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: 4A Facile Method for Boosting the Graphitic Carbon Nitride's Photocatalytic Activity Based on 0d/2d S-Scheme Heterojunction Nanocomposite Architecture(Elsevier, 2024) Kahraman, Zeynep; Kartal, Uğur; Gent, Aziz; Alp, EmreGraphitic carbon nitride (g-C 3 N 4 ) has received significant interest as a metal -free photocatalyst. The S -scheme photocatalytic system has great potential to improve the charge separation in semiconductor photocatalysts. In this study, we have fabricated non-toxic and low-cost photocatalytic nanocomposites of 0D/2D S -scheme heterojunction composed of iron oxide and graphitic carbon nitride by a facile method. The developed facile method provides a sustainable way with a high atom economy to further enhance the photocatalytic performance of exfoliated g-C 3 N 4 . The 0D -iron oxide/2D-C 3 N 4 exhibited nearly 10 times better than bulk g-C 3 N 4 and almost 60 % better than exfoliated g-C 3 N 4 under simulated solar light irradiation. The experimental results demonstrated that the effective charge -carrier mechanism led to an improved generation of reactive oxygen species (ROSs), resulting in an impressive photocatalytic performance. A serial photocatalytic test was also conducted to understand photocatalytic reaction mechanisms with various scavengers.Article Citation - WoS: 6Citation - Scopus: 6Inverse Effects of Lanthanide Co-Doping on the Photocatalytic Hydrogen Production and Dye Degradation Activities of Cu Doped Sol-Gel Tio<sub>2</Sub>(Elsevier, 2023) Yurtsever, Husnu Arda; Erzin, Kubilay; Ciftcioglu, Muhsin; Yurtsever, Hüsnü Arda; Erzin, Kubilay; Çiftçioğlu, MuhsinCopper doped and lanthanide-copper co-doped titania powders were prepared by sol-gel technique and the effects of co-doping on the photocatalytic reduction and oxidation activities of titania were investigated in this work. Characterization studies indicated that a reduced structure was formed due to the presence of Ti3+ species in copper doped titania powder and a more stable structure was formed when lanthanides were used as co-dopants. Copper doped powder had a significantly higher activity in photocatalytic hydrogen production (1037 mu mol/g/h) than the co-doped powders (similar to 400 mu mol/g/h). The oxidation activities of co-doped powders however were determined to be about 2 times higher than that of the copper doped powder. The decrease in the reduction activity was attributed to the decrease in the number of Ti3+ sites, whereas the increase in oxidation activity was probably a result of the increase in the surface area and dye adsorption due to lanthanide co-doping.Article Citation - WoS: 11Citation - Scopus: 12Box-Behnken Design for Hydrogen Evolution From Sugar Industry Wastewater Using Solar-Driven Hybrid Catalysts(American Chemical Society, 2022) Orak, Ceren; Yüksel, AslıHydrogen is a clean and green fuel and can be produced from renewable sources via photocatalysis. Solar-driven hybrid catalysts were synthesized and characterized (scanning electron microscopy (SEM), transmission electron microscopy (TEM), Brunauer-Emmett-Teller (BET), X-ray diffraction (XRD), photoluminescence (PL) spectroscopy, and UV-vis diffuse reflectance spectroscopy (DSR)), and the results implied that graphene-supported LaRuO3is a more promising photocatalyst to produce hydrogen and was used to produce hydrogen from sugar industry wastewater. To investigate the main and interaction effects of reaction parameters (pH, catalyst amount, and [H2O2]0) on the evolved hydrogen amount, the Box-Behnken experimental design model was used. The highest hydrogen evolution obtained was 6773 μmol/gcatfrom sugar industry wastewater at pH 3, 0.15 g/L GLRO, and 15 mM H2O2. Based on the Pareto chart for the evolved hydrogen amount using GLRO, among the main effects, the only effective parameter was the catalyst amount for the photocatalytic hydrogen evolution from sugar industry wastewater. In addition, the squares of pH and two-way interaction of pH and [H2O2]0were also statistically efficient over the evolved hydrogen amount.