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: 18Citation - Scopus: 18Ab Initio and Semiempirical Modeling of Excitons and Trions in Monolayer Tis3(American Physical Society, 2018) Torun, Engin; Şahin, Hasan; Chaves, A.; Wirtz, Ludger; Peeters, François M.We explore the electronic and the optical properties of monolayer TiS3, which shows in-plane anisotropy and is composed of a chain-like structure along one of the lattice directions. Together with its robust direct band gap, which changes very slightly with stacking order and with the thickness of the sample, the anisotropic physical properties of TiS3 make the material very attractive for various device applications. In this study, we present a detailed investigation on the effect of the crystal anisotropy on the excitons and the trions of the TiS3 monolayer. We use many-body perturbation theory to calculate the absorption spectrum of anisotropic TiS3 monolayer by solving the Bethe-Salpeter equation. In parallel, we implement and use a Wannier-Mott model for the excitons that takes into account the anisotropic effective masses and Coulomb screening, which are obtained from ab initio calculations. This model is then extended for the investigation of trion states of monolayer TiS3. Our calculations indicate that the absorption spectrum of monolayer TiS3 drastically depends on the polarization of the incoming light, which excites different excitons with distinct binding energies. In addition, the binding energies of positively and the negatively charged trions are observed to be distinct and they exhibit an anisotropic probability density distribution.Conference Object Citation - WoS: 6Citation - Scopus: 8Comparison of Four Ab Initio Microrna Prediction Tools(SciTePress, 2013) Saçar, Müşerref Duygu; Allmer, JensMicroRNAs are small RNA sequences of 18-24 nucleotides in length, which serve as templates to drive post transcriptional gene silencing. The canonical microRNA pathway starts with transcription from DNA and is followed by processing by the Microprocessor complex, yielding a hairpin structure. This is then exported into the cytosol where it is processed by Dicer and next incorporated into the RNA induced silencing complex. All of these biogenesis steps add to the overall specificity of miRNA production and effect. Unfortunately, experimental detection of miRNAs is cumbersome and therefore computational tools are necessary. Homology-based miRNA prediction tools are limited by fast miRNA evolution and by the fact that they are template driven. Ab initio miRNA prediction methods have been proposed but they have not been analyzed competitively so that their relative performance is largely unknown. Here we implement the features proposed in four miRNA ab initio studies and evaluate them on two data sets. Using the features described in Bentwich 2008 leads to the highest accuracy but still does not provide enough confidence into the results to warrant experimental validation of all predictions in a larger genome like the human genome. Copyright © 2013 SCITEPRESS - Science and Technology Publications.Article Citation - Scopus: 29Computational Methods for Ab Initio Detection of Micrornas(Frontiers Media S.A., 2012) Allmer, Jens; Yousef, MalikMicroRNAs are small RNA sequences of 18-24 nucleotides in length, which serve as templates to drive post-transcriptional gene silencing. The canonical microRNA pathway starts with transcription from DNA and is followed by processing via the microprocessor complex, yielding a hairpin structure. Which is then exported into the cytosol where it is processed by Dicer and then incorporated into the RNA-induced silencing complex. All of these biogenesis steps add to the overall specificity of miRNA production and effect. Unfortunately, their modes of action are just beginning to be elucidated and therefore computational prediction algorithms cannot model the process but are usually forced to employ machine learning approaches. This work focuses on ab initio prediction methods throughout; and therefore homology-based miRNA detection methods are not discussed. Current ab initio prediction algorithms, their ties to data mining, and their prediction accuracy are detailed.