Işık, EşmeTaşyürek, Lütfi BilalIşık, İbrahimKılınç, Necmettin2022-07-202022-07-202022Isik, E., Tasyurek, L. B., Isik, I., & Kilinc, N. (2022). Synthesis and analysis of TiO2 nanotubes by electrochemical anodization and machine learning method for hydrogen sensors. Microelectronic Engineering, 111834.0167-9317https://doi.org/10.1016/j.mee.2022.111834https://hdl.handle.net/20.500.12899/1163Received 4 April 2022, Revised 10 June 2022, Accepted 18 June 2022, Available online 25 June 2022, Version of Record 28 June 2022.Eşme Işık, Department of Optician, Malatya Turgut Özal University, 44210 Malatya, Turkey. Lütfi Bilal Taşyürek, Department of Optician, Malatya Turgut Özal University, 44210 Malatya, Turkey. İbrahim Işık, Department of Electrical Electronics Engineering, Faculty of Engineering, Inonu University, 44280 Malatya, Turkey Necmettin Kılınç, Department of Physics, Faculty of Science & Arts, Inonu University, 44280 Malatya, Turkey.The conductometric hydrogen gas sensors were used to explore TiO2 nanotubes in this study. TiO2 nanotubes are synthesized by anodization of the titanium foils using a neutral 0.5% and 1% (wt) NH4F in glycerol solution depending on anodization time and anodization voltage at the temperature of 20 °C. The amorphous, rutile and anatase phases of TiO2 are observed for as-prepared TiO2 nanotubes, annealed at 700 and 300 °C, respectively. The diameters of the nanotubes grow as the anodization time and voltage increase, according to scanning electron microscopy (SEM) images. The inner diameter of nanotubes is changed between ~70 nm to ~225 nm. Hydrogen sensing properties of Ti/TiO2 nanotubes/Pd device has been tested at room temperature under concertation range from 0.5% to 10% depending on the crystalline phase. The highest sensor response is observed for anatase crystalline TiO2 nanotubes. Typical Schottky-type behavior is observed from the I-V measurement. All the fabricated nanotube diameters are also simulated by using Support Vector Machine and Artificial Neural Network models. And also, some of the nanotube diameters which are not obtained experimentally (anodization voltage of 70 V) are estimated using the Support Vector Machine and Artificial Neural Network models. In addition, an analytical model is also proposed using Jacobi numeric analysis method alternative to the simulation model for the nanotube diameter. Finally, the analytical, simulation, and experimental results are compared, and the best result is obtained using the 1 Hidden Layer Artificial Neural Network model.eninfo:eu-repo/semantics/closedAccessTitanium dioxideNanotubesHydrogen sensorAnodizationSupport vector machineArtificial neural networkArticle10.1016/j.mee.2022.1118342621122-s2.0-85132850160Q2WOS:000829756000005Q3