Microwave-Assisted Synthesis of Mushrooms Like MWCNT/SiO2@ZnO Nanocomposite: Influence on Nematic Liquid Crystal E7 and Highly Effective Photocatalytic Activity in Degradation of Methyl Blue

Eskalen, Hasan; Uruş, Serhan; Özğan, Şükrü

doi:10.1007/s10904-020-01804-2

Cited by 12 publications

(2 citation statements)

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“…Dopants such as as metal oxides have been found to enhance the electro-optical characteristics of LCs 19 . Metal oxides used as dopants are typically transition metal oxides such as titanium dioxide ( ) 20 , zinc oxide ( ZnO ) 21 , and barium titanate ( ) 22 . The doping ZnO nanoparticles (NPs) into LC change the molecular orientation and decrease the threshold voltage ( ), which leads lower power consumption 23 , 24 .…”

Section: Introductionmentioning

confidence: 99%

“…In the literature, co-doped ZnO and LC were examined in only one study. Eskalen et al investigated the effect of (Multi-Walled Carbon Nanotube-MWCNT; Silicon Dioxide-SiO ) MWCNT/SiO @ZnO nanocomposite on the thermal and electrical behavior of the E7 nematic liquid crystal 21 . The authors only examined the predetermined concentration ratios of nanocomposites and reported the electrical behavior of the E7 nematic liquid crystal.…”

Section: Introductionmentioning

confidence: 99%

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

Önsal

Uğurlu

Kaynar

et al. 2023

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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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