WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7150

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  • Article
    Citation - WoS: 1
    Citation - Scopus: 1
    Correlation of Low Field Nuclear Magnetic Resonance Relaxation With Composition and Glass Transition of Hard Candies
    (Frontiers Media S.A., 2024) Ozel, Baris; Berk, Berkay; Uguz, Sirvan Sultan; Grunin, Leonid; Oztop, Mecit Halil
    Hard candies produced from sucrose and doctoring agents such as glucose syrup (GS) and high fructose corn syrup (FS) have been investigated in terms of their final composition, glass transition temperature (Tg), degree of crystallinity, total soluble solids (TSS) content and water activity (aw). Time domain (TD) 1H NMR longitudinal relaxation time (T1) and second moment (M2) measurements have been used to understand the glassy state and crystallization characteristics for different hard candy formulations. The investigated candies include sucrose as the main sugar component. Different levels of doctoring agents have been mixed with sucrose to obtain products with different characteristics. It has been shown that addition of any doctoring agent to sucrose formulations decreases the Tg of the system significantly (p <= 0.05). Furthermore, GS or FS addition also induce significant changes in TSS and aw. T1 and M2 results are almost parallel to each other, both reaching the highest values for the highest sucrose concentration (p <= 0.05). The results demonstrate that the glass transition and crystallization characteristics of hard candy formulations can be monitored and analyzed by TD NMR relaxometry, alternative to other frequently used conventional methods including differential scanning calorimetry (DSC) and X-ray diffraction.
  • 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