Characterization of titanium dioxide atomic layer growth from titanium ethoxide and water

2000 ◽  
Vol 370 (1-2) ◽  
pp. 163-172 ◽  
Author(s):  
Jaan Aarik ◽  
Aleks Aidla ◽  
Väino Sammelselg ◽  
Teet Uustare ◽  
Mikko Ritala ◽  
...  
Keyword(s):  
Titanium Dioxide ◽  
Atomic Layer ◽  
Layer Growth ◽  
Titanium Ethoxide

Titanium isopropoxide as a precursor for atomic layer deposition: characterization of titanium dioxide growth process

2000 ◽  
Vol 161 (3-4) ◽  
pp. 385-395 ◽  
Author(s):  
Jaan Aarik ◽  
Aleks Aidla ◽  
Teet Uustare ◽  
Mikko Ritala ◽  
Markku Leskelä
Keyword(s):  
Titanium Dioxide ◽  
Atomic Layer Deposition ◽  
Growth Process ◽  
Atomic Layer ◽  
Titanium Isopropoxide ◽  
Layer Deposition

Atomic Layer Growth and Characterization of ZnO Thin Films

2002 ◽  
Vol 229 (2) ◽  
pp. 925-929 ◽  
Author(s):  
K. Saito ◽  
Y. Yamamoto ◽  
A. Matsuda ◽  
S. Izumi ◽  
T. Uchino ◽  
...  
Keyword(s):  
Thin Films ◽  
Atomic Layer ◽  
Layer Growth ◽  
Zno Thin Films

scholarly journals Magneto-optical characterization of ZnO / Ni nano-laminate obtained via Atomic Layer Deposition

2021 ◽  
Vol 1762 (1) ◽  
pp. 012041
Author(s):  
K Buchkov ◽  
A Galluzzi ◽  
B Blagoev ◽  
A Paskaleva ◽  
P Terziyska ◽  
...  
Keyword(s):  
Atomic Layer Deposition ◽  
Atomic Layer ◽  
Optical Characterization ◽  
Layer Deposition

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

Nanomaterials ◽  
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.

Sign in / Sign up

Export Citation Format

Share Document