Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Pvc/Pan-immobilized H2TiO3 Adsorbent: a Tailored Titanium-Based Lithium-Ion Sieve for High-Performance Lithium Recovery(Royal Soc Chemistry, 2025) Recepoglu, Yasar Kemal; Ipek, Onur; Yuksel, AsliThe increasing demand for lithium, driven by the rapid development of electric vehicles and energy storage systems, has created a pressing need for efficient and sustainable lithium recovery technologies. Conventional methods often face challenges related to selectivity, environmental impact, and scalability, necessitating the development of alternative materials. In this study, a polyvinyl chloride/polyacrylonitrile (PVC/PAN)-immobilized titanium-based lithium-ion sieve (HTO) was synthesized for lithium recovery from aqueous media, including geothermal brine. The objective was to obtain a selective, reusable, and mechanically stable adsorbent suitable for industrial-scale applications. The synthesized PVC/PAN-HTO composite was characterized by FT-IR, BET, XRD, and SEM techniques. Batch adsorption studies showed that the optimum lithium recovery occurred at pH 12, with efficiencies of 98.7% in model lithium solutions and 91.6% in geothermal water using a 4 g L-1 adsorbent dosage. Adsorption kinetics followed a pseudo-second-order model, and the Langmuir isotherm provided the best fit, indicating monolayer adsorption with a maximum capacity of 5.79 mg g-1. Thermodynamic analysis confirmed that the adsorption process is spontaneous and exothermic. Reusability tests demonstrated stable performance over three adsorption-desorption cycles, confirming the potential of PVC/PAN-HTO for practical lithium extraction applications.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.