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Comparative study of antimicrobial activity of silica-based nanohybrids functionalized with 5-aminosalicylic acid: toward reduced silver usage
No full textIn this study, we report the synthesis, characterization, and antimicrobial evaluation of two silica-based hybrid nanocomposites: SiO2 functionalized with 5-aminosalicylic acid (5-ASA), and its silver-decorated counterpart, SiO2/5-ASA/Ag. The organic ligand 5-ASA was covalently anchored onto the surface of amorphous silica nanoparticles, forming interfacial charge-transfer (ICT) complexes capable of visible-light absorption, as confirmed by UV–Vis diffuse reflectance spectroscopy and supported by DFT/TD-DFT calculations. The subsequent deposition of silver nanoparticles resulted in the formation of plasmonic nanohybrids with enhanced light-harvesting properties. The materials were extensively characterized using FTIR, TGA/DTA, XRD, HRTEM/EDX, and DRS techniques. Their antimicrobial activities were assessed against Escherichia coli, Staphylococcus aureus, and Candida albicans using time-resolved CFU assays at multiple concentrations. Both hybrids demonstrated significant antimicrobial performance; however, notably, the silver-free SiO2/5-ASA sample exhibited potent bactericidal activity, particularly against S. aureus, even at low concentrations. This finding suggests that the presence of –NH2 groups from the 5-ASA ligand contributes to antimicrobial action via interactions with bacterial cell walls, highlighting the potential for silver-free nanomaterials in antimicrobial applications. The results support the development of multifunctional ICT-based nanohybrids with reduced reliance on metallic silver, addressing growing environmental and regulatory concerns
Radiological analysis of soil samples, black walnut leaves, fruits, and extracts from three locations in Serbia and risk assessment due to black walnut extract ingestion
No full textThe walnut tree (Juglans) and its products have found applications in human nutrition, medicine, and industry. Although the common walnut is the most commercially important, it is followed by the black walnut. Extracts of the black walnut fruit or leaf are used in traditional medicine worldwide due to their antifungal properties. Soil, black walnut leaf, and fruit samples were collected from three locations in Serbia (Aleksinac, Stara Pazova, and Despotovac). Black walnut leaf and fruit extracts were prepared by ultrasonic-assisted extraction. All samples were radiologically analyzed using a semiconductor HPGe spectrometer system. The results showed that 137Cs were detected in the soil samples, while their activity concentrations were below the detection level in the leaves, fruits, and extracts for all three locations. Among the soil samples, natural radionuclide activity concentrations were highest in Stara Pazova (210Pb – (74.3 ± 4.6) Bq/kg; 226Ra – (52.2 ± 4.0) Bq/kg; 235U – (2.7 ± 0.4) Bq/kg; 238U – (48.9 ± 4.6) Bq/kg; 228Ac – (56.7 ± 3.3) Bq/kg), except for the 40K, which showed the highest activity concentration at Despotovac ((733 ± 37) Bq/kg). The specific activities of natural radionuclides (210Pb, 226Ra, 235U, 238U, 228Ac, and 40K) were used to calculate the annual effective doses due to ingestion for each radionuclide, as well as the total annual effective dose. The total annual effective doses for all age groups (one-year-olds, ten-year-olds, and adults) and extract types were found to be less than 100 μSv. Among the age groups, the highest annual effective dose was observed in one-year-olds (exceeding 50 μSv for all the fruit extracts; the highest value is 86.0 μSv for Despotovac), while adults experienced negligible doses, not exceeding 20 μSv
Emerging antimicrobial hydrogel dressings based on bacterial cellulose–chitosan composites: A review
No full textWounds result from disruptions in the tightly regulated wound healing process, which is often complicated by comorbidities, infections, and biofilm formation. Advancing therapeutic strategies relies on a comprehensive understanding of cellular and molecular mechanisms, with particular emphasis on host-pathogen interactions. Early intervention can prevent biofilm maturation through antioxidant and anti-inflammatory treatments administered within 24 h post-debridement. Furthermore, growth factors can promote angiogenesis and tissue regeneration. The escalating challenge of antibiotic resistance highlights the critical need to develop effective alternative therapies. Antimicrobial hydrogel wound dressings composed of nanomaterials represent a promising strategy that integrates effective infection control with accelerated tissue regeneration. This review focuses on composite materials composed solely of bacterial cellulose and chitosan, which exhibit excellent antibacterial, antibiofilm, and antioxidant activities along with high porosity, superior fluid absorption capacity, and strong support for cell migration. The originality of this work lies in its detailed discussion of the influence of these composites on the molecular mechanisms underlying angiogenesis. Preclinical and clinical studies support their potential in promoting wound healing. Future advancements are likely to involve the development of smart hydrogels incorporating pH- and temperature-responsive sensors and controlled-release systems, leading to more effective and sustainable approaches to wound care
Combining two biocompatible singlet oxygen generators into a potent photoactive agent: Graphene quantum dots-curcuma hybrid
No full textAs a cutting-edge treatment strategy, photodynamic therapy (PDT) utilizes light-sensitive compounds to selectively destroy target cells, offering extensive possibilities for managing numerous medical conditions, including bacterial infections and tumors. Graphene quantum dots (GQDs), zero-dimensional nanomaterials, possess unique properties that make them ideal for biomedical applications, including high dispersibility, biocompatibility, and luminescent emission. In this study, we synthesized GQDs and modified them by incorporating curcumin, a natural compound known for its antimicrobial properties. Both UV–Vis and infrared spectroscopies confirmed the formation of the hybrid material. AFM and DLS analyses revealed an increase in particle size and changes in zeta potential, indicating successful incorporation of curcumin. Notably, the modified GQDs exhibited enhanced singlet oxygen production under blue light irradiation, as proven with EPR spectroscopy and ABDA degradation, demonstrating their potential as a photosensitizer of a new generation for PDT
First-principles calculations of the structural, electronic, elastic and thermodynamic properties of MgAl2O4:Ti3+ and ZnAl2O4:Ti3+
No full textDetailed first-principles calculations of structural, electronic, elastic, thermodynamic and vibrational properties of two spinel crystals, MgAl2O4 (MAO) and ZnAl2O4 (ZAO): neat and doped with Ti3+ ions, at ambient and elevated hydrostatic pressure, are reported. Special attention is given to the location of Ti3+ 3d level in the hosts’ band gap. Various exchange-correlation functionals are employed for that purpose; the best agreement with the experimental data is obtained for the M06 functional, which places the Ti3+ state at 4.39 eV above the valence band top in MgAl2O4 and at 4.08 eV in ZnAl2O4. Crystal field splitting of the Ti3+ 3d states is calculated for different pressures; dependence of the crystal field strength 10Dq on pressure and Ti3+–O2− distance is analyzed. Our calculations of the Debye temperature (based on the knowledge of elastic constants) result in close agreement with the corresponding experimental data. Doping with Ti3+ ions leads to a slight decrease of the elastic parameters and lowering the Debye temperature by 20–40 K, because the Ti3+–O2− chemical bonds become longer and softer when compared with the Al3+–O2− ones in undoped materials. As a result, slight red shift of the most prominent features in the vibrational spectra is expected; this is confirmed by the performed calculations. Obtained results give a deeper insight into the properties of doped optical materials, highlight the effect of added impurity ions on their physical parameters and may serve as useful guides for smart materials engineering with wide opportunities of fine tuneability of their most important characteristics for potential applications
Curing Mechanisms of an Allyl-Functionalized Preceramic Polymer With Radical Initiators: Kinetics and Thermodynamics
No full textThis study investigates the thermal curing behavior of allyl-functionalized SMP-10, a preceramic polymer used as a silicon carbide (SiC) precursor in ceramic matrix composites (CMCs). The relatively high curing temperature of SMP-10 may pose a significant processing challenge, as it can impact quality, microstructure, and performance of the composite. However, the allyl group enables radical-initiated crosslinking pathways. To address this, the effect of radical initiators, dicumyl peroxide (DCP) and Luperox 101, on lowering the curing temperature was examined. Using non-isothermal differential scanning calorimetry (DSC) at heating rates of 0.5, 1, 2.5, 5, and 10 (Formula presented.) /min, the behavior of pure SMP-10 and systems with 2 wt.% initiator was monitored. Kinetic analysis was performed using Kissinger and Ozawa peak-based methods, model-free isoconversional methods (Kissinger–Akahira–Sunose [KAS], Flynn–Wall–Ozawa [FWO], and Starink) and model fitting method (Master plot). The results showed that the initiators significantly lower the onset curing temperature. However, the apparent activation energy ((Formula presented.)) increases from approximately 116–122 kJ/mol for pure SMP-10 to 141–153 kJ/mol for the system with DCP and 155–156 kJ/mol for the system with Luperox 101. To better understand this trend, transition state theory was applied. It was found that the acceleration is not driven by a reduction in the enthalpic barrier, but rather by a shift in the entropy of activation ((Formula presented.)), from negative values in the pure system (approximately (Formula presented.) J/ (Formula presented.)) to large positive values with initiators (approximately 77 J/ (Formula presented.) for DCP and 85 J/ (Formula presented.) for Luperox). The results suggest that curing in the presence of initiators proceeds through a dissociative transition state that is entropically more favorable, leading to a lower Gibbs free energy of activation ((Formula presented.)). These findings provide a basis for developing more efficient low-temperature curing strategies for advanced CMC processing
Identification of high-risk genes and classification of acute myocardial infarction patients utilizing deep learning in a restricted cohort
No full textBackground and motivation: Classifying diseases like heart problems using gene expression data depends on selecting important genes. Traditional machine learning (ML) often uses simple feature selection (FS) techniques, which can limit accuracy. In our research, we combine deep learning (DL) with gene-focused methods like differential expression analysis (DEA) to improve classification performance significantly. Method: We thoroughly and rigorously evaluated ML and DL classifiers using two gene expression datasets (GSE36961 and GSE57345). We tested four hypotheses using feature selection methods such as chi-square, DEA. We applied principal component analysis (PCA) to reduce the number of features. To ensure the reliability of our findings, we applied k-fold cross-validation, hyperparameter tuning, block effect analysis, and assessed data augmentation and generalization. Statistical tests, including paired t -test and Mann–Whitney U test, and Wilcoxon signed-rank test were performed to compare model performances rigorously. Results: Our experiments on two gene expression datasets (GSE36961, GSE57345) not only confirmed all four hypotheses (H1, H2, H3, and H4) but also revealed significant performance improvements. For H1, without FS, DL outperformed ML models by a substantial margin. For H2, with FS, DL outperformed ML models by a significant percentage. In H3, ML with FS improved over ML without FS by a considerable margin. For H4, DL with FS outperformed DL without FS by a noticeable percentage. Among FS methods, DEA consistently yielded the best results for both ML and DL, further underlining the significance of our findings. Conclusions: Combining DL with biological feature selection, especially DEA, improves gene expression classification and enables gene ranking and biomarker identification. This integrative approach balances modeling power with biological relevance, providing a reproducible framework for robust biomarker-based classification.Data available at [https://www.ncbi.nlm.nih.gov/geo/
Multifractal features of multimodal cardiac signals: Nonlinear dynamics of exercise recovery
No full textWe investigate the recovery dynamics of healthy cardiac activity after physical exertion using multimodal biosignals recorded with a polycardiograph. Multifractal features derived from the singularity spectrum capture the scale-invariant properties of cardiovascular regulation. Five supervised classification algorithms-Logistic Regression (LogReg), Support Vector Machine with radial basis function kernel, k-Nearest Neighbors, Decision Tree, and Random Forest-were evaluated to distinguish recovery states in a small, imbalanced dataset. Our results show that multifractal analysis, combined with multimodal sensing, yields reliable features for characterizing recovery and points toward nonlinear diagnostic methods for heart conditions.This is a peer-reviewed version of the article: Maluckov, A., Stojanović, D. B., Miletić, M., Ivanović, M. D., Hadžievski, L., & Petrović, J. (2026). Multifractal features of multimodal cardiac signals: Nonlinear dynamics of exercise recovery. Chaos: An Interdisciplinary Journal of Nonlinear Science, 36(1). [http://dx.doi.org/10.1063/5.0303657
Determination of transport characteristics of high-temperature THz quantum cascade lasers: numerical simulations and machine learning
No full textWe present our recent numerical study, in which the transport simulations are performed using the density matrix model presented in [1]. These results served as a basis for applying different machine learning models to determine the material gain, emission frequency, and current density of a two-well THz quantum cascade laser (QCL) designed for hightemperature operation [2]. In particular, we examine Random Forest (RF) [3], Extreme Gradient Boosting (XGBoost) [4], and Artificial Neural Network (ANN) [5]. RF reduces the variance through ensemble averaging, XGBoost improves predictive performance through gradient-boosted regularization, while ANN architectures employ neural representation learning to capture complex nonlinear relationships. The models were trained by varying the layer widths and external electric bias as the input variables, enabling the prediction of the desired outputs. Combining numerical simulations with machine learning models enables rapid and accurate prediction of key device characteristics, bridging detailed physics-based modelling with efficient data-driven approaches.19th Photonics Workshop, (International Conference), Kopaonik, March 08-12, 2026
Tailoring fly ash geopolymer ceramics: The influence of alkaline activation and thermal treatment on structure and phase transformation
No full textIn this study, fly ash, an industrial waste material, was used as a solid precursor for geopolymer synthesis. The sodium silicate to sodium hydroxide volume ratio was maintained at 1.6, with NaOH molarities of 2M, 4M, 6M, and 12M, and a liquid-to-solid ratio of 0.9. Thermal analysis demonstrated good stability of the geopolymer samples up to 900 °C, with major mass loss below 200 °C due to water evaporation and structural dehydroxylation between 200 and 650 °C. XRD analysis confirmed the presence of quartz, albite, and mullite in all samples, while increasing NaOH molarity led to the formation of faujasite. When these samples were heated, their internal structure became more organized and stable, leading to the formation of the mineral nepheline. This suggests that the thermal treatment was effective in promoting a more defined arrangement of atoms within the material, with this effect being particularly pronounced in samples produced using alkaline activators of higher molarity. FTIR spectroscopy identified vibrational bands corresponding Si–O/Al–O structures, with shifts in Si–O–T bands (T is Si, Al, Na) toward lower wavenumbers as alkalinity increased. Thermal exposure resulted in the disappearance of water-related signals and the emergence of carbonate band, confirming further phase transformation. These findings demonstrate the successful conversion of fly ash into geopolymer ceramics with tunable properties through controlled alkaline activation and heat treatment