Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Citation - WoS: 15Citation - Scopus: 16A Machine Learning Ensemble Approach for Predicting Solar-Sensitive Hybrid Photocatalysts on Hydrogen Evolution(IOP Publishing, 2024) Bakır, Rezan; Orak, Ceren; Yuksel, AsliHydrogen, as the lightest and most abundant element in the universe, has emerged as a pivotal player in the quest for sustainable energy solutions. Its remarkable properties, such as high energy density and zero emissions upon combustion, make it a promising candidate for addressing the pressing challenges of climate change and transitioning towards a clean and renewable energy future. In an effort to improve efficiency and reduce experimental costs, we adopted machine learning techniques in this study. Our focus turned to predictive analyses of hydrogen evolution values using three photocatalysts, namely, graphene-supported LaFeO3 (GLFO), graphene-supported LaRuO3 (GLRO), and graphene-supported BiFeO3 (GBFO), examining their correlation with varying levels of pH, catalyst amount, and H2O2 concentration. To achieve this, a diverse range of machine learning models are used, including Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), XGBoost, Gradient Boosting, and AdaBoost-each bringing its strengths to the predictive modeling arena. An important step involved combining the most effective models-Random Forests, Gradient Boosting, and XGBoost-into an ensemble model. This collaborative approach aimed to leverage their collective strengths and improve overall predictability. The ensemble model emerged as a powerful tool for understanding photocatalytic hydrogen evolution. Standard metrics were employed to assess the performance of our ensemble prediction model, encompassing R squared, Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE). The yielded results showcase exceptional accuracy, with R squared values of 96.9%, 99.3%, and 98% for GLFO, GBFO, and GLRO, respectively. Moreover, our model demonstrates minimal error rates across all metrics, underscoring its robust predictive capabilities and highlighting its efficacy in accurately forecasting the intricate relationships between GLFO, GBFO, and GLRO values and their influencing factors.Article Citation - WoS: 3Citation - Scopus: 4Photocatalytic Degradation of Aquatic Organic Pollutants With Zn- and Zr-Based Metalorganic Frameworks: Zif-8 and Uio-66(TÜBİTAK - Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, 2022) Çalık, Fatma Defne; Erdoğan, Bilgesu; Yılmaz, Esra; Saygı, Gizem; Çakıcıoğlu-Özkan, FehimeWater treatment has been an essential issue with the increasing population over 40 years. Researchers center on the major organic pollutants, such as dyes, pesticides, and pharmaceutical products. Photocatalytic degradation is one of the promising methods for aquatic organic pollutant treatment. Over the years, scientists have been working on developments for photocatalysts to enhance their pollutant degradation performances. From the reviewed studies, it is seen that properties like surface area, chemical, mechanical, and thermal stability, and uniform distribution of active sites are crucial, and an increase in these properties provides better degradation efficiency. In this sense, metal-organic frameworks as photocatalysts can be considered more advantageous. This study focuses on the organic aquatic pollutant degradation studies by using well-known MOFs like ZIF-8 and UiO-66 photocatalysts. Mainly the organic dye (RhB, MB, MO, etc.) degradation efficiencies of ZIF-8 and UiO-66 have been achieved to 100%. Recently, the degradation capacities of various pharmaceuticals such as diazinon, acetaminophen, levofloxacin, and sulfamethoxazole have also been investigated. According to the reviewed studies, ZIF-8 and UiO-66 can be considered remarkable photocatalysts for the degradation of organic pollutants.Article Citation - WoS: 14Citation - Scopus: 12Photocatalytic Hydrogen Energy Evolution From Sugar Beet Wastewater(Wiley-VCH Verlag, 2021) Orak, Ceren; Yüksel, AslıHydrogen is a clean, environmentally friendly, storable, and sustainable green energy source as well as a potential fuel. It could be produced from various biomass, wastewater, or other sources by different processes. In this study, hydrogen was evolved from sucrose model solution and real sugar beet wastewater by photocatalytic oxidation using a perovskite catalyst under solar light irradiation. In this context, firstly, the graphene supported LaFeO3 (GLFO) was synthesized and then, a characterization study shows that GLFO is successfully synthesized. To optimize the reaction parameters (pH, catalyst loading, and initial hydrogen peroxide concentration), an experimental matrix was created using the Box Behnken model. Whereas the highest hydrogen evolution from sucrose model solution was observed as 3520 μmol/gcat, the highest hydrogen evolution from sugar beet wastewater was obtained as 7035 μmol/gcat. The highest TOC removal (99.73 %) from sugar beet wastewater was also achieved at the same reaction conditions.Article Citation - WoS: 8Citation - Scopus: 12A Box–behnken Design (bbd) Optimization of the Photocatalytic Degradation of 2,4-Dichlorophenoxyacetic Acid (2,4-D) Using Tio2/H2o2(Desalination Publications, 2018) Doğdu Okçu, Gamze; Baldan Pakdil, Nazlı; Ökten, Hatice Eser; Yalçuk, Arda2,4-Dichlorophenoxyacetic acid (2,4-D), a chlorinated phenoxy-alkanoic herbicide, is used extensively in agriculture. This work investigates TiO2/H2O2 mediated UV photocatalytic degradation of 2,4-D in a laboratory-scale photoreactor. Three levels of Box–Behnken design technique, combined with response surface methodology (RSM), were used to design the experiments. Two kinds of multivariate experimental design (pH, TiO2, and 2,4-D concentration) and (pH, TiO2, and H2O2 concentrations) were employed to establish two quadratic models (Model 1 and Model 2), showing the functional relationship between degradation rate of 2,4-D and three independent experimental parameters. Model 1 predicted optimum values for pH, TiO2, and 2,4-D concentrations to be 5.7, 1.20 g L−1, and 32 mg L−1, respectively. Model 2 predicted optimum values for pH, TiO2, and initial H2O2 concentrations to be 4.94, 1.34 g L−1, and 161 mg L−1. Degradation rate of 2,4-D approached 78.10% for Model 1 and 83.63% for Model 2. For both models, similar results were obtained through optimizing variables by RSM and using single factorial batch reactor operation. Regression analysis showed good agreement between experimental results and predictive values for Models 1 and 2, with R2 values of 0.9958 and 0.9976, respectively.Article Citation - WoS: 31Citation - Scopus: 33Enhanced Bactericidal and Photocatalytic Activities of Zno Nanostructures by Changing the Cooling Route(Royal Society of Chemistry, 2018) Horzum, Nesrin; Hilal, Mohamed Elhousseini; Işık, TuğbaWe report on a simple synthesis of ZnO nanowires by calcination of zinc acetate. The effect of calcination temperature and cooling route on the antibacterial and photocatalytic properties is demonstrated by varying the size and surface area of the nanowires. The decrease of the calcination temperature followed by a rapid cooling procedure leads to a smaller size and larger surface area of the nanowires. ZnO nanowires are found to be effective against the growth of E. coli at the microgram level. In addition, the photocatalytic activity of the synthesized ZnO nanowires is demonstrated by the successful degradation of the organic dye methylene blue.