Preparation and characterization of polystyrene/titanium dioxide composite particles containing organic ultraviolet-stabilizer groups

2007 ◽  
Vol 104 (5) ◽  
pp. 2792-2798 ◽  
Author(s):  
Jiangpeng Zou ◽  
Yi Zhao ◽  
Mingjiao Yang ◽  
Yi Dan
Keyword(s):  
Titanium Dioxide ◽  
Composite Particles

Preparation and characterization of titanium dioxide core/polymer shell hybrid composite particles prepared by emulsion polymerization

2004 ◽  
Vol 92 (5) ◽  
pp. 2970-2975 ◽  
Author(s):  
Dong-Guk Yu ◽  
Jeong Ho An ◽  
Jin Young Bae ◽  
Yong Eui Lee ◽  
Seong Deok Ahn ◽  
...  
Keyword(s):  
Titanium Dioxide ◽  
Emulsion Polymerization ◽  
Hybrid Composite ◽  
Composite Particles ◽  
Polymer Shell ◽  
Core Polymer

CHARACTERIZATION OF BRUCITE/TiO2 COMPOSITE PARTICLE MATERIAL PREPARED BY MECHANO-CHEMICAL METHOD

2018 ◽  
Vol 25 (04) ◽  
pp. 1850085 ◽  
Author(s):  
YU LIANG ◽  
HAO DING ◽  
QIANG XUE
Keyword(s):  
Titanium Dioxide ◽  
Electron Microscope ◽  
Reaction Mechanism ◽  
Chemical Method ◽  
Composite Particle ◽  
Composite Particles ◽  
X Ray Diffraction ◽  
X Ray ◽  
Scanning Electron

Brucite/TiO2 composite particle was prepared with Titanium dioxide (TiO2), coating on brucite particles’ surfaces by mechano-chemical method. X-ray diffraction (XRD), scanning electron microscope (SEM) and infrared spectra were used to investigate the microstructures, morphologies of the composite and the reaction mechanism. The results show that the ground extent of brucite as well as the mixed particles of brucite and TiO2 should keep at a suitable state. The hiding power and whiteness of the composite particles are 17.24[Formula: see text]g/m2 and 96.88%, respectively, presenting its similarity to TiO2 in pigment properties. TiO2 coats evenly on the surfaces of brucite through surface dehydroxylation reaction during mechano-chemical method.

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