Search Results (845)

Background/Objectives: Goupi plaster (GP) is a traditional black plaster composed of a biphasic fibrous–oil matrix containing multiple bioactive compounds, and it has been widely used for the treatment of musculoskeletal disorders. Representative active compounds include sinomenine, osthole, cinnamaldehyde, and imperatorin, which exhibit anti-inflammatory and analgesic effects. However, due to its heterogeneous matrix structure and multi-component nature, the pharmaceutical delivery behavior of GP remains difficult to evaluate using conventional methods. Therefore, this study aimed to establish an integrated structure–release–permeation–pharmacokinetic evaluation framework to systematically characterize the transdermal delivery behavior of GP. Methods: GP was evaluated using multi-level analysis, including microstructural imaging (FESEM), in vitro release, ex vivo skin permeation, and in vivo dual-site microdialysis. Four representative bioactive compounds (sinomenine, osthole, cinnamaldehyde, and imperatorin) were selected as marker compounds. Release data were fitted to kinetic models, and structure–release relationships were examined using the Higuchi release constant (k_h). Skin-barrier alterations were assessed by attenuated total reflectance–Fourier transform infrared spectroscopy (ATR–FTIR) and differential scanning calorimetry (DSC). Local concentrations in subcutaneous (SC) and intra-articular (IA) compartments were measured by ultra-performance liquid chromatography–tandem mass spectrometry (UPLC–MS/MS) to explore potential in vitro–in vivo correlation (IVIVC). Results: FESEM revealed a fibrous–oil network structure. GP exhibited sustained, diffusion-dominated release, with k_h = 0.9908–0.9977 and Korsmeyer–Peppas (K–P) release exponents (n) = 0.61–0.66, differing from active pharmaceutical ingredient (API) controls. Fiber area fraction and fiber length density showed negative correlations with k_h (r = −0.91 to −0.99); ex vivo permeation profiles varied among compounds, and ATR–FTIR and DSC analyses showed moderate changes in skin-barrier properties. Dual-site microdialysis demonstrated sustained local exposure, and a positive relationship was observed between in vitro release and in vivo concentrations. Conclusions: This study establishes an integrated structure–release–permeation–pharmacokinetic evaluation framework for traditional black plaster systems. The observed IVIVC is descriptive rather than predictive, reflecting a trend-level association under the current experimental conditions. These findings highlight the importance of integrating in vitro release, skin permeation, and local pharmacokinetics for understanding drug delivery behavior in complex transdermal matrix systems, and provide a methodological basis for quality consistency evaluation of traditional black plaster formulations. Full article

Accurate estimation of curvature radius from interference fringes is critical in optical metrology and precision manufacturing. Conventional interferogram analytical approaches often require manual intervention and are sensitive to fringe variations related to noise and environmental vibrations. To address these limitations, we combine an improved Twyman–Green interferometer with different artificial intelligence (AI) deep learning models and utilize a self-developed MATLAB analysis program to propose a non-destructive and rapid measurement system for optical coating substrates. The proposed AI-assisted Twyman–Green interferometric system differs fundamentally from conventional wavefront sensing techniques in both principle and implementation. This paper utilizes the Twyman–Green interferometer to generate interference fringe datasets on B270 glass and sapphire substrates, and employs convolutional neural network (CNN), ResNet-18, and VGG-16 models for training and evaluation. The proposed method integrates image enhancement, fringe pattern clustering, and analysis and validation based on fast Fourier transform (FFT). Experimental results show that ResNet-18 outperforms other models, with a mean absolute percentage error of 5.44% on sapphire substrates and 3.40% on B270 glass substrates. These findings highlight the effectiveness and robustness of deep learning models, especially residual networks, in automatic ROC prediction for optical measurement applications. Full article

Hydrogels derived from biopolymers have attracted considerable interest in biomedical applications because of their biocompatibility and structural similarity to the extracellular matrix (ECM). Bacterial Cellulose (BC), despite being a promising biopolymer for hydrogel preparation, lacks antimicrobial properties itself. To address this drawback, we prepared Probiotic Bacterial Cellulose (PBC) in our laboratory, which has intrinsic antibacterial properties. No research was found on the preparation of a hydrogel using PBC and κ-carrageenan, which motivated us to develop a PBC/κ-carrageenan-based hydrogel. In the study, a novel biocomposite hydrogel system has been developed by integrating PBC with κ-carrageenan, yielding a multifunctional hydrogel with enhanced antibacterial properties and biocompatibility. The novel hydrogel has been evaluated for its structural, physicochemical, antibacterial, and biocompatible properties. Fourier transform infrared spectroscopy (FTIR) analysis confirmed the formation of intermolecular interactions between PBC and κ-carrageenan. Scanning electron microscopy (SEM) images revealed a porous internal morphology and the presence of probiotic bacteria within the hydrogel networks. Porosity analysis and swelling behaviour indicated an elevated water uptake capacity and structural stability. The composite hydrogel demonstrated promising antibacterial properties against pathogenic bacteria Escherichia coli (Gram-negative) and Staphylococcus aureus (Gram-positive) and exhibited favourable in vitro biocompatibility. The developed PBC/κ-carrageenan hydrogel exhibits a synergistic combination of porosity, swelling capacity, biocompatibility, and antibacterial activity, making it a potential candidate for healthcare applications viz. wound healing and other tissue engineering applications. Full article

Calcium formate (Ca(HCOO)₂) is an important industrial chemical widely used in construction, feed additives, and various chemical processes. In this work, calcium formate was synthesized from cockle shell waste and concentrated formic acid (50%, 60%, and 70% w/w) by a simple, rapid, low-cost, and environmentally friendly process, denoted as CF50, CF60, and CF70, respectively. The chemical and physical properties of as-synthesized calcium formate using cockle shells as a renewable calcium source were investigated by Fourier transform infrared (FT-IR), X-ray diffraction (XRD), X-ray fluorescence (XRF), Thermal gravimetric analysis (TGA), and scanning electron microscopy (SEM) techniques. The FTIR and XRD results revealed that the samples prepared using 50% and 60% formic acid produced well-crystallized α-calcium formate. In contrast, the reaction using 70% formic acid generated a strongly exothermic reaction, which hindered the complete conversion of calcium carbonate and resulted in the presence of residual CaCO₃ in the final product. Similarly, the SEM images of the CF50 and CF60 samples show the slick surface of orthorhombic crystals of calcium formate; on the other hand, the SEM image of CF70 shows some small particles of aragonite on the surface of the calcium formate crystals. The 60% formic acid provided the optimal synthesis condition, yielding pure calcium formate with the shortest synthesis time. Overall, the proposed approach provides a simple, rapid, and cost-effective route for producing calcium formate from shell waste. Furthermore, the utilization of cockle shell waste as a renewable calcium source contributes to waste valorization, reduces environmental impacts associated with shell disposal, and minimizes dependence on mined limestone resources, supporting sustainable resource utilization within a circular economy. Full article

Broiler respiratory rate (RR) in cage systems is a core physiological indicator of health and stress. However, real-time, non-invasive continuous RR monitoring is difficult in a high-density breeding environment, thereby limiting precise poultry health management. This study developed a feasible non-contact broiler RR measurement method to address this gap. The proposed method integrates infrared thermal imaging and phase-based video magnification (PBVM). Using cage-reared white-feathered broilers as subjects, we selected the thoracodorsal and tail regions as regions of interest (ROI), applied PBVM to amplify subtle respiratory-related body surface movements, and extracted RR features via the Fast Fourier Transform (FFT). Two validation experiments were conducted under controlled laboratory conditions. One was an RR dynamic monitoring experiment covering the entire life cycle (4 to 36 days), which analyzed video data of 198 individual quiet broilers. The other was a multi-gradient heat stress experiment with temperature increases of +2 °C, +4 °C, and +5 °C, and analyzed video data of 162 individual quiet broilers. The method achieved favorable measurement accuracy: in the whole-life-stage experiment, the mean absolute error (MAE) was 0.036 Hz, the mean absolute percentage error (MAPE) was 4.461%, and the coefficient of determination (R²) reached 0.961; in the heat stress experiment, the MAE was 0.042 Hz, the MAPE was 3.270%, and the R² reached 0.928. Linear regression analysis confirmed that healthy broiler RR decreased linearly with increasing age, and verified that RR showed a stepwise response to thermal challenge with a positive correlation between RR increase and temperature increment, accompanied by growth stage specificity. This study provides a feasible non-invasive approach for broiler RR monitoring, offering preliminary reference data for early heat stress detection and sustainable poultry production. Full article

The methods employed for image demosaicing and denoising play a pivotal role in image acquisition and restoration, and have been extensively studied over the past few decades. Traditionally, these tasks are performed sequentially, with demosaicing followed by denoising, or vice versa, treating each process independently. While this approach can enhance image quality, it often leads to issues such as color inaccuracies and information loss, as the outcome of the first task influences the second. Consequently, the integration of joint demosaicing and denoising (JDD) has become a focal point in recent research. Deep convolutional neural networks have shown promising results in addressing JDD challenges. This study introduces an end-to-end network, termed the Frequency-domain Features learning Network (FFNet), designed to tackle the JDD problem. Unlike conventional methods that focus on spatial domain features, FFNet utilizes frequency-domain (FD) characteristics to capture both global and local image details. Based on the vision Transformer architecture, FFNet consists of two key components: a global Fourier block (GFB), which uses global attention to determine the weights of FD parameters, and an MLP-based local Fourier block (LFB), which improves local feature extraction. These blocks are integrated with a channel attention mechanism to form the frequency-domain attention block (FAB), the core element of FFNet. Extensive experimental results on benchmark datasets demonstrate that FFNet achieves superior performance in terms of both quantitative metrics (PSNR/SSIM) and visual quality compared to existing state-of-the-art JDD methods. Furthermore, we provide a comprehensive analysis of its computational efficiency, including parameter count, FLOPs, and inference time, showing a competitive trade-off between performance and complexity. Full article

An eco-friendly green synthesis approach was employed to produce copper nanoparticles (CuNPs) using a polyherbal extract derived from two medicinally important plant species, Hygrophila auriculata (Schumach.) Heine and Leucas aspera (Willd.) Link. The plant extracts were initially subjected to phytochemical screening to identify bioactive constituents potentially involved in nanoparticle synthesis. The synthesized CuNPs were characterized using UV-visible spectroscopy, Fourier-transform infrared spectroscopy (FTIR), gas chromatography–mass spectrometry (GC-MS), field-emission scanning electron microscopy coupled with energy-dispersive X-ray analysis (FESEM-EDAX), X-ray diffraction (XRD), and thin-layer chromatography (TLC). UV-visible spectroscopy revealed a characteristic absorption peak at 233.6 nm. FTIR analysis indicated the presence of functional groups associated with nanoparticle reduction and stabilization, whereas FESEM imaging showed predominantly spherical particles with sizes ranging 63–68 nm. Elemental composition was confirmed using EDAX analysis. XRD analysis demonstrated polycrystalline nature of the CuNPs, with an average crystallite size of 11.5 nm. GC-MS analysis and phytochemical screening further confirmed the presence of bioactive compounds, whereas TLC analysis revealed differences in mobility between the plant extract and synthesized CuNPs. Antibacterial activity of the synthesized CuNPs was evaluated using the agar well diffusion method against clinically relevant bacterial strains, including those of Staphylococcus aureus, Pseudomonas aeruginosa, Escherichia coli, and Streptococcus pyogenes. The polyherbal-derived CuNPs produced larger inhibition zones than the individual plant extracts, particularly against multidrug-resistant pathogens such as P. aeruginosa and S. aureus. Additionally, the nanoparticles exhibited concentration-dependent antioxidant activity in the 2,2-diphenyl-1-picrylhydrazyl assay at concentrations ranging 10–50 mg/mL, with radical scavenging activity increasing from 29.9% to 76.5% and a corresponding decrease in absorbance from 0.698 to 0.234 (p < 0.05). Cytotoxic evaluation in HepG2 cells after 48 h of exposure demonstrated dose-dependent morphological changes and reduced cell viability. These findings suggest that polyherbal-derived CuNPs possess antibacterial, antioxidant, and cytotoxic properties with potential relevance for biomedical applications. Full article

Hafnium carbide (HfC) ceramics are of growing interest due to their exceptional mechanical properties and ultra-high melting points, making them ideal for extreme environmental applications. In this study, we present a synthesis route for HfC nanoparticles via carbothermal reduction of an organic–inorganic hybrid precursor derived from hafnium tetrachloride (HfCl₄) and pectin, followed by thermal treatment at 1500 °C for 1.5 h under an argon atmosphere. According to TGA/DSC analysis of the hybrid precursor, hafnia phases initially formed during pyrolysis and were subsequently converted into HfC at 1500 °C, with the endothermic carbothermal reduction reaction initiating near 1200 °C. Comprehensive characterization using Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis/differential scanning calorimetry (TGA/DSC), X-ray diffraction (XRD) with Rietveld refinement, scanning electron microscopy (SEM), and transmission electron microscopy (TEM) confirmed the synthesis of hafnium carbide (HfC) exhibiting predominantly cubic morphology. XRD analysis determined a lattice parameter of a = 4.63 Å and an interplanar spacing of d = 2.68 Å. Rietveld refinement revealed a phase composition of 98.08% HfC and 1.92% monoclinic hafnium dioxide (m-HfO₂). Debye–Scherrer analysis indicated an average crystallite size of 67.6 nm. SEM and TEM images showed uniformly distributed nanoparticles with an average particle size of approximately 65–70 nm. Full article

Bisphenol A (BPA) is a widespread endocrine disruptor that interferes with metabolism in humans and animals by inducing oxidative stress, lipid peroxidation, and cell death. Probiotics, conversely, have shown potential in promoting host health and reducing the toxicity of endocrine-disrupting chemicals (EDCs). This study examined whether sub-chronic BPA exposure disrupts hepatic lipid metabolism in female zebrafish (Danio rerio), and whether co-administration of probiotics mitigates these effects. Adult females were exposed for 28 days to the following treatments: 10 µg/L BPA via water (BPA); 10⁹ CFU/g body weight/day of probiotic formulation (P); and both treatments (BPA+P). An untreated group served as a control (CTRL). Hepatic lipid composition was analyzed using UHPLC-QTOF-MS, while liver sections were investigated by Fourier Transform Infrared Imaging (FTIRI) spectroscopy. BPA exposure decreased 14 unsaturated triacylglycerols and lysophosphatidylcholine 18:0, suggesting steatosis onset and inflammation, while in the group exposed to BPA+P, the decrease was limited to 8 triacylglycerols and the reduction in lysophosphatidylcholine 18:0 was prevented. Analyses of pooled liver samples precluded modeling tank-level effects; thus, the results are interpreted as semi-quantitative. Partial least square discriminant analysis built on the comparison of all groups together confirmed an intermediate phenotype for BPA+P fish between BPA and P groups. The observed beneficial role of probiotics in counteracting BPA-related metabolic disturbances was also supported by FTIRI, evidencing the ability to mitigate the effects of BPA on lipid and glycosylated compound metabolism. These findings highlight the potential of probiotic supplementation as a practical and accessible strategy to mitigate BPA-induced metabolic disturbances, contributing to the development of mitigating approaches against environmental contaminant-related liver dysfunction. Full article

In this study, the in situ solvothermal synthesis of a copper-based metal–organic framework (Cu-BTC MOF) into two porous ceramic substrates with a 10 cm diameter and 2 cm thickness was reported. X-ray diffraction (XRD), Fourier transform infrared (FT-IR) spectroscopy, diffuse reflectance spectroscopy (DRS), Tauc plot analysis, optical microscopy, scanning electron microscopy (SEM), and transmission electron microscopy (TEM) were the techniques that were utilized to verify the formation and incorporation of the MOF into ceramics (two samples, with different SiO₂ particles; 500 µm (Ceramic 1), and 150 µm (Ceramic 2)). The synthesized Cu-MOF exhibited a crystalline structure. Both the composites and the Cu-MOF exhibited visible-light absorption, with optical band gaps of 2.5 eV and 2.4 eV, respectively, as determined by DRS. TEM images demonstrated that crystalline MOF domains were successfully included inside the ceramics. Methyl orange (MO), Congo red (CR), and methylene blue (MB) were used to assess the composites’ ability to remove dyes. Catalytic hydrogenation, powered by in situ hydrogen production from NaBH₄ hydrolysis, demonstrated high removal efficiencies of 91–97% after 60 min. Adsorption, on the other hand, was ineffective. Despite undergoing four consecutive cycles without performance degradation, the materials demonstrated remarkable recyclability. Cu-MOF@ceramic composites are effective, durable, and practically applicable for improved wastewater treatment. Full article

Effluents from industries, manufacturing companies, textile looms, and floodwater contaminate the surface water reservoirs. This endangers the quality of water for use by humans. Wastewater remediation is one of the ways to recycle the dirty water and make it suitable for use. Photocatalysis is the most common method for wastewater remediation, especially using Titanium dioxide (TiO₂) nanoparticles. However, chemical synthesis and direct addition of nanoparticles may cause toxicity to the flora and fauna present in the water body. To address this limitation, we have green-synthesized TiO₂ nanoparticles using a horticulture waste, Calendula officinalis dried flower extract and entrapped them in a natural polymer, chitosan (CTS-TiO₂-CO nanocomposite). The polymer entrapment ensures biocompatibility as well as reduced aggregation of nanoparticles. The synthesized CTS-TiO₂-CO nanocomposite was characterized using UV-visible spectrophotometry, dynamic light scattering, zeta potential, Fourier Transformed Infrared Spectroscopy (FTIR), X-ray diffractometry (XRD), scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDAX) analysis. The absorption peak was found at 302 nm, and the hydrodynamic diameter at 490 nm. SEM images show flower-like morphology with 326 nm average particle diameter. The non-toxic dose of the nanoparticles was estimated by MTT assay and zebrafish embryo developmental studies. More than 82% fibroblast cells were viable after treatment with 100 μg/mL of CTS-TiO₂-CO nanocomposite. 85% embryos hatched after treatment with 50 μg/mL of CTS-TiO₂-CO nanocomposite. Further, the textile dye remediation assessment was done using the dye crystal violet, exhibiting 69.19% dye degradation after 4 h of sunlight exposure. Altogether, the results demonstrate that the CTS-TiO₂-CO nanocomposite was effective in the remediation of crystal violet without causing any toxicity up to a dose of 100 μg/mL. Full article

Blended nanocomposite solid polymer electrolytes are gaining considerable attention as next-generation materials for use in flexible lithium-ion battery systems. These materials help ensure a more uniform distribution of lithium ions at the electrode–electrolyte interface, contributing to the development of a stable interfacial layer that mitigates lithium dendrite formation. In this study, solid polymer electrolytes were synthesized using a binary polymer matrix composed of polyethylene oxide (PEO) and polymethyl methacrylate (PMMA), with lithium iodide (LiI) as the ionic salt. Zirconium dioxide (ZrO₂) nanoparticles were introduced as nanofillers in varying concentrations to investigate their influence on the physical and functional characteristics of the polymer matrix. Characterization was carried out using Scanning Electron Microscopy (SEM), Fourier Transform Infrared Spectroscopy (FTIR), and X-ray Diffraction (XRD). SEM images indicated that ZrO₂ nanoparticles remained well-dispersed up to 3 wt%, while higher loadings showed slight agglomeration. FTIR analysis revealed noticeable changes in absorption bands, suggesting strong interactions among polymer chains and the nanofillers. XRD data confirmed the semi-crystalline behavior of the PEO/PMMA blend system. The inclusion of ZrO₂ nanofillers enhanced the structural integrity and ionic conductivity of the polymer matrix, making them promising candidates for applications in electrochemical energy storage and advanced material interfaces. The systematic incorporation of ZrO₂ nanofillers into the PEO/PMMA matrix significantly improved the microstructural uniformity, polymer–filler interactions, and ionic transport behavior of the solid polymer electrolytes. Full article

With the increasing demand for detecting small water bodies in remote sensing imagery, particularly in Synthetic Aperture Radar (SAR) images, traditional water body extraction models often struggle to capture fine details and accurately delineate boundaries. To address these challenges, this paper introduces the integration of Frequency-Selective Deformable Convolution (FSDC) into the TransUNet architecture, optimizing water body extraction in SAR imagery. FSDC enhances feature representation in both the frequency and spatial domains. It does so through two key components: (1) The Frequency Selection Module, which employs Fourier transform to selectively enhance or suppress features across different frequency bands, thereby emphasizing the unique structure and boundaries of water bodies. (2) The Deformable Convolution Unit, which dynamically adjusts the receptive field via content-based sampling, allowing it to adapt to local variations at multiple scales and improve fine detail capture. After incorporating FSDC into the decoder of TransUNet, experimental results on the NY and C2S-MS datasets show a significant improvement in extraction accuracy, especially in detecting small water bodies. These findings underscore the effectiveness of the FSDC mechanism for small water body extraction from SAR imagery, offering a novel solution for precise water body analysis in remote sensing. Full article

A hybrid material based on the copolymerization of EDOT (3,4-ethylenedioxythiophene) and Py (pyrrole), 1:1 monomer ratio, onto multi-walled carbon nanotubes (MWCNTs) was synthesized through a multistep functionalization approach. The resulting P(EDOT-co-Py)@MWCNT hybrid, poly(3,4-ethylenedioxythiophene-co-pyrrol)@MWCNT hybrid, was characterized by Fourier-transform infrared spectroscopy (FTIR), Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), and thermogravimetric analysis (TGA). These characterizations confirmed the successive functionalization steps, the effective anchoring of the monomers, and the subsequent formation of the copolymer. Transmission electron microscopy (TEM) images revealed a homogeneous polymer coating along the nanotube surface while preserving the structural integrity of the MWCNTs throughout the functionalization and polymerization processes. The P(EDOT-co-Py)@MWCNT hybrid was evaluated as an active electrode material for aluminum-ion storage in an aqueous aluminum sulfate electrolyte. The system exhibited two distinct charge-storage mechanisms: at high current densities, proton surface adsorption dominated, whereas at lower rates, a faradaic contribution associated with polymer chain redox activity and the reversible extraction/insertion of Al³⁺ became prevalent. The hybrid electrode delivered high specific capacities, reaching 200.6, 106.3, and 44.3 mAh g⁻¹ at 0.10, 0.25, and 0.50 A g⁻¹, respectively. These values are comparable to—or even exceed—those reported for similar cathodic materials designed for Al³⁺ storage, highlighting P(EDOT-co-Py)@MWCNT hybrid as a highly promising cathode candidate for aqueous aluminum-ion energy-storage systems. Full article

Core-collapse supernovae (CCSNe) remain a critical focus in the search for gravitational waves in modern astronomy. Their detection and subsequent analysis will enhance our understanding of the explosion mechanisms in massive stars. This paper investigates the use of convolutional neural networks (CNN) to enhance the detection of gravitational waves originating from CCSNe. We employ two time–frequency analysis techniques to generate spectrograms (training data): short-time Fourier transform (STFT) and Q-transform (QT). Two CNNs were trained independently on sets of spectrogram images of simulated CCSNe signals and advanced LIGO noise. The CNNs detect CCSNe signals based on their time–frequency representation. Both CNNs achieve a near 100% true positive rate for CCSNe GW events with a signal-to-noise ratio greater than 0.5 in our test set. Nevertheless, the CNN trained on the STFT spectrograms outperforms the one based on the Q-transform for SNRs below 0.5. Full article