preprocessing strategies, simple approaches such as Savitzky–Golay smoothing or even raw spectra provided the best clustering results. The most discriminative spectral regions were consistently the carbohydrate fingerprint (1200–950 cm⁻¹) and the C–H stretching zone (2935–2885 cm⁻¹). Mean spectra and PCA confirmed that variability between locations arises mainly from carbohydrate-associated bands, while solvent type, biological matrix, and environmental exposure also affect spectral fingerprints. These results highlight FTIR spectroscopy as a rapid, non-destructive, and robust method for nectar discrimination, with potential applications in food authentication, ecological research, and pollinator–plant studies. Full article

Reliable DC cable insulation is crucial for photovoltaic (PV) systems and high-voltage DC (HVDC) networks. However, conventional materials such as cross-linked polyethylene (XLPE) and polyvinyl chloride (PVC) face challenges under prolonged DC stress—notably space charge buildup, dielectric losses, and thermal aging. Cross-linked polyolefin (XLPO) has emerged as a halogen-free, thermally stable alternative, but its comparative DC performance remains underreported. Methods: We evaluated the insulations of virgin XLPO, XLPE, and PVC PV cables under ±1 kV DC using time-domain indices (IR, DAR, PI, Loss Index), supported by MATLAB and FTIR. Multi-layer cable geometries were modeled in MATLAB to simulate radial electric field distribution, and Fourier-transform infrared (FTIR) spectroscopy was employed to reveal polymer chemistry and functional groups. Results: XLPO exhibited an IR on the order of 10⁸–10⁹ Ω, and XLPE (IR ~ 10⁸ Ω) and PVC (IR ~ 10⁷ Ω, LI ≥ 1) at 60 s, with favorable polarization indices under both polarities. Notably, they showed high insulation resistance and low-to-moderate loss indices (≈1.3–1.5) under both polarities, indicating controlled relaxation with limited conduction contribution. XLPE showed good initial insulation resistance but revealed polarity-dependent relaxation and higher loss (especially under positive bias) due to trap-forming cross-linking byproducts. PVC had the lowest resistance (GΩ-range) and near-unit DAR/PI, dominated by leakage conduction and dielectric losses. Simulations confirmed a uniform electric field in XLPO insulation with no polarity asymmetry, while FTIR spectra linked XLPO’s low polarity and PVC’s chlorine content to their electrical behavior. Conclusions: XLPO outperforms XLPE and PVC in resisting DC leakage, charge trapping, and thermal stress, underscoring its suitability for long-term PV and HVDC applications. This study provides a comprehensive structure–property understanding to guide the selection of advanced, polarity-resilient cable insulation materials. Full article