Estimation of Dielectric Constant of Ni(II)Pc and CdSeS/ZnS QDs Dope Liquid Crystal Structures by Machine Learning Algorithms

Year 2023, Volume: 11 Issue: 1, 513 - 523, 31.01.2023

Mustafa Aksoy Gülnur Önsal Onur Uğurlu

https://doi.org/10.29130/dubited.1091499

Abstract

In this study, the dielectric properties of Ni(II)Pc (nickel(II)phthalocyanine) and CdSeS/ZnS (cadmium selenide sulfide/zinc sulfide) quantum dot-doped 5CB (4-pentyl-4′-cyanobiphenyl) nematic liquid crystal composite structures along with the prediction of these properties by machine learning algorithms are reported. In order to determine the effects of the concentration ratio on the dielectric properties, 2% and 5% wt/wt Ni(II)Pc and CdSeS/ZnS quantum dots were doped into the 5CB nematic liquid crystal. The dielectric measurements of the samples were carried out using the dielectric spectroscopy method. Moreover, k-Nearest Neighbor, Decision Tree and Random Forest algorithms were used for the estimation of the real (ε^') and imaginary components (ε^'') of the dielectric constant, and the prediction performances of the algorithms were examined comparatively. According to the results obtained, the best estimation performance of the dielectric constant was obtained with the Random Forest algorithm.  

Keywords

Nematic liquid crystal; Dielectric properties, Machine learning

Ni(II)Pc ve CdSeS/ZnS Kuantum Nokta Katkılı Sıvı Kristal Yapıların Dielektrik Sabitinin Makine Öğrenmesi Algoritmaları ile Tahminlenmesi

Abstract

Bu çalışmada, Ni(II)Pc (nikel(II)ftalosiyanin) ve CdSeS/ZnS (cadmium selenide sulfide/zinc sulfide) kuantum nokta katkılı 5CB (4-pentyl-4′-cyanobiphenyl) nematik sıvı kristal kompozit yapıların dielektrik özellikleri ile birlikte bu özelliklerin makine öğrenmesi algoritmaları ile tahminlenmesi rapor edilmektedir. Konsantrasyon oranının dielektrik özelliklere etkilerini saptamak için 5CB nematik sıvı kristal yapıya ağırlıkça %2 ve %5 oranında Ni(II)Pc ve CdSeS/ZnS kuantum nokta katkılanmıştır. Numunelerin dielektrik ölçümleri, dielektrik spektroskopi yöntemi kullanılarak gerçekleştirilmiştir. Ayrıca, dielektrik sabitinin reel (ε^') ve sanal bileşenlerinin (ε^'') tahmini için k-En Yakın Komşu, Karar Ağacı, Rastgele Orman algoritmaları kullanmış ve algoritmaların tahmin performansları karşılaştırmalı olarak incelenmiştir. Algoritmalarda girdi parametreleri frekans, voltaj ve katkı oranı; çıktı parametreleri ise, dielektrik sabitinin reel (ε^') ve sanal bileşenleri (ε^'') olarak belirlenmiştir. Elde edilen sonuçlara göre dielektrik sabitinin en iyi tahmin performansına Rastgele Orman algoritması ile ulaşılmıştır. 

Keywords

Nematik sıvı kristal, Dielektrik özellikler, Makine öğrenmesi, Nematic liquid crystal; Dielectric properties, Machine learning

References

• [1]S. Kasap, P. Capper, F. Pascal, and M. J. Deen, Springer Handbook of Electronic and Photonic Materials, Springer-Verlag. Boston, 2017.
• [2] P. Malik, A. Chaudhary, R. Mehra, and K. K. Raina, “Electrooptic and dielectric studies in cadmium sulphide nanorods/ferroelectric liquid crystal mixtures,” Advances in Condensed Matter Physics, vol. 2012, p. 853160, 2012.
• [3]Y. Huang, E.-L. Hsiang, M.-Y. Deng, and S.-T. Wu, “Mini-LED, Micro-LED and OLED displays: Present status and future perspectives,” Light: Science & Applications, vol. 9, no. 1, pp. 1–16, 2020.
• [4]H.-W. Chen, J.-H. Lee, B.-Y. Lin, S. Chen, and S.-T. Wu, “Liquid crystal display and organic light-emitting diode display: present status and future perspectives,” Light: Science & Applications, vol. 7, no. 3, pp. 17168–17168, 2018.
• [5]C. Cirtoaje, E. Petrescu, C. Stan, and A. Rogachev, “Electric Freedericksz transition in nematic liquid crystals with graphene quantum dot mixture,” Applied Surface Science, vol. 487, pp. 1301–1306, 2019.
• [6]A.N. Gowda, M. Kumar, A.R. Thomas, R. Philip, S. Kumar, “Self-Assembly of Silver and Gold Nanoparticles in a Metal-Free Phthalocyanine Liquid Crystalline Matrix: Structural, Thermal, Electrical and Nonlinear Optical Characterization,” Chem. Sel. Vol. 1, pp. 1361–1370, 2016.
• [7]M. Pande, P. K. Tripathi, S. K. Gupta, R. Manohar, and S. Singh, “Enhancement of birefringence of liquid crystals with dispersion of poly (n-butyl methacrylate)(PBMA),” Liquid Crystals, vol. 42, no. 10, pp. 1465–1471, 2015.
• [8]R. K. Shukla, A. Chaudhary, A. Bubnov, and K. K. Raina, “Multi-walled carbon nanotubes-ferroelectric liquid crystal nanocomposites: effect of cell thickness and dopant concentration on electro-optic and dielectric behaviour,” Liquid Crystals, vol. 45, no. 11, pp. 1672–1681, 2018.
• [9]D. Bonegardt, D. Klyamer, B. Köksoy, M. Durmuş, and T. Basova, “Hybrid materials of carbon nanotubes with fluoroalkyl-and alkyl-substituted zinc phthalocyanines,” Journal of Materials Science: Materials in Electronics, vol. 31, pp. 11021–11028, 2020.
• [10] S. Moradian, H. Dezhampanah, J. B. Ghasemi, and H. Behnejad, “Spectrophotometric-chemometrics study of the effect of solvent composition and temperature on the spectral shape and shift of copper and nickel phthalocyanines in different aqueous-nonaqueous mixed solvents,” Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, vol. 227, p. 117621, 2020.
• [11]K. Sakamoto and E. Ohno-Okumura, “Syntheses and functional properties of phthalocyanines,” Materials, vol. 2, no. 3, pp. 1127–1179, 2009.
• [12]Ö. Bekaroğlu, Y. Bian, G. Bottari, X. Cai, G de la Torre, U. Hahn, N. Ishikawa, J. Jiang, N. Kobayashi, X. Li, Y. Liu, J-Y. Liu, P-C. Lo, Q. Luo, D.K.P. Ng, T. Nyokong, H. Tian, T. Torres, H. Wang, H. Wu, S. Yoshimoto and Y. Zhang, Functional phthalocyanine molecular materials, vol. 135, Heidelberg, Germany: Springer Science & Business Media, 2010, pp. 9.
• [13]S. Moradian, H. Dezhampanah, J. B. Ghasemi, and H. Behnejad, “Spectrophotometric-chemometrics study of the effect of solvent composition and temperature on the spectral shape and shift of copper and nickel phthalocyanines in different aqueous-nonaqueous mixed solvents,” Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, vol. 227, p. 117621, 2020.
• [14]F. Ghani, J. Kristen, and H. Riegler, “Solubility properties of unsubstituted metal phthalocyanines in different types of solvents,” Journal of Chemical & Engineering Data, vol. 57, no. 2, pp. 439–449, 2012.
• [15]E. Güzel, B. S. Arslan, G. Y. Atmaca, M. Nebioğlu, and A. Erdoğmuş, “High Photosensitized Singlet Oxygen Generating Zinc and Chloroindium Phthalocyanines Bearing (4-isopropylbenzyl) oxy Groups as Potential Agents for Photophysicochemical Applications,” ChemistrySelect, vol. 4, no. 2, pp. 515–520, 2019.
• [16]A. Rastogi, G. Pathak, A. Srivastava, J. Herman, and R. Manohar, “Cd1- X ZnXS/ZnS core/shell quantum dots in nematic liquid crystals to improve material parameter for better performance of liquid crystal based devices,” Journal of Molecular Liquids, vol. 255, pp. 93–101, 2018.
• [17]Y. Umeda, H. Hayashi, H. Moriwake, and I. Tanaka, “Prediction of dielectric constants using a combination of first principles calculations and machine learning,” Japanese Journal of Applied Physics, vol. 58, no. SL, p. SLLC01, 2019.
• [18]G. Pilania, C. Wang, X. Jiang, S. Rajasekaran, and R. Ramprasad, “Accelerating materials property predictions using machine learning,” Scientific reports, vol. 3, no. 1, pp. 1–6, 2013.
• [19]Ö. Eyecioglu, M. Kılıç, ve Z. G. Özdemir, “Polipropilen/Polianilin Kompozit Filmlerin Dielektrik Özelliklerinin Yapay Sinir Ağları Modeli İle Tahmini,” Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, c. 6, s. 4, ss. 787-802, 2018.
• [20]J. Wei et al., “Machine learning in materials science,” InfoMat, vol. 1, no. 3, pp. 338–358, 2019.
• [21]A. Mannodi-Kanakkithodi, G. Pilania, and R. Ramprasad, “Critical assessment of regression-based machine learning methods for polymer dielectrics,” Computational Materials Science, vol. 125, pp. 123–135, 2016.
• [22]A. Mannodi-Kanakkithodi, G. Pilania, T. D. Huan, T. Lookman, and R. Ramprasad, “Machine learning strategy for accelerated design of polymer dielectrics,” Scientific reports, vol. 6, no. 1, pp. 1–10, 2016.
• [23]M. Kılıç, Ö. Eyecioğlu, Z. Özdemir, and Ü. Alkan, “Estimation of dielectric parameters of LDPE/PANI composite films depending on temperature and PANI additive concentration by GRNN,” Journal of the Faculty of Engineering and Architecture of Gazi University, vol. 35, no. 2, pp. 1077–1088, 2020.
• [24]Ö. Eyecioğlu, “Bazalt/PANI Kompozitlerinin Dielektrik Özelliklerinin Tahmini için Makine Öğrenmesi Modellerinin Karşılaştırılması,” Avrupa Bilim ve Teknoloji Dergisi, s 23, ss. 817-826, 2021.
• [25] J. Mendes-Moreira, C. Soares, A. M. Jorge, and J. F. D. Sousa, “Ensemble approaches for regression: A survey,” Acm computing surveys (csur), vol. 45, no. 1, pp. 1–40, 2012.
• [26]L. E. Peterson, “K-nearest neighbor,” Scholarpedia, vol. 4, no. 2, pp. 1883, 2009.
• [27]E. Alpaydin, Introduction to machine learning. MIT press, 2020.
• [28]Y. L. Pavlov, Random forests. De Gruyter, 2019.