7 Papers
12 Citations
Ming De Ou is an academic researcher from National Yunlin University of Science and Technology. The author has contributed to research in topics: Chemistry & Medicine. The author has an hindex of 1, co-authored 1 publications.
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Papers
Regulating Na Occupation in P2‐Type Layered Oxide Cathode for All‐Climate Sodium‐Ion Batteries
Siying Liu,Jing Wan,Ming De Ou,Wen Zhang,Miao Chang,Fang Cheng,Yue Xu,Shixiong Sun,Cheng Luo,Kai Yang,Chun Fang,Jiantao Han +11 more
TL;DR: In this paper , the authors investigated the storage mechanism in P2-type NNMO and provided a universal strategy to improve the rate and cycling life of P2•type layered oxide cathode materials.
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Correlation between Potassium-Ion Storage Mechanism and Local Structural Evolution in Hard Carbon Materials
Jia Xu,Chenyang Fan,Ming De Ou,Shixiong Sun,Yue Xu,Yi Liu,Xin Wang,Qing Li,Chun Fang,Jiantao Han +9 more
TL;DR: In this article , a series of hard carbon materials with a continuously adjustable structure were synthesized at 800-2900 °C to study the structure-mechanism relationships of hard carbons.
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Analysis of tunable transmission properties in photonic crystals containing doped semiconductor
TL;DR: In this paper, the authors investigate the tunable transmission properties in a photonic crystal (PC) that contains doped semiconductor, n-GaAs, as a defect layer.
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Modulation of Redox Chemistry of Na2Mn3O7 by Selective Boron Doping Prompted by Na Vacancies.
Jing Wan,Yuegang Qiu,Xueping Sun,Ming De Ou,Jia Xu,Xiaoyu Zhang,Yi Liu,Shixiong Sun,Yue Xu,Chun Fang,Li Huang,Paul K. Chu,Jiantao Han +12 more
TL;DR: In this article , a Na vacancy-induced boron doping strategy is demonstrated to improve the properties of transition metal-oxide-layered electrode materials, and the results reveal that it is an important strategy for studying transition metaloxide-layer electrode materials.
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Domain Generalization in Restoration of Cataract Fundus Images Via High-Frequency Components
Haofeng Liu,Heng Li,Ming De Ou,Yitian Zhao,Hong Qi,Yan Hu,Jiang Liu +6 more
- 28 Mar 2022
TL;DR: A restoration algorithm is designed for cataractous images without paired or annotated data using domain generalization to learn domain-invariant features (DIFs) from synthesized data, and the high-frequency components are extracted to conduct domain alignment.
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