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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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    Öğe
    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; Arslanoğlu, 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.
  • [ X ]
    Öğe
    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.