Fei Liang
4 Papers
Fei Liang is an academic researcher. The author has contributed to research in topics: Chemistry & Medicine. The author has an hindex of 2, co-authored 4 publications.
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Papers
Heteroanionic Melilite Oxysulfide: A Promising Infrared Nonlinear Optical Candidate with a Strong Second-Harmonic Generation Response, Sufficient Birefringence, and Wide Bandgap.
Ruiqi Wang,Fei Liang,Xin Liu,Yi Xiao,Qianqian Liu,Xian Zhang,Limin Wu,Ling Chen,Fuqiang Huang +8 more
TL;DR: In this paper , the authors theoretically confirmed that the heteroanionic oxysulfide tetrahedron would produce improved polarizability anisotropy and quadratic hyperpolarizability compared with the monoanionic oxide or sulfide tetrashedra.
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Exfoliated 2D Layered and Nonlayered Metal Phosphorous Trichalcogenides Nanosheets as Promising Electrocatalysts for CO2 Reduction.
Honglei Wang,Yunfei Jiao,Bing-Cai Wu,Dayong Wang,Yue Lian Hu,Fei Liang,Chensi Shen,Andrea Knauer,Dan Ren,Hongguang Wang,Peter A. van Aken,Hongbin Zhang,Michael Grätzel,Zdeněk Sofer,Peter Schaaf +14 more
TL;DR: In this paper , the authors successfully exfoliated both layered and non-layered ultra-thin metal phosphorous trichalcogenides (MPCh3) nanosheets via wet grinding exfoliation (WGE), and systematically investigated the mechanism of MPCh3 as catalysts for CO2 ECR.
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SrAgAsS4: A Noncentrosymmetric Sulfide with Good Infrared Nonlinear Optical Performance Induced by Aliovalent Substitution from Centrosymmetric SrGa2S4.
TL;DR: In this article , a new non-centrosymmetric quaternary sulfide, SrAgAsS4, was obtained via the strategy of aliovalent substitution based on centroidymmetric (CS) SrGa2S4.
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Accelerated Screening of Ternary Chalcogenides for High-Performance Optoelectronic Materials
Chensi Shen,Tianshu Li,Yixuan Zhang,Teng Long,Nuno M. Fortunato,Fei Liang,Mian Dai,Jiahong Shen,Chris Wolverton,Hongbin Zhang +9 more
- 04 May 2023
TL;DR: In this article , the authors implemented a systematic high-throughput screening process combined with first-principles calculations on ternary chalcogenides using 34 crystal structure prototypes and trained a model based on crystal graph convolutional neural networks to predict the thermodynamic stability of novel materials.