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: 52
    Citation - Scopus: 57
    Optimizing 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 LLC
  • Article
    Citation - WoS: 15
    Citation - Scopus: 15
    Granulation of Hydrometallurgically Synthesized Spinel Lithium Manganese Oxide Using Cross-Linked Chitosan for Lithium Adsorption From Water
    (Elsevier B.V., 2024) Recepoğlu,Y.K.; Arabacı,B.; Kahvecioğlu,A.; Yüksel,A.
    A drastic increase in demand for electric vehicles and energy storage systems increases lithium (Li) need as a critical metal for the 21st century. Lithium manganese oxides stand out among inorganic adsorbents because of their high capacity, chemical stability, selectivity, and affordability for lithium recovery from aqueous media. This study investigates using hydrometallurgically synthesized lithium manganese oxide (Li1.6Mn1.6O4) in granular form coated with cross-linked chitosan for lithium recovery from water. Characterization methods such as SEM, FTIR, XRD, and BET reveal the successful synthesis of the composite adsorbent. Granular cross-linked chitosan-coated and delithiated lithium manganese oxide (CTS/HMO) adsorbent demonstrated optimal removal efficiency of 86 % at pH 12 with 4 g/L of adsorbent dosage. The Langmuir isotherm at 25 °C, which showed monolayer adsorption with a maximum capacity of 4.94 mg/g, a better fit for the adsorption behavior of CTS/HMO. Adsorption was endothermic and thermodynamically spontaneous. Lithium adsorption followed the pseudo-first-order kinetic model. © 2024