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    Box-Behnken experimental design for optimization of chitosan foam materials reinforced with cellulose and zeolite
    (Wiley, 2024) Kurt, Rifat; Ergun, Halime; Ergun, Mehmet Emin; Istek, Abdullah
    Foam materials produced from biopolymers stand out as a more environmentally friendly insulation material solution. This study presents a comprehensive investigation into the development and optimization of chitosan-based foam materials using a Box-Behnken design. The foams were engineered using varying proportions of chitosan (0.5-3%), cellulose (0.5-3%), and zeolite (0.5-3%), targeting their application as thermal insulators. The physical and thermal properties of the foams that were produced were affected by the type and ratios of components, with density and thermal conductivity ranging from 0.0853 to 0.1915 g cm-3 and 0.0324 to 0.0921 W mK-1, respectively. Higher chitosan content improved insulation properties and mechanical strength whereas zeolite increments increased density and thermal conductivity. Using statistical analysis through the Box-Behnken design, we optimized the foam formulations, achieving minimum thermal conductivity and maximum compression strength at an averaged density, suggesting a strong potential for environmental sustainability applications. The recommended optimal chitosan:cellulose:zeolite composition ratio of 3:3:0.88 provides a valuable insight for tailored foam material formulation. This study shows the relationships between the composition of a composite material and its resultant properties, optimizing its preparation for industrial applicability in an environmentally conscious way within the context of insulation and construction. This investigation contributes to the field of material science by highlighting the versatility and potential of biopolymers but also aligns with the increasing need for green building materials.
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    Influence of Activated Carbon Concentration on Foam Material Properties: Design and Optimization
    (Springer Heidelberg, 2024) 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
    (Uk Zhende Publishing Ltd, 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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    Modeling Xanthan Gum Foam's Material Properties Using Machine Learning Methods
    (Mdpi, 2024) Ergun, Halime; Ergun, Mehmet Emin
    Xanthan gum is commonly used in the pharmaceutical, cosmetic, and food industries. However, there have been no studies on utilizing this natural biopolymer as a foam material in the insulation and packaging sectors, which are large markets, or modeling it using an artificial neural network. In this study, foam material production was carried out in an oven using different ratios of cellulose fiber and xanthan gum in a 5% citric acid medium. As a result of the physical and mechanical experiments conducted, it was determined that xanthan gum had a greater impact on the properties of the foam material than cellulose. The densities of the produced foam materials ranged from 49.42 kg/m3 to 172.2 kg/m3. In addition, the compressive and flexural moduli were found to vary between 235.25 KPa and 1257.52 KPa and between 1939.76 KPa and 12,736.39 KPa, respectively. Five machine-learning-based methods (multiple linear regression, support vector machines, artificial neural networks, least squares methods, and generalized regression neural networks) were utilized to analyze the effects of the components used in the foam formulation. These models yielded accurate results without time, material, or cost losses, making the process more efficient. The models predicted the best results for density, compression modulus, and flexural modulus achieved in the experimental tests. The generalized regression neural network model yielded impressive results, with R2 values above 0.97, enabling the acquisition of more quantitative data with fewer experimental results.