Synthesis and analysis of TiO2 nanotubes by electrochemical anodization and machine learning method for hydrogen sensors

Isik E., Tasyurek L. B., IŞIK İ., KILINÇ N.

MICROELECTRONIC ENGINEERING, cilt.262, 2022 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 262
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1016/j.mee.2022.111834
  • Dergi Adı: MICROELECTRONIC ENGINEERING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Chemical Abstracts Core, Communication Abstracts, INSPEC, Metadex, Civil Engineering Abstracts
  • Anahtar Kelimeler: Titanium dioxide, Nanotubes, Hydrogen sensor, Anodization, Support vector machine, Artificial neural network, ARTIFICIAL NEURAL-NETWORK, PHOTOELECTROCHEMICAL PROPERTIES, SENSING PROPERTIES, ARRAYS, FILM, PD, FABRICATION, MORPHOLOGY, ANATASE
  • İnönü Üniversitesi Adresli: Evet

Özet

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 degrees C. The amorphous, rutile and anatase phases of TiO2 are observed for as-prepared TiO2 nanotubes, annealed at 700 and 300 degrees 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 similar to 70 nm to similar 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.