Yazar "Kandemirli, Fatma" seçeneğine göre listele

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    Öğe
    MRI kontrast maddelerinin gelişimi için kuantum kimyasal ve QSAR çalışmaları
    (2019) Saraçoğlu, Murat; Kandemirli, Sedat Giray; Sayıner, Hakan Sezgin; Başaran, Murat Alper; Kandemirli, Fatma
    Bileşiklerin stabilitesini belirlemek için poliamino-polikarboksilik ligandları çalıştık. Bileşikler, Gaussian Program kullanılarak B3LYP / 6-311G(d,p) ve B3LYP / 6-311 ++ G(d,p) teorisi ile hesaplanmıştır. Tüm istatistiksel analizler sırasıyla; Mat Lab 7.9 ve SPSS 20.0 adı verilen iki farklı yazılım kullanılarak gerçekleştirilmiştir. Gd (III) komplekslerinin kararlılık sabitlerinin tahmini için 37 poliaminopolikarboksilik ligandlarının için EHOMO, ELUMO, EHOMO ve ELUMO arasındaki enerji farkı (?E), iyonlaşma enerjisi (IE), mutlak elektronegatiflik (?), mutlak sertlik (?), yumuşaklık (?) gibi elektronik ve moleküler özellikler nötr moleküller için gaz fazı için DFT/B3LYP/6-311G(d,p) ve 6-311++ G(2d,2p) yöntemleri ile hesaplanmıştır.
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    Öğe
    The quantum chemical and QSAR studies on acinetobacter baumannii oxphos inhibitors
    (Bentham Science Publ Ltd, 2018) Sayıner, Hakan Sezgin; Abdalrahm, Afaf A. S.; Başaran, Murat A.; Kovalishyn, Vasyl; Kandemirli, Fatma
    Background: Acinetobacter is a Gram-negative, catalase-positive, oxidase-negative, non-motile, and no fermenting bacteria. Objective: In this study, some of the electronic and molecular properties, such as the highest occupied molecular orbital energy (E-HOMO), lowest unoccupied molecular orbital energy (ELUMO), the energy gap between E-HOMO and E-LUMO, Mulliken atomic charges, bond lengths, of molecules having impact on antibacterial activity against A. baumannii were studied. In addition, calculations of some QSAR descriptors such as global hardness, softness, electronegativity, chemical potential, global electrophilicity, nucleofugality, electrofugality were performed. Method: The descriptors having impact on antibacterial activity against A. baumannii have been investigated based on the usage of 29 compounds employing two statistical methods called Linear Regression and Artificial Neural Networks. Results: Artificial Neural Networks obtained accuracies in the range of 83-100% (for active/inactive classifications) and q(2)=0.63 for regression. Conclusion: Three ANN models were built using various types of descriptors with publicly available structurally diverse data set. QSAR methodologies used Artificial Neural Networks. The predictive ability of the models was tested with cross-validation procedure, giving a q(2)=0.62 for regression model and overall accuracy 70-95 % for classification models.
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    Öğe
    Theoretical studies on mild steel corrosion inhibition by 5-substituted 1H-tetrazoles in acidic media
    (Esg, 2019) Elusta, Mahmud İbrahim; Başaran, Murat Alper; Kandemirli, Fatma
    In this theoretical study, calculations for the three types of the tetrazole which are 2-(1H-Tetrazole-5-yl)-3-phenylacrylonitrile, 2-(1H-Tetrazole-5-yl)-3-(4-nitrophenylacrylonitrile), and 2-(1H-Tetrazole-5-yl)-3-(4-hydroxyphenyl acrylonitrile) showing the corrosion inhibition efficiency on mild steel in 1M HCl were carried out with the Density Functional Theory (DFT) at the B3LYP functionals with the use of 6-311g (d, p) basis set. Calculated parameters such as E-HOMO, E-LUMO, energy gap, electronegativity (chi), chemical potential (mu), hardness (eta), softness (S), electrophilicity, electrofugality, nucleofugality, Proton affinity, polarizability and hyperpolarizability. The correlation and regression analysis have been conducted to determine which descriptors have effect on inhibition efficiency. Both the theoretical results and experimental data are in accordance based on the inhibition efficiency.