Yazar "Aydogmus, Ercan" seçeneğine göre listele

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    Antibacterial efficacy of pyrolysis-derived plant fractions against resistant pathogens: a comparative evaluation using nutrient and Müller-Hinton agar
    (Wiley, 2026) Demirel, Maruf Hursit; Gul, Abdulkadir; Aydogmus, Ercan; Ozgen, Inanc; Arslanoglu, Hasan
    BACKGROUND This study investigates the antibacterial potential of pyrolysis-derived extracts from rosehip fruit (RF), orange peel (OP), corn silk (CS), spurge root (ER) and mullein leaf (ML) against antibiotic-resistant pathogens using two different culture media. Bioactive compounds were obtained via a PID-controlled pyrolysis system, and antibacterial activity was evaluated to clarify both extract efficacy and medium-dependent effects on bacterial growth and diffusion.RESULTS Antibacterial activities were assessed using the agar well diffusion method, with ampicillin as a positive control, against Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus and Enterococcus faecalis. A key novelty of this work is the comparative evaluation of extract performance on nutrient agar (NA) and M & uuml;ller-Hinton agar (MHA). Among all samples, the ML extract exhibited the strongest antibacterial activity across all tested strains, producing inhibition zones of 18.85 mm against E. coli and 17.15 mm against E. faecalis on NA, compared with 13.05 mm and 13.60 mm on MHA, respectively. CS and ER extracts showed moderate antibacterial effects, with consistently higher inhibition zones on NA than on MHA. Ampicillin generated substantially larger inhibition zones on NA (33.35 mm for E. coli and 34.45 mm for P. aeruginosa) compared with MHA (13.80 and 27.70 mm, respectively), confirming the strong influence of culture medium composition on measurable antibacterial activity.CONCLUSION These results indicate that both plant extracts and ampicillin exhibit higher antibacterial activity on NA than on MHA. The pronounced efficacy of the ML extract highlights pyrolysis-derived plant fractions as promising natural antimicrobials and emphasizes the critical importance of culture medium selection. (c) 2026 Society of Chemical Industry.
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    Comparative analysis of artificial neural networks and adaptive neuro-fuzzy inference system for biocomposite material synthesis and property prediction
    (Elsevier Science Sa, 2025) Aydin, Muhammet; Aydogmus, Ercan; Arslanoglu, Hasan
    Biocomposite materials (BMs) are becoming increasingly prevalent in modern applications. Estimating their production values involves various techniques, depending on the proportions of materials used. Among these techniques, artificial neural networks (ANN), fuzzy logic, statistical methods, and the adaptive neural fuzzy inference system are prominent. In this study, polyester biocomposites have been synthesized experimentally by adjusting the quantities of methyl ethyl ketone peroxide (MEKP), cobalt octoate (Co Oc) metal catalyst, marble factory waste, modified castor oil (MCO), and polyester raw material (UP) in specific ratios. The testing and analysis of these materials are conducted to determine parameters such as bulk density (BD), thermal conductivity coefficient (TCC), and activation energy (Ea). Subsequently, input and output values of the BMs are obtained, and ANN and adaptive neuro-fuzzy inference system (ANFIS) methods are employed for assessment. Both networks are trained and modeled using experimental data to construct their respective architectures. Validation of the models has been performed using data separate from the training set. A comparison between the actual values and those predicted by the network architectures revealed that the ANN method yielded outcomes with an average error of 0.3849 %, outperforming ANFIS. The findings showed that while ANFIS produced superior predictions for the Ea output value, the ANN structure fared better in predicting output values the BD and TCC.
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    Development of Polyurethane-Based Composites With Salt Clay and Industrial Wastes as Fillers: Corrosion, Mechanical Properties, and Machine Learning Insights
    (Wiley, 2025) Dag, Mustafa; Aydogmus, Ercan; Arslanoglu, Hasan; Yalcin, Zehra Gulten; Barlak, Semahat
    In this study, a polyurethane-based composite is developed by incorporating salt clay, ulexite, colemanite, and various other industrial waste materials. The effects of these fillers on the composite are evaluated and modeled using machine learning techniques. Among the tested models, random forest and neural network demonstrate the highest performance in predicting changes in compressive strength, hardness, and thermal conductivity. The dispersion of salt clay within the polyurethane matrix provides a 300%-500% increase in compressive strength and a 25%-40% improvement in hardness. Ulexite enhances compressive strength by 250%-350% and increases hardness by up to 30%, while colemanite contributes to a 400%-500% rise in compressive strength and a 35%-40% improvement in hardness. The addition of K & imath;rka clay waste and tincal further improves the composite's hardness and overall durability. Fly ash significantly increases compressive strength, although its effect on hardness is limited. The machine learning models effectively capture the relationship between input parameters and composite performance. The random forest model achieves a mean squared error (MSE) of 0.15 for compressive strength and 0.20 for hardness, while the neural network model yields the best results for thermal conductivity prediction with an MSE of 0.12. These findings highlight the potential of the developed composite for industrial applications, particularly in thermal insulation and low-load structural components. Future studies will focus on evaluating its performance under real-world conditions and further assessing its long-term durability.
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    Sustainable Epoxy Biocomposites Reinforced With Tenebrio molitor Biofiller: A Comprehensive Study on Thermal, Mechanical, and Dielectric Properties
    (Wiley, 2025) Ozgen, Inanc; Aydogmus, Ercan; Oner, Ilyas; Karagoz, Mustafa Hamdi; Arslanoglu, Hasan
    Obtaining biological material by drying and grinding Tenebrio molitor insects is original research in the field of innovative materials science. This study investigates the impact of T. molitor biofiller on the thermal, mechanical, and dielectric properties of epoxy-based biocomposites. The results revealed that increasing the content of the biofiller (from 0 to 4 wt.%) significantly reduced the bulk density (from 1134 to 1096 kg/m3), the Shore D hardness (from 77.6 to 73.1) and the thermal conductivity (from 0.112 to 0.090 W/mK), while enhancing the thermal insulation properties. A non-linear regression model confirmed the progressive reduction in density, with an optimal biofiller ratio of 2 wt.% minimizing trade-offs in thermal stability (activation energy: 178.37 kJ/mol). Dielectric constant measurements (4.09-3.78) showed improved insulating properties. Scanning electron microscopy (SEM) and other microscopic analyses confirmed homogeneous filler distribution and preserved structural integrity at optimal loadings. These findings highlight the potential of the biofiller-reinforced composites for use in lightweight, sustainable applications in the construction, electronics, and automotive industries, in line with the goal of innovating eco-friendly materials.
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    Valorization of Industrial Waste in Polymer Composites: Enhancing Mechanical and Thermal Properties for Insulation Applications Using Machine Learning Analysis
    (Wiley, 2026) Dag, Mustafa; Aydogmus, Ercan; Yalcin, Zehra Gulten; Arslanoglu, Hasan
    This study investigates the incorporation of industrial waste materials into polyurethane-based composites and evaluates their mechanical, thermal, and microstructural properties. The polyurethane matrix was synthesized from methylene diphenyl diisocyanate (MDI) and polyether polyol, into which various waste fillers, including ulexite, colemanite, tincal, and K & imath;rka clay, were introduced in different proportions. Mechanical testing revealed that specific wastes significantly enhance compressive strength, with ulexite- and clay-reinforced composites achieving improvements of 42.19% and 43.54%, respectively, compared to the pure polymer. The ulexite-clay composite exhibited the highest mechanical strength (38.67 kN), whereas tincal-containing samples demonstrated the weakest performance. Shore A hardness values generally decreased with waste incorporation, indicating that filler addition reduces polymer rigidity. Thermal conductivity results showed property variations within +/- 25%, where ulexite increased conductivity while K & imath;rka clay reduced it, thereby improving thermal insulation potential. Microstructural analysis using scanning electron microscopy (SEM) confirmed heterogeneous morphologies with dense filler distribution that intensified with increasing filler ratios. Fourier transform infrared spectroscopy (FTIR) indicated both physical and chemical interactions between the polymer matrix and boron-containing fillers, highlighting the complex interfacial bonding mechanisms. To complement the experimental analyses, machine learning (ML) models were applied to predict composite performance based on waste type and ratio. Among the tested algorithms, Random Forest (RF) demonstrated the highest predictive accuracy (R-2 > 0.90), confirming its suitability for modeling composite properties. The integration of ML provided quantitative insights into the role of individual and combined waste fillers, aligning closely with experimental observations. This research demonstrates that the controlled selection and optimization of waste fillers can enhance the performance of polyurethane composites, promote recycling of industrial byproducts, and support the development of sustainable materials for applications such as thermal insulation and structural components.