Electrical conductivity improvement of (Fe + Al) co-doped ZnO nanoparticles for optoelectronic applications

Saadi, H.; Benzarti, Z.; Mourad, Sabrine; Sanguino, P.; Hadouch, Youness; Mezzane, D.; Abdelmoula, N.; Khemakhem, H.

doi:10.1007/s10854-022-07957-9

Cited by 3 publications

(1 citation statement)

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“…The ZnO structure can be doped with some elements such as Fe 4 , Cu 5 , 6 , Co 7 , Gd 8 , or Al 9 to improve its optical and electrical properties. In the past few years, many studies have been conducted to examine the effect of co-dopants such as (Cu-Mg) 10 , (Cd-Ni) 11 , (Al-In) 12 , (Fe-Al) 13 , (Al-Cu) 14 on the electro-optical properties of ZnO, and it was shown that the electro-optical properties of the ZnO nanomaterial are improved through co-doping.…”

Section: Introductionmentioning

confidence: 99%

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

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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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