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    Solvent effects on free-radical copolymerization of styrene and 2-hydroxyethyl methacrylate: a DFT study
    (Royal Soc Chemistry, 2014) Ozaltin, T. Furuncuoglu; Dereli, B.; Karahan, O.; Salman, S.; Aviyente, V.
    The free-radical homopolymerization and copolymerization kinetics of styrene (ST) and 2-hydroxyethyl methacrylate (HEMA) in three different media (bulk, DMF, toluene) have been investigated by means of Density Functional Theory (DFT) calculations in combination with the Polarizable Continuum Model (PCM) and the Conductor-like Screening Model for Real Solvents (COSMO-RS). The conventional Transition State Theory (TST) is applied to calculate the rate parameters of polymerization. Calculated propagation rate constants are used to predict the monomer reactivity ratios, which are then used in the evaluation of the copolymer composition following the Mayo-Lewis equation. it is found that copolymerization reactions in bulk and toluene show similar transition geometries;, whereas, DMF has a tendency to form H-bonding interactions with the polar HEMA molecules, thus decreasing the reactivity of this monomer during homopolymerization and towards ST during copolymerization. Calculations of copolymer composition further show that the amount of HEMA monomer in the ST-HEMA copolymer system decreases in the polar DMF solution. The calculated spin densities of the radical species are in agreement with the rate parameters and confirm that the copolymerization propagation rate of the ST-HEMA system is in the order: k(p)(bulk) approximate to k(p)(toluene) > k(p)(DMF).
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    Towards a Multilingual Platform for Gamified Morphology Learning
    (Institute of Electrical and Electronics Engineers Inc., 2022) Bektas, F.; Dereli, B.; Hayta, F.; Sahin, E.; Ali, U.; Eryigit, G.
    Mobile-assisted language learning is an emerging trend in language education. Recently, the use of gamification for complex morphology learning has been perceived positively by morphologically rich language learners (namely, Turkish as a foreign language learners). Generating automatic explicit grammar exercises with the use of finite-state transducers (FST) in a gamified environment is a new and practical approach in this field. The integration of such platforms into new languages should be easily implementable in order to reduce the development efforts. This study is a first attempt towards this goal. The paper introduces an architecture for a multilingual platform supporting gamified morphology learning. As a test case, the French language module is developed in addition to Turkish. Experiences with these languages are explained and the stages for adding additional languages are described. The study reveals that morphological complexity is an important issue to consider, and the morphological differences of the target language should be considered while designing a mobile language learning application. © 2022 IEEE.