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MATERIALS ENGINEERING

Modeling optical energy gap of thin film cuprous oxide semiconductor using swarm intelligent computational method

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Article: 2137936 | Received 27 Aug 2022, Accepted 14 Oct 2022, Published online: 01 Nov 2022

References

  • Abbas, A., & Abass, K. H. Materials today : Proceedings Nano-layers of Cu 2 O prepared by thermal evaporation technique : Morphology and optical properties. Mater. Today Proc. 2022.
  • Aboelkassem, Y., & Savic, D. (2021). Particle swarm optimizer for arterial blood flow models. Computer Methods and Programs in Biomedicine, 201, 105933. https://doi.org/10.1016/j.cmpb.2021.105933
  • Akande, K. O., Owolabi, T. O., Olatunji, S. O., & AbdulRaheem, A. (2017). A hybrid particle swarm optimization and support vector regression model for modelling permeability prediction of hydrocarbon reservoir. journal of Petroleum Science and Engineering, 150(October 2016), 43–18. https://doi.org/10.1016/j.petrol.2016.11.033
  • Akinpelu, A. A., Ali, M. E., Owolabi, T. O., Johan, M. R., Saidur, R., Olatunji, S. O., & Chowdbury, Z. (2020). A support vector regression model for the prediction of total polyaromatic hydrocarbons in soil: An artificial intelligent system for mapping environmental pollution. Neural Computing and Applications, 3. https://link.springer.com/article/10.1007/s00521-020-04845-3
  • Akomolafe, O., Owolabi, T. O., Rahman, M. A. A., Kechik, M. M. A., Yasin, M. N. M., & Souiyah, M. (2021). Modeling superconducting critical temperature of 122-iron-based pnictide intermetallic superconductor using a hybrid intelligent computational method. Materials (Basel), 14(16), 4604. https://doi.org/10.3390/ma14164604
  • Alp, E. (2021). The Facile Synthesis of Cu 2 O-Cu hybrid cubes as ef fi cient visible-light-driven photocatalysts for water remediation processes. Powder Technol, 394, 1111–1120. https://doi.org/10.1016/j.powtec.2021.09.031
  • Altindemir, G., & Gumus, C. (2020). Materials science in semiconductor processing Cu 2 O thin films prepared by using four different copper salts at a low temperature : An investigation of their physical properties. materials Science in Semiconductor Processing, 107((November 2019), 104805. https://doi.org/10.1016/j.mssp.2019.104805
  • Beheshti, Z. (2020). A time-varying mirrore d S-shape d transfer function for binary particle swarm optimization. Information Sciences, 512, 1503–1542. https://doi.org/10.1016/j.ins.2019.10.029
  • Chen, S., Pan, X., Xu, C., Huang, J., & Ye, Z. (2016). X-ray photoelectron spectroscopy study of energy-band alignments of ZnO on buffer layer Lu 2 O 3. Physics Letters, 380(7–8), 970–972. https://doi.org/10.1016/j.physleta.2015.12.038
  • Fan, G., Yu, M., Dong, S., Yeh, Y., & Hong, W. (2021). Forecasting short-term electricity load using hybrid support vector regression with grey catastrophe and random forest modeling. Utilities Policy, 73(September), 101294. https://doi.org/10.1016/j.jup.2021.101294
  • Gu, Y., Bao, Z., Lin, Y., Qin, Z., Lu, J., & Wang, H. (2017). Journal of natural gas science and engineering the porosity and permeability prediction methods for carbonate reservoirs with extremely limited logging data : Stepwise regression vs . N-way analysis of variance. Journal of Natural Gas science and Engineering, 42, 99–119. https://doi.org/10.1016/j.jngse.2017.03.010
  • Hadis Derikvandi, M. V. A. N.-E., Vosough, M., & Nezamzadeh-Ejhieh, A. (2020). A comprehensive study on the enhanced photocatlytic activity of a double-shell mesoporous plasmonic Cu@Cu2O/SiO2 as a visible-light driven nanophotocatalyst. Environmental Science and Pollution Research, 27(22), 27582–27597. https://doi.org/10.1007/s11356-020-08817-x
  • Henrique, B. M., Sobreiro, V. A., & Kimura, H. (2018). ScienceDirect stock price prediction using support vector regression on daily and up to the minute prices *. The Journal of Finance and Data science, 4(3), 183–201. https://doi.org/10.1016/j.jfds.2018.04.003
  • Hssi, A. A., Atourki, L., Labchir, N., Abouabassi, K., Ouafi, M., Mouhib, H., Ihlal, A., Elfanaoui, A., Benmokhtar, S., & Bouabid, K. Materials today : Proceedings structural and optical properties of electrodeposited Cu 2 O thin films. Mater. Today Proc. 2020, 22, 89–92.
  • Jafari, A., Tahani, K., Dastan, D., Asgary, S., Shi, Z., Yin, X., Zhou, W., Garmestani, H., & Ţă, Ş. (2020). Ion implantation of copper oxide thin fi lms ; statistical and experimental results. Surfaces and Interfaces, 18(January). https://www.sciencedirect.com/science/article/abs/pii/S2468023019306194
  • Kennedy, J., & Eberhart, R. Particle swarm optimization. IEEE International Conference on Particle swarm optimization, 4, 1942–1948. 1995. Perth, WA, Australia.
  • Li, K., Ma, Z., Robinson, D., Lin, W., & Li, Z. (2020). A data-driven strategy to forecast next-day electricity usage and peak electricity demand of a building portfolio using cluster analysis, Cubist regression models and particle swarm optimization. journal of Cleaner Production, 273, 123115. https://doi.org/10.1016/j.jclepro.2020.123115
  • Liu, T., Liu, Q., Hong, R., Tao, C., Wang, Q., Lin, H., Han, Z., & Zhang, D. (2021). Cuprous oxide induced the surface enhanced Raman scattering of silver thin films. Chemical Physics Letters, 783(September), 139071. https://doi.org/10.1016/j.cplett.2021.139071
  • Majumder, M., Biswas, I., Pujaru, S., & Chakraborty, A. K. (2015). Cuprous oxide thin fi lms grown by hydrothermal electrochemical deposition technique. Thin Solid Films, 589, 741–749. https://doi.org/10.1016/j.tsf.2015.07.002
  • Mallik, M., Monia, S., Gupta, M., Ghosh, A., Prakash, M., & Roy, H. (2020). Synthesis and characterization of Cu 2 O nanoparticles. Journal of Alloys and Compounds, 829, 154623. https://doi.org/10.1016/j.jallcom.2020.154623
  • Messaoudi, O., Ben, I., Gannouni, M., Souissi, A., Makhlouf, H., Bardaoui, A., & Chtourou, R. (2016). Applied Surface Science Structural, morphological and electrical characteristics of electrodeposited Cu 2 O : Effect of deposition time. applied Surface Science, 366, 383–388. https://doi.org/10.1016/j.apsusc.2016.01.035
  • Moharam, M. M., Elsayed, E. M., Nino, J. C., Abou-shahba, R. M., & Rashad, M. M. (2016). Potentiostatic deposition of Cu 2 O fi lms as p-type transparent conductors at room temperature. Thin Solid Films, 616, 760–766. https://doi.org/10.1016/j.tsf.2016.10.005
  • Murillo-escobar, J., Sepulveda-suescun, J. P., Correa, M. A., & Orrego-metaute, D. (2019). Urban Climate Forecasting concentrations of air pollutants using support vector regression improved with particle swarm optimization : Case study in aburrá valley, Colombia. Urban climate, 29(August 2018), 100473. https://doi.org/10.1016/j.uclim.2019.100473
  • Nair, M. T. S., Guerrero, L., Arenas, O. L., & Nair, P. K. (1999). Chemically deposited copper oxide thin films : Structural, optical and electrical characteristics. Applied Surface Science, 143–151. https://www.sciencedirect.com/science/article/abs/pii/S0169433299002391
  • Norouzi, A., & Nezamzadeh-ejhieh, A. (2020). α -Fe 2 O 3/Cu 2 O heterostructure : Brief characterization and kinetic aspect of degradation of methylene blue. Physica B: Condensed Matter, 599(August), 412422. https://doi.org/10.1016/j.physb.2020.412422
  • Olatunji, S. O., Owolabi, T. O., & Houssein, E. (2021). Barium titanate semiconductor band gap characterization through gravitationally optimized support vector regression and extreme learning machine computational methods. Mathematical Problems in Engineering, 2021, 1–12. https://doi.org/10.1155/2021/9978384
  • Omrani, N., & Nezamzadeh-ejhieh, A. (2020a). Focus on scavengers ’ effects and GC-MASS analysis of photodegradation intermediates of sulfasalazine by Cu 2 O/CdS nanocomposite. separation and Purification Technology, 235(October 2019), 116228. https://doi.org/10.1016/j.seppur.2019.116228
  • Omrani, N., & Nezamzadeh-ejhieh, A. (2020b). A novel quadripartite Cu2O-CdS-BiVO4-WO3 visible-light driven photocatalyst: Brief characterization and study the kinetic of the photodegradation and mineralization of sulfasalazine. Journal of Photochemistry and Photobiology A: Chemistry, 400(June), 112726. https://doi.org/10.1016/j.jphotochem.2020.112726
  • Omrani, N., & Nezamzadeh-ejhieh, A. (2020c). Photodegradation of sulfasalazine over Cu2O-BiVO4-WO3 nano-composite: Characterization and experimental design. International Journal of Hydrogen Energy, 5. https://www.sciencedirect.com/science/article/abs/pii/S036031992031747X
  • Omrani, N., & Nezamzadeh-ejhieh, A. (2020d). A ternary Cu 2 O/BiVO 4/WO 3 nano-composite : Scavenging agents and the mechanism pathways in the photodegradation of sulfasalazine. journal of Molecular Liquids, 315, 113701. https://doi.org/10.1016/j.molliq.2020.113701
  • Owolabi, T. O. (2019). Modeling the magnetocaloric effect of manganite using hybrid genetic and support vector regression algorithms. Physics Letters, 383(15), 1782–1790. https://doi.org/10.1016/j.physleta.2019.02.036
  • Owolabi, T. O., & Abd Rahman, M. A. (2021). Modeling the optical properties of a polyvinyl alcohol-based composite using a particle swarm optimized support vector regression algorithm. Polymers (Basel), 13(16), 1–17. https://doi.org/10.3390/polym13162697
  • Owolabi, T. O., Akande, K. O., & Olatunji, S. O. (2015). Development and validation of surface energies estimator (SEE) using computational intelligence technique. Computational Materials Science, 101. https://www.sciencedirect.com/science/article/abs/pii/S0927025615000270
  • Owolabi, T. O., Amiruddin, M., & Rahman, A. (2021b). Energy band gap modeling of doped bismuth ferrite multifunctional material using gravitational search algorithm optimized support vector regression. Crystals, 1–15. https://www.mdpi.com/2073-4352/11/3/246
  • Owolabi, T. O., & Gondal, M. A. (2015). Estimation of surface tension of methyl esters biodiesels using computational intelligence technique. Applied Soft computing, 37, 227–233. https://doi.org/10.1016/j.asoc.2015.08.028
  • Owolabi, T. O., Saleh, T. A., Olusayo, O., Souiyah, M., & Oyeneyin, O. E. (2021). Modeling the specific surface area of doped spinel ferrite nanomaterials using hybrid intelligent computational method. Journal of Nanomaterials, 2021. https://www.hindawi.com/journals/jnm/2021/9677423/
  • Ozaslan, D., Ozkendir, O. M., Gunes, M., Ufuktepe, Y., & Gumus, C. (2018). Optik Study of the electronic properties of Cu 2 O thin films by X-ray absorption spectroscopy. Optik, 157, 1325–1330. https://doi.org/10.1016/j.ijleo.2017.12.119
  • Prabu, R. D., Valanarasu, S., Ganesh, V., Shkir, M., Kathalingam, A., & Alfaify, S. S. (2018). Coatings technology E ff ect of spray pressure on optical, electrical and solar cell e ffi ciency of novel Cu 2 O thin fi lms. Surface and Coatings Technology, 347((September 2017), 164–172. https://www.sciencedirect.com/science/article/abs/pii/S0257897218304602
  • Pramanik, D., Roy, N., Kuar, A. S., Sarkar, S., & Mitra, S. (2022). Experimental investigation of sawing approach of low power fiber laser cutting of titanium alloy using particle swarm optimization technique. Optics & Laser technology, 147(August 2021), 107613. https://doi.org/10.1016/j.optlastec.2021.107613
  • Qu, F., Jiang, Q., Jin, G., Wei, Y., & Zhang, Z. (2020). Journal of petroleum science and engineering Mud pulse signal demodulation based on support vector machines and particle swarm optimization. journal of Petroleum Science and Engineering, 193(December 2019), 107432. https://doi.org/10.1016/j.petrol.2020.107432
  • Ravichandiran, C., Sakthivelu, A., Davidprabu, R., Arun, K. D., Valanarasu, S., Kathalingam, A., Ganesh, V., Shkir, M., & AlFaify, S. (2019). The e ff ect of rare earth Nd 3 + doping on physical characteristics of Cu 2 O thin fi lms derived by electrodeposition technique. Thin Solid Films, 683(May), 82–89. https://doi.org/10.1016/j.tsf.2019.05.008
  • Ravichandiran, C., Sakthivelu, A., Davidprabu, R., Valanarasu, S., Kathalingam, A., Ganesh, V., Shkir, M., Algarni, H., & AlFaify, S. (2019). Microelectronic engineering In-depth study on structural, optical, photoluminescence and electrical properties of electrodeposited Cu 2 O thin films for optoelectronics : An effect of solution pH. microelectronic Engineering, 210(March), 27–34. https://doi.org/10.1016/j.mee.2019.03.013
  • Raza, M., Tasmia, S., Jilani, A., Raza, K., Zajif, S., Bilal, M., Iqbal, J., Abdel-wahab, M. S., & Darwesh, R. (2021). Synthesis and characterization of a novel single-phase sputtered Cu 2 O thin films : Structural, antibacterial activity and photocatalytic degradation of methylene blue. Inorganic Chemistry Communications, 128(April), 108606. https://doi.org/10.1016/j.inoche.2021.108606
  • Reyes-Vallejo, O., Escorcia-garcía, J., & Sebastian, P. J. (2022). Materials science in semiconductor processing effect of complexing agent and deposition time on structural, morphological, optical and electrical properties of cuprous oxide thin films prepared by chemical bath deposition. materials Science in Semiconductor Processing, 138(September 2021), 106242. https://doi.org/10.1016/j.mssp.2021.106242
  • Sanjana, T., Sunil, M. A., Shaik, H., & Kumar, K. N. (2022). Studies on DC sputtered cuprous oxide thin films for solar cell absorber layers. Materials Chemistry and Physics, 281(February), 125922. https://doi.org/10.1016/j.matchemphys.2022.125922
  • Song, W., Cattani, C., & Chi, C. (2020). Multifractional Brownian motion and quantum-behaved particle swarm optimization for short term power load forecasting : An integrated approach. Energy, 194, 116847. https://doi.org/10.1016/j.energy.2019.116847
  • Srinivasan, S. S. G., Govardhanan, B., Ashok, M., & Kumar, M. C. S. (2021). Influence of deposition time on the visible-light-driven photocatalytic activity of Cu 2 O thin films by reactive sputtering at room temperature. materials Letters, 284, 128980. https://doi.org/10.1016/j.matlet.2020.128980
  • Tian, X., Wen, J., Chen, Z., Liu, X., Peng, H., Ji, C., Li, J., Peng, Y., & He, H. (2019). One-pot green hydrothermal synthesis and visible-light photocatalytic properties of Cu 2 O/Cu hybrid composites using egg albumin as structure modi fi er. Solid State sciences, 93(March), 70–78. https://doi.org/10.1016/j.solidstatesciences.2019.04.013
  • Vapnik, V. N. (1995). The nature of statistical learning theory (pp. 70–92). Springer-Verlag New York, Inc.
  • Wu, Y., Li, Y., Zhao, Y., Zhou, W., & Zhong, F. (2021). Preparation and photoelectric properties of F-doped cuprous oxide thin films. Optical Materials, 111(3), 110167. https://doi.org/10.1016/j.optmat.2020.110167
  • Xu, H., Dong, J., & Chen, C. (2014). One-step chemical bath deposition and photocatalytic activity of Cu 2 O thin fi lms with orientation and size controlled by a chelating agent. materials Chemistry and Physics, 143(2), 713–719. https://doi.org/10.1016/j.matchemphys.2013.10.004
  • Xue, Z., Zhang, Y., Cheng, C., & Ma, G. (2020). Neurocomputing remaining useful life prediction of lithium-ion batteries with adaptive unscented kalman filter and optimized support vector regression. Neurocomputing, 376, 95–102. https://www.sciencedirect.com/science/article/abs/pii/S0925231219313426
  • Zhang, Z., Ding, S., & Sun, Y. (2020). A support vector regression model hybridized with chaotic krill herd algorithm and empirical mode decomposition for regression task. Neurocomputing, 410, 185–201. https://doi.org/10.1016/j.neucom.2020.05.075
  • Zhang, L., Mcmillon, L., & Mcnatt, J. (2013). Solar energy materials & solar cells gas-dependent bandgap and electrical conductivity of Cu 2 O thin films. Solar Energy Materials and Solar Cells, 108, 230–234. https://doi.org/10.1016/j.solmat.2012.05.010