Thermoluminescence glow curve analysis and kinetic parameters of Eu doped Li2MoO4 ceramic phosphors

Souadi, G.; Oglakci, M.; Kaynar, Ümit H.; Correcher, V.; Benavente, J.F.; Bulcar, K.; Ayvacıklı, M.; Hızıroğlu, Abdulkadir; Topaksu, M.; Can, N.; Karali, E. Ekdal

doi:10.1016/j.ceramint.2022.03.218

Cited by 12 publications

(1 citation statement)

References 34 publications

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“…Urea was put in the beaker and the mixture was stirred for 1 hour at 80 C using a magnetic stirrer. The top of the beaker was then removed and the mixture was stirred to evaporate excess water at the same temperature until a gel-like consistency was attained 36 . Following the evaporation of the water, a flame combustion reaction concluded the synthesis process.…”

Section: Methodsmentioning

confidence: 99%

Minimization of the threshold voltage parameter of the co-doped ZnO doped liquid crystals by machine learning algorithms

Önsal

Uğurlu

Kaynar

et al. 2023

Sci Rep

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This study aims to examine the influence of the co-doped semiconductor nanostructure (Al-Cu):ZnO on the electro-optical properties of the E7 coded pure nematic liquid crystal structures and minimize the threshold voltage of pure E7 liquid crystal. To determine the ideal concentration ratios of the materials for the minimum threshold voltage, we employed different machine learning algorithms. In this context, we first produced twelve composite structures through lab experimentation with different concentrations and created an experimental dataset for the machine learning algorithms. Next, the ideal concentration ratios were estimated using the AdaBoost algorithm, which has an $$R^2$$ R 2 of 96% on the experimental dataset. Finally, additional composite structures having the estimated concentration ratios were produced. The results show that, with the help of the employed machine learning algorithms, the threshold voltage of pure E7 liquid crystal was reduced by 19% via the (Al-Cu):ZnO doping.

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Section: Methodsmentioning

confidence: 99%