Sen Lu
13 Papers
Sen Lu is an academic researcher. The author has contributed to research in topics: Oxygen evolution & Density functional theory. The author has an hindex of 1, co-authored 2 publications.
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Papers
Bilayer MN4-O-MN4 by bridge-bonded oxygen ligands: Machine learning to accelerate the design of bifunctional electrocatalysts
Pengyue Shan,Xue Bai,Q. Y. Jiang,Yunjian Chen,Sen Lu,Pei Song,Zepeng Jia,Taiyang Xiao,Yang Han,Yazhou Wang,Tong Liu,H. Cui,Rong Feng,Qin Kang,Zhiyong Liang,Hongkuan Yuan +15 more
TL;DR: In this paper , a series of efficient monofunctional and bifunctional electrocatalysts were successfully predicted with an average prediction error of only 0.04 V and 0.06 V for ORR and OER overpotential.
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Heterojunction of MXenes and MN4-graphene: Machine learning to accelerate the design of bifunctional oxygen electrocatalysts.
Xue Bai,Sen Lu,Pei Song,Zepeng Jia,Zhikai Gao,Tiren Peng,Zhiguo Wang,Q. Y. Jiang,Hong Cui,Weizhi Tian,Rong Feng,Qin Kang +11 more
TL;DR: This study employs machine learning and density functional theory to design and optimize 153 MN4-graphene/MXene electrocatalysts for oxygen reduction and evolution reactions, identifying CoN4-Gra/Ti2NO as a promising bifunctional catalyst.
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Density Functional Theory Study of Superalkali NLi4-Decorated Graphdiyne Nanosheets as Hydrogen Storage Materials
Q. Y. Jiang,Xue Bai,Zepeng Jia,Sen Lu,Pei Song,Yunjian Chen,Pengyue Shan,H. Cui,Rong Feng,Qin Kang,Zhiyong Liang,Hongkuan Yuan +11 more
TL;DR: Density Functional Theory study reveals NLi4-decorated graphdiyne nanosheets exhibit exceptional hydrogen storage properties, with optimal configurations achieving gravimetric densities of up to 8.91 wt%, suggesting a promising avenue for novel hydrogen storage materials.
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Novel NLi4-BGra/MgH2-based heterojunctions for efficient hydrogen storage and modulation of hydrogen-desorption temperature ranges
Zepeng Jia,Sen Lu,Pei Song,Zhikai Gao,Zhiguo Wang,Tiren Peng,Weizhi Tian,Rong Feng,Qin Kang,Zhiyong Liang,Hongkuan Yuan +10 more
TL;DR: Researchers design novel NLi4-BGra/MgH2 heterojunctions for efficient hydrogen storage and temperature modulation, achieving controlled hydrogen release at 298-570 K, and validate the strategy's effectiveness through DFT and ML predictions.
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Machine-learning-assisted hydrogen adsorption descriptor design for bilayer MXenes
Weizhi Tian,Gongchang Ren,Yuanting Wu,Sen Lu,Yuan Huan,Tiren Peng,Peng Liu,Jiangong Sun,Hui Su,Hong Cui +9 more
TL;DR: Researchers developed machine-learning models to predict hydrogen adsorption energies in bilayer MXenes, achieving high accuracy with R2 values of 0.957 and 0.952 for chemisorption and physical adsorption, respectively, and identifying simple descriptors for material design.
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