Photo-detector diode based on thermally oxidized TiO2 nanostructures/p-Si heterojunction

2016 ◽  
Vol 119 (1) ◽  
pp. 014503 ◽  
Author(s):  
Z. S. Hosseini ◽  
M. Shasti ◽  
S. Ramezani Sani ◽  
A. Mortezaali
Keyword(s):  
Tio2 Nanostructures ◽  
Detector Diode

scholarly journals Feasibility of Machine Learning Algorithms for Predicting the Deformation of Anodic Titanium Films by Modulating Anodization Processes

Materials ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1089
Author(s):  
Sung-Hee Kim ◽  
Chanyoung Jeong
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Multiclass Classification ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Smart Manufacturing ◽  
Gradient Boosting ◽  
Experimental Conditions ◽  
Learning Techniques ◽  
Tio2 Nanostructures

This study aims to demonstrate the feasibility of applying eight machine learning algorithms to predict the classification of the surface characteristics of titanium oxide (TiO2) nanostructures with different anodization processes. We produced a total of 100 samples, and we assessed changes in TiO2 nanostructures’ thicknesses by performing anodization. We successfully grew TiO2 films with different thicknesses by one-step anodization in ethylene glycol containing NH4F and H2O at applied voltage differences ranging from 10 V to 100 V at various anodization durations. We found that the thicknesses of TiO2 nanostructures are dependent on anodization voltages under time differences. Therefore, we tested the feasibility of applying machine learning algorithms to predict the deformation of TiO2. As the characteristics of TiO2 changed based on the different experimental conditions, we classified its surface pore structure into two categories and four groups. For the classification based on granularity, we assessed layer creation, roughness, pore creation, and pore height. We applied eight machine learning techniques to predict classification for binary and multiclass classification. For binary classification, random forest and gradient boosting algorithm had relatively high performance. However, all eight algorithms had scores higher than 0.93, which signifies high prediction on estimating the presence of pore. In contrast, decision tree and three ensemble methods had a relatively higher performance for multiclass classification, with an accuracy rate greater than 0.79. The weakest algorithm used was k-nearest neighbors for both binary and multiclass classifications. We believe that these results show that we can apply machine learning techniques to predict surface quality improvement, leading to smart manufacturing technology to better control color appearance, super-hydrophobicity, super-hydrophilicity or batter efficiency.

scholarly journals Ni/Si-Codoped TiO2 Nanostructure Photoanode for Enhanced Photoelectrochemical Water Splitting

Materials ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4102 ◽  
Author(s):  
Ting Li ◽  
Dongyan Ding
Keyword(s):  
Water Splitting ◽  
Annealing Temperature ◽  
Photocurrent Density ◽  
Electrochemical Anodization ◽  
Codoped Tio2 ◽  
Lower Charge ◽  
Si Doped ◽  
Tio2 Nanostructures ◽  
Tio2 Photoanode ◽  
Pec Water Splitting

We synthesized Ni/Si-codoped TiO2 nanostructures for photoelectrochemical (PEC) water splitting, by electrochemical anodization of Ti-1Ni-5Si alloy foils in ethylene glycol/glycerol solutions containing a small amount of water. The effects of annealing temperature on PEC properties of Ni/Si-codoped TiO2 photoanode were investigated. We found that the Ni/Si-codoped TiO2 photoanode annealed at 700 °C had an anatase-rutile mixed phase and exhibited the highest photocurrent density of 1.15 mA/cm2 at 0 V (vs. Ag/AgCl), corresponding to a photoconversion efficiency of 0.70%, which was superior to Ni-doped and Si-doped TiO2. This improvement in PEC water splitting could be attributed to the extended light absorption, faster charge transfer, possibly lower charge recombination, and longer lifetime.

Fabrication and characterization of mesoporous carbon nanosheets-1D TiO2 nanostructures

2010 ◽  
Vol 20 (12) ◽  
pp. 2424 ◽  
Author(s):  
Wenqin Peng ◽  
Zhengming Wang ◽  
Noriko Yoshizawa ◽  
Hiroaki Hatori ◽  
Takahiro Hirotsu ◽  
...  
Keyword(s):  
Mesoporous Carbon ◽  
Carbon Nanosheets ◽  
Fabrication And Characterization ◽  
Tio2 Nanostructures

scholarly journals Large-scale functionalization of biomedical porous titanium scaffolds surface with TiO2 nanostructures

2017 ◽  
Vol 61 (4) ◽  
pp. 557-564 ◽  
Author(s):  
Lei Qian ◽  
Peng Yu ◽  
Jinquan Zeng ◽  
Zhifeng Shi ◽  
Qiyou Wang ◽  
...  
Keyword(s):  
Large Scale ◽  
Porous Titanium ◽  
Tio2 Nanostructures
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