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    Influence of Activated Carbon Concentration on Foam Material Properties: Design and Optimization
    (Springer Heidelberg, 2023) Ergun, Mehmet Emin; Ergun, Halime
    Activated carbon is widely used in adsorption, but there is limited research on its interaction with foam materials. In the first part of this study, activated carbon was produced from ash wood (Fraxinus excelsior) waste through phosphoric acid activation and characterized. The BET surface area of the activated carbon was found to be 623 m2/g. SEM and XRD analyses determined the physical surface morphology and crystallographic properties of activated carbon. In the second part of the study, xanthan gum-based foams were produced with the addition of activated carbon at four different ratios (0%, 2%, 4%, and 6%), and their suitability for insulation purposes was investigated. As the amount of activated carbon increased, the density and thermal conductivity of the foam materials increased while the porosity decreased. Furthermore, adding activated carbon up to 4% increased the compressive properties of the foam materials. On the other hand, a further activated carbon ratio increase to 6% led to aggregation within the foam material, decreasing the compressive strength. In the final part of the study, the quadratic linear analysis provided valuable insights into the relationships between activated carbon concentration and foam material properties. The statistical significance and prediction power of the analysis were rigorously evaluated, ensuring the reliability of the obtained results. The findings presented in this study have important implications for the design and optimization of foam materials. Understanding the influence of activated carbon concentration on foam properties enables researchers and engineers to tailor foam materials.
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    Investigating the feasibility of guar gum based foams for insulation applications using regression analysis
    (Federacion Asociaciones Ingenieros Industriales Espana, 2023) Ergun, Mehmet-Emin; Ergun, Halime
    Guar gum is commonly utilized in the pharmaceutical, cosmetic, and food industries. However, its use as a foam material for insulation purposes in construction fields has not been extensively studied, especially with regards to machine learning. This study aimed to investigate the potential use of foams produced from biopolymers for insulation and to estimate their properties using two different regression analyses. The foams were produced using a simple and quick procedure involving a mixture of guar gum, cellulose, and boric acid in different proportions, and then dried in the oven. The results of the produced foams showed promising features such as low density, low thermal conductivity, and good mechanical properties, which are highly desirable in insulation materials. A regression model was developed to analyze the effects of the components used in the foam formulation and to provide an estimated method for future research. The regression model was able to accurately predict the results, with an R squared value of up to 0.99, allowing for more quantitative data to be obtained with fewer experimental results. Furthermore, it was found that guar gum had the most significant effect on the properties of the foams.
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    Investigation of the usability of activated carbon as a filling material in nitrile butadiene rubber/natural rubber components and modeling by regression analysis
    (Sage Publications Ltd, 2024) Bulbul, Saban; Ergun, Halime
    Activated carbon is a versatile material with a wide range of applications due to its porous structure and large surface area. In this study, activated carbon was manufactured from cellulose using zinc chloride and phosphoric acid activation agents, and it was characterized using Brunauer-Emmett-Teller (BET), Field-Emission Scanning Electron Microscopes (FE-SEM), Energy Distribution Spectroscopy (EDS), mapping, and Fourier Transform Infrared Spectrophotometer (FTIR). Two different types of activated carbon utilized as a filler in Nitrile Butadiene Rubber (NBR)/Natural Rubber (NR) blends at different proportions (%0, %5, %10, %15 and 20%), and compared its properties to those of carbon black. The results showed that the addition of activated carbon improved the mechanical properties of the rubber blends, including hardness, tensile strength, and unit elongation. Furthermore, the experimental data obtained were used to examine the effects of carbon black, activated carbon salt, and activated carbon acid values on density, hardness, tensile strength, and percentage elongation variables using Multiple Linear Regressions (MLR). These models provided successful results in predicting the data with fewer experiments. The results have the potential to contribute to the promotion of the use of environmentally friendly materials in future research and to be an important step towards a sustainable industry.
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    Segmentation of Rays in Wood Microscopy Images Using the U-Net Model
    (North Carolina State Univ Dept Wood & Paper Sci, 2021) Ergun, Halime
    Rays are an important anatomical feature in tree species identification. They are found in certain proportions in trees, which vary for each tree. In this study, the U-Net model is adopted for the first time to detect wood rays. A dataset is created with images taken from the wood database. The resolution of microscopic wood images in tangential section is 640x400. The input image for training is divided into 32x32 image blocks. Each pixel in the dataset is labeled as belonging to the ray or the background. Then, the dataset is increased by applying scale, rotation, salt-and-pepper noise, circular mean filter, and gauss filter. The U-Net network created for ray segmentation is trained using the Adam optimization algorithm. The experimental results show that the ray segmentation accuracy in testing is 96.3%.
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    Segmentation of Rays in Wood Microscopy Images Using the U-Net Model
    (North Carolina State Univ Dept Wood & Paper Sci, 2021) Ergun, Halime
    Rays are an important anatomical feature in tree species identification. They are found in certain proportions in trees, which vary for each tree. In this study, the U-Net model is adopted for the first time to detect wood rays. A dataset is created with images taken from the wood database. The resolution of microscopic wood images in tangential section is 640x400. The input image for training is divided into 32x32 image blocks. Each pixel in the dataset is labeled as belonging to the ray or the background. Then, the dataset is increased by applying scale, rotation, salt-and-pepper noise, circular mean filter, and gauss filter. The U-Net network created for ray segmentation is trained using the Adam optimization algorithm. The experimental results show that the ray segmentation accuracy in testing is 96.3%.
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    Segmentation of wood cell in cross-section using deep convolutional neural networks
    (Ios Press, 2021) Ergun, Halime
    Fiber and vessel structures located in the cross-section are anatomical features that play an important role in identifying tree species. In order to determine the microscopic anatomical structure of these cell types, each cell must be accurately segmented. In this study, a segmentation method is proposed for wood cell images based on deep convolutional neural networks. The network, which was developed by combining two-stage CNN structures, was trained using the Adam optimization algorithm. For evaluation, the method was compared with SegNet and U-Net architectures, trained with the same dataset. The losses in these models trained were compared using IoU (Intersection over Union), accuracy, and BF-score measurements on the test data. The automatic identification of the cells in the wood images obtained using a microscope will provide a fast, inexpensive, and reliable tool for those working in this field.