WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7150
Browse
12 results
Search Results
Now showing 1 - 10 of 12
Article Citation - WoS: 4Citation - Scopus: 4Unveiling the Bi-Functional Potential of Cowo4 Hybridized With Tubular G-C3n4 for Highly Photocatalytic Hydrogen Production, Water Purification and Supercapacitance Activities(Pergamon-elsevier Science Ltd, 2025) Erdem, Nurseli Gorener; Tuna, Ozlem; Inan, Ece; Caglar, Basar; Ertis, Irem Firtina; Simsek, Esra BilginIn this paper, CoWO4 nanospheres were successfully hybridized with graphitic carbon nitride with tubular morphology (TCN) and the photocatalytic antibiotic degradation, hydrogen evolution and supercapacitor performances were examined in detail. The morphological, structural and optical properties were characterized. The hybrid CoWO4/TCN-2 catalyst showed the highest tetracycline degradation efficiency with the rate constant of 0.0198 min(-1) which was 3.73 and 5.21 times greater than that of CoWO4 and TCN samples, respectively. The rate of photocatalytic hydrogen evolution was found to be 1997 mu mol/g.h for CoWO4/TCN-2 which was 2.03-fold and 1.09-fold higher than of TCN and CoWO4 samples, respectively. Electrochemical analysis revealed that the charge storage in CoWO4, TCN, and CoWO4/TCN electrodes was mainly governed by faradaic processes. All electrodes showed high-rate capability (>85 %), coulombic efficiency (>90 %) and cycle stability (>100 %). While CoWO4 and TCN electrodes show higher redox activities and specific capacitances than CoWO4/TCN composites, this limitation is mitigated by enhanced wettability in CoWO4/TCN electrodes. Notably, the CoWO4/TCN-2 electrode exhibits superior long-term capacitance, surpassing the TCN electrode after 2000 GCD cycles. This work offers new application areas for the metal tungstate/carbon nitride photocatalysts with improved photocatalytic and electrochemical performances.Article Citation - WoS: 16Citation - Scopus: 17Integrating Experimental and Machine Learning Approaches for Predictive Analysis of Photocatalytic Hydrogen Evolution Using Cu/G-c3n4(Pergamon-elsevier Science Ltd, 2024) Arabaci, Bahriyenur; Bakir, Rezan; Orak, Ceren; Yuksel, AsliThis study addresses environmental issues like global warming and wastewater generation by exploring waste-toenergy strategies that produce renewable hydrogen and treat wastewater simultaneously. Cu/g-C3N4 is used to evolve hydrogen from sucrose solution and the impact of reaction parameters such as pH (3, 5, and 7), Cu loading (5, 10, and 15 wt%), catalyst amount (0.1, 0.2, and 0.3 g/L), and oxidant (H2O2) concentration (0, 10, and 20 mM) on the evolved hydrogen amount is examined. Characterization study confirmed successful incorporation of Cu without significantly altering g-C3N4 properties. The highest hydrogen production (1979.25 mu mol g- 1 & sdot;h- 1) is achieved with 0.3 g/L catalyst, 20 mM H2O2, 5 % Cu loading, and pH 3. The experimental study concludes that Cu/g-C3N4 is an effective photocatalyst for renewable hydrogen production. In addition to the experimental investigations, various machine learning (ML) models, including Random Forest, Decision Tree, XGBoost, among others, are employed to analyze the impact of reaction parameters and forecast the quantities of produced hydrogen. Alongside these individual models, an ensemble approach is proposed and utilized. The R2 values of these ML models ranged from 0.9454 to 0.9955, indicating strong predictive performance across the board. Additionally, these models exhibited low error rates, further confirming their reliability in predicting hydrogen evolution.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: 52Citation - Scopus: 57Optimizing Hydrogen Evolution Prediction: a Unified Approach Using Random Forests, Lightgbm, and Bagging Regressor Ensemble Model(Elsevier Ltd, 2024) Bakır,R.; Orak,C.; Yüksel,A.Hydrogen, as a clean and versatile energy carrier, plays a pivotal role in addressing global energy challenges and transitioning towards sustainable energy systems. This study explores the convergence of machine learning (ML) for photocatalytic hydrogen evolution from sucrose solution using perovskite-type catalysts, namely LaFeO3 (LFO) and graphene-supported LaFeO3 (GLFO). This study pioneers the practical application of ML techniques, including Random Forests, LightGBM, and Bagging Regressor, to predict hydrogen yields in the presence of these photocatalysts. LFO and GLFO underwent a thorough characterization study to validate their successful preparation. Noteworthy, the highest hydrogen yield from the sucrose model solution was achieved using GLFO as 3.52 mmol/gcat. The optimum reaction conditions were experimentally found to be pH = 5.25, 0.15 g/L of catalyst amount, and 7.5 mM of HPC (hydrogen peroxide concentration). A pivotal contribution of this research lies in the practical application of ML models, culminating in the development of an ensemble model. This collaborative approach not only achieved an overall R2 of 0.92 but also demonstrated exceptional precision, as reflected in remarkably low error metrics. The mean squared logarithmic error (MSLE) was 0.0032, and the mean absolute error (MAE) was 0.049, underscoring the effectiveness of integrating diverse ML algorithms. This study advances both the understanding of photocatalytic hydrogen evolution and the practical implementation of ML in predicting intricate chemical reactions. © 2024 Hydrogen Energy Publications LLCArticle 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: 5Citation - Scopus: 6Formation of Monolithic Srtio3-Tio2 Ceramic Heterostructures by Reactive Hydrothermal Sintering(Elsevier, 2023) Karacasulu, Levent; Kartal, Uğur; İçin, Öykü; Bortolotti, Mauro; Biesuz, Mattia; Ahmetoğlu, Çekdar VakıfIn a one-pot approach, monolithic SrTiO3-TiO2 ceramic heterostructures were obtained using the reactive hydrothermal liquid phase densification (rHLPD). Structural, morphological, and photocatalytic properties of the obtained ceramics were analyzed. The relative density of the formed components reached about 80% with reaction time, temperature, and NaOH concentration variation. It was observed via Rietveld refinement that there was no XRD detectable phase other than TiO2 and SrTiO3 in the final structure. The monolithic SrTiO3-TiO2 ceramics obtained by hydrothermal reaction at 120 °C for 24 h in 1 M NaOH concentration showed a dielectric constant being around 500, and the dielectric loss was below 0.25 at frequencies higher than 10 kHz. The SrTiO3-TiO2 heterostructured monoliths having only 20 vol% total porosity and low specific surface area, demonstrated ∼60% efficiency (in 5 h) in degrading Methylene Blue photo-catalytically. © 2023 Elsevier LtdArticle Citation - WoS: 4Citation - Scopus: 5The Effect of Geometrical Characteristics of Tio2 Nanotube Arrays on the Photocatalytic Degradation of Organic Pollutants(Springer, 2023) Kartal, Uğur; Uzunbayır, Begüm; Doluel, Eyyüp Can; Yurddaşkal, Metin; Erol, MustafaHighly ordered TiO2 nanotube arrays (TNAs) were fabricated by electrochemical anodization under varying durations and voltages. The effects of the anodizing parameters on geometrical properties were investigated. The results showed that as the anodizing time increased from 15 to 45 min, the length of the nanotubes increased, but there was no change in their diameter, hence the surface area increased while the open porosity did not change. When the effect of the anodizing voltage was examined, it was observed that both the length and diameter increased as the voltage increased from 15 to 45 V. Thus, a significant increase in open porosity and surface area was observed. The UV-Vis spectrophotometer was used to evaluate the effects of all geometrical characteristics on the photodegradation of methylene blue (MB). The results showed that the anodizing parameters were highly effective on the photocatalytic degradation of MB. With the decrease of the anodizing voltage, the photocatalytic activity increased because of the geometrical characteristics of TNAs. Accordingly, TNAs with the surface area of 25 m(2)/g and the open porosity of 35% obtained by anodizing for 45 min at 15 V showed the highest photocatalytic activity with a degradation efficiency of similar to 81% in 7 h.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: 12Citation - Scopus: 12Dye Removal by Polymer Derived Ceramic Nanobeads(Elsevier, 2021) İçin, Öykü; Ahmetoğlu, Çekdar VakıfEmulsion processed polymer derived ceramic (PDC) nanobeads are used for Methylene Blue dye removal from aqueous solutions. The PDC nanobeads, produced at 600 degrees C and 1200 degrees C pyrolysis, are subsequently coated with titania (anatase). Titania-coated nanobeads show less than 35%, i.e., limited dye adsorption capability in dark. Instead, enhanced total removal efficiency (similar to 97%) is obtained when the initial adsorption is succeeded by photodegradation under UV. Direct reusability tests show that even after the third cycle, very high regeneration efficiencies being above 92% are observed for titania-coated nanobeads.