Characterization of Photochemically or Electrochemically Reduced Blue Titanium Dioxide

Blue TiO2 which is formed under UV irradiation under inert gases in the presence of hole scavengers is characterized. The concentration of surface-trapped electrons which cause the blue colour was analytically determined after irradiation in the presence of methanol or other organic compounds. The saturation concentration is controlled by a dynamic equilibrium of photoreduction and reoxidation of the reduced surface sites which leads to H2 formation. The blue colour can also be produced by chemical or electrochemical reduction. Mobility of the surface-trapped electrons in an electrical field gradient is demonstrated. The reduced TiO2 is similar to a hydrogen titanium bronze.

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Synthesis and Characterization of Titanium Dioxide Films and There Application In P3HT/TiO2 Hybrid Solar Cells

Indian Journal Of Applied Research ◽

10.15373/2249555x/aug2013/207 ◽

2011 ◽

Vol 3 (8) ◽

pp. 645-648 ◽

Cited By ~ 2

Author(s):

Haidar Gazy Lazim ◽

◽

Khalid. I Ajeel ◽

Aseel Hassan

Keyword(s):

Titanium Dioxide ◽

Solar Cells ◽

Synthesis And Characterization ◽

Hybrid Solar Cells ◽

Titanium Dioxide Films

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Green synthesis and characterization of titanium dioxide nanoparticles using leaf extract of Pouteria campechiana and larvicidal and pupicidal activity on Aedes aegypti

Environmental Research ◽

10.1016/j.envres.2021.111333 ◽

2021 ◽

pp. 111333

Author(s):

Mathiyazhagan Narayanan ◽

P.Gokila Devi ◽

Devarajan Natarajan ◽

Sabariswaran Kandasamy ◽

Kesavan Devarayan ◽

...

Keyword(s):

Titanium Dioxide ◽

Aedes Aegypti ◽

Green Synthesis ◽

Leaf Extract ◽

Titanium Dioxide Nanoparticles ◽

Synthesis And Characterization

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Deep Learning Based Instance Segmentation of Titanium Dioxide Particles in the Form of Agglomerates in Scanning Electron Microscopy

Nanomaterials ◽

10.3390/nano11040968 ◽

2021 ◽

Vol 11 (4) ◽

pp. 968

Author(s):

Paul Monchot ◽

Loïc Coquelin ◽

Khaled Guerroudj ◽

Nicolas Feltin ◽

Alexandra Delvallée ◽

...

Keyword(s):

Electron Microscopy ◽

Scanning Electron Microscopy ◽

Titanium Dioxide ◽

Learning Community ◽

Spherical Particles ◽

Average Value ◽

Size Characterization ◽

Scanning Electron

The size characterization of particles present in the form of agglomerates in images measured by scanning electron microscopy (SEM) requires a powerful image segmentation tool in order to properly define the boundaries of each particle. In this work, we propose to use an algorithm from the deep statistical learning community, the Mask-RCNN, coupled with transfer learning to overcome the problem of generalization of the commonly used image processing methods such as watershed or active contour. Indeed, the adjustment of the parameters of these algorithms is almost systematically necessary and slows down the automation of the processing chain. The Mask-RCNN is adapted here to the case study and we present results obtained on titanium dioxide samples (non-spherical particles) with a level of performance evaluated by different metrics such as the DICE coefficient, which reaches an average value of 0.95 on the test images.

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