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- MakaleYalnız künye2026Scopus SJRQ1Influence on the thermoelectric characteristics of the growth parameters of BiSeTe thin films
Yüzüak, G. Durak; Yüzüak, E.
This study systematically investigates the influence of magnetron sputtering parameters on the structural, electronic, and thermoelectric properties of 100 nm thick Bi2SexTe3-x thin films deposited on Si(100). The initial parameters for applied film deposition are used to set the x value. The films are fabricated with sputtering powers ranging from 30 to 100 W and pressures from 3 to 15 mTorr, with films grown under optimal conditions selected for post-deposition annealing at 523 K. A comprehensive characterization suite, including X-ray diffraction (XRD), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), atomic force microscopy (AFM), and Kelvin probe force microscopy (KPFM), is used to establish direct correlations between processing, structure, and performance. XRD analysis reveals that increasing the sputtering pressure from 3 to 15 mTorr was critical for enhancing film crystallinity and promoting a strong (015) preferred orientation, while AFM showed a corresponding increase in average grain size from similar to 46 to similar to 60 nm via heat treatment (HT). EDS shows that films grown at 10 mTorr were almost certainly stoichiometric, but increasing the power to 15 mTorr caused tellurium deficiency. The films exhibited a power factor ranging from 2.5 to 2.8 mW m(- 1) K- 2 at 40 degrees C. HT improved the Seebeck coefficient from -115 mu V K- 1 to -140 mu V K- 1 and decreased the electrical resistivity by over 25 % through defect reduction and enhanced crystalline order. KPFM supported these findings, directly linking the crystallographic texture to the surface electronic structure and enhanced Seebeck effect.
AtıfWoS2OpenAlex2
İncele - MakaleYalnız künye2016Scopus SJRQ2Binuclear cyanido complexes containing [Pt(CN)4]2- building block: Synthesis, crystal structures, magnetic properties and anticancer activities
Korkmaz, Sengul Asian; Karadag, Ahmet; Aydin, Ali; Yerli, Yusuf; Soylu, Mustafa Serkan
Five new complexes of composition [Ni(N-bishydeten)Pt(CN)(4)] (1), [Ni(N-bishydeten)(2)][Pt(CN)(4)] (2), [Cu (N-bishydeten)Pt(CN)(4)] (3), [Cu(N-bishydeten)(2)][Pt(CN)(4)] (4) and [Cd(N-bishydeten)(2)][Pt(CN)(4)] (5) (N-bishydeten = N,N-bis(2-hydroxyethyl)-ethylenediamine) have been obtained and characterized by IR and EPR spectroscopies, elemental and thermal analysis and magnetic measurements. X-ray studies of 2 and 3 exhibited different structures and two different coordination modes of N-bishydeten ancillary ligand with a ratio of M:L. While N-bishydeten acts as a tetradentate with N', N', O and O' atoms in a molar ratio of 1:1 formed 2,2-CT-type zigzag chain for 3, it behaves tridentate ligand by ending up with six coordinated Ni-II center in a complex salt by using 1:2 M ratio for 2. None of the 1 and 3 complexes produced in this study showed sufficient anticancer activity but all did exhibit apoptotic properties and one of them also performed low cytotoxic activity against cell tested. (C) 2016 Elsevier B.V. All rights reserved.
AtıfWoS7OpenAlex8İncele - MakaleAçık Erişim2010Scopus SJRQ1Wavelet-based feature extraction and selection for classification of power system disturbances using support vector machines
Eristi, Hueseyin; Uçar, Ayseguel; Demir, Yakup
This paper presents a new approach for the classification of the power system disturbances using support vector machines (SVMs). The proposed approach is carried out at three serial stages. Firstly, the features to be form the SVM classifier are obtained by using the wavelet transform and a few different feature extraction techniques. Secondly, the features exposing the best classification accuracy of these features are selected by a feature selection technique called as sequential forward selection. Thirdly, the best appropriate input vector for SVM classifier is rummaged. The input vector is started with the first best feature and incrementally added the chosen features. After the addition of each feature, the performance of the SVM is evaluated. The kernel and penalty parameters of the SVM are determined by cross-validation. The parameter set that gives the smallest misclassification error is retained. Finally, both the noisy and noiseless signals are applied to the classifier given above stages. Experimental results indicate that the proposed classifier is robust and has more high classification accuracy with regard to the other approaches in the literature for this problem. (C) 2010 Elsevier B.V. All rights reserved.
AtıfWoS119OpenAlex163İncele - DergiINORGANICA CHIMICA ACTA