T
Tao Yang
Researcher at University of Science and Technology Beijing
Publications - 21
Citations - 253
Tao Yang is an academic researcher from University of Science and Technology Beijing. The author has contributed to research in topics: Chemistry & Engineering. The author has an hindex of 1, co-authored 1 publications receiving 9 citations.
Papers
More filters
Journal ArticleDOI
Piezoelectric nanogenerators with high performance against harsh conditions based on tunable N doped 4H-SiC nanowire arrays
TL;DR: In this paper, a large scale free-standing N doped 4H-SiC nanowire arrays (NWAs) fabricated by anodic oxidation of single-crystalline N-doped four-hexagonal SiC wafer are proposed for the first time.
Journal ArticleDOI
Boosting of water splitting using the chemical energy simultaneously harvested from light, kinetic energy and electrical energy using N doped 4H-SiC nanohole arrays
Linlin Zhou,Tao Yang,Zhi Xiang Fang,Jiadong Zhou,Yapeng Zheng,Chunyu Guo,Laipan Zhu,Enhui Wang,Xinmei Hou,Kuo-Chih Chou,Zhong Lin Wang +10 more
TL;DR: In this article , a method of inducing piezopotential in N doped 4H-SiC nanohole arrays (NHAs) for water splitting is proposed, and the modulation effects of N doping with various contents on the electronic structure and piezoelectricity of 4HSiC are comprehensively analyzed.
Journal ArticleDOI
The mechanism of PVDF/CsPbBr3 perovskite composite fiber as self-polarization piezoelectric nanogenerator with ultra-high output voltage
You Xue,Tao Yang,Yapeng Zheng,Enhui Wang,Hongyang Wang,Laipan Zhu,Zhentao Du,Xinmei Hou,Kuo-Chih Chou +8 more
TL;DR: In this paper , a piezoelectric properties of conventional PDE materials are generally obtained through rearrangement of dipoles by electric poling process, however, they show significant attenuation after removal of external electric field.
Journal ArticleDOI
A new strategy for long-term complex oxidation of MAX phases: database generation and oxidation kinetic model establishment with aid of machine learning
TL;DR: In this paper , a machine learning-based real physical picture (ML-RPP) model can accurately deal with the long-term complex oxidation of various MAX phases, which can provide a useful guideline for the cognition of complex oxidation in other ceramics and alloys.