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: 14Citation - Scopus: 15Predictive Modeling of Photocatalytic Hydrogen Production: Integrating Experimental Insights With Machine Learning on Fe/G-c3n4 Catalysts(Amer Chemical Soc, 2025) Arabaci, Bahriyenur; Bakir, Rezan; Orak, Ceren; Yuksel, AsliHydrogen emerges as a promising alternative to fossil fuels with its pollutant-free emissions, high energy density, versatility, and efficiency in generating power. In this study, photocatalytic hydrogen production from using 1000 ppm of model solution prepared with sucrose was investigated in the presence of Fe/g-C3N4 photocatalysts over Box-Behnken experimental design developed using the Minitab statistical software. The amount of hydrogen produced was optimized at different pH environments (3, 5, and 7) for 2 h reaction time with different amounts of metal loaded (10, 20, and 30 wt %), Fe/g-C3N4 (0.1, 0.2, and 0.3 g/L), and oxidant (H2O2; 0, 10, and 20 mM) concentrations. SEM, BET, XRD, FTIR, and PL analyses were employed for the characterization of synthesized photocatalysts. According to the response optimization, using Fe/g-C3N4, the optimal conditions for hydrogen production were found as 0.3 g/L catalyst loading, 18.8 mM H2O2, and 26.6% Fe loading by mass when the pH was 3 for the reaction medium. Furthermore, machine learning algorithms were employed to predict hydrogen evolution based on experimental parameters. Notably, ensemble models such as Voting Regressor combining the Bagging Regressor, Random Forest Regressor, LGBM Regressor, Extra Trees Regressor, XGB Regressor, and Gradient Boosting Regressor achieved superior performance with a mean squared error of 0.0068 and R-squared (R 2) of 0.9895. This integrated approach demonstrates the efficacy of machine learning in optimizing photocatalytic hydrogen generation processes.Article Citation - WoS: 2Citation - Scopus: 2Electrolytic Oxidation of 1,8-Diazabicyclo[5.4.0]undec in Hot-Compressed Water on a Titanium Electrode(American Chemical Society, 2020) Orak, Ceren; Yüksel Özşen, AslıThe nitrogen-containing heterocyclic organic compound, 1,8-diazabicyclo[5.4.0]undec-7-ene (DBU), was chosen to prepare a model solution to represent nitrogen-containing industrial waste streams. A hybrid reactor system was designed to combine electrolysis with wet oxidation in hot compressed water using a titanium electrode. The effects of current density, NaOH concentration, and reaction time on DBU and total organic carbon (TOC) removal were investigated via Minitab 18 software to clarify the main and interaction effects. Statistical analysis shows that the NaOH concentration and current density had significant effects on DBU removal. The highest DBU (91.2%) and TOC (45%) removal was observed at the lowest DBU concentration (3 mM) for 90 min of reaction time. Last, the effect of temperature on DBU and TOC removal was investigated. TOC removal was described with the first-order reaction kinetic model. Rate constants were determined as 0.0025, 0.041, and 0.050 min(-1) at 200, 240, and 280 degrees C, respectively. The activation energy was calculated as 79.86 kJ/mol.