Lianping Wu
University of Maryland, College Park
14 Papers
5 Citations
Lianping Wu is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Catalysis & Chemistry. The author has an hindex of 5, co-authored 7 publications. Previous affiliations of Lianping Wu include Xi'an Jiaotong University.
Chat about Author
Papers
High temperature shockwave stabilized single atoms.
Yonggang Yao,Zhennan Huang,Pengfei Xie,Lianping Wu,Lu Ma,Tangyuan Li,Zhenqian Pang,Miaolun Jiao,Zhiqiang Liang,Jinlong Gao,Yang He,Dylan J. Kline,Michael R. Zachariah,Chongmin Wang,Jun Lu,Tianpin Wu,Teng Li,Chao Wang,Reza Shahbazian-Yassar,Liangbing Hu +19 more
TL;DR: A repeated on–off high-temperature shockwave is shown to be a generalizable way of efficiently synthesizing and stabilizing single atoms at high temperatures, which opens a general route for single-atom manufacturing that is conventionally challenging.
355
Surface‐Decorated High‐Entropy Alloy Catalysts with Significantly Boosted Activity and Stability
Kaizhu Zeng,Jianwei Zhang,Wenqiang Gao,Lianping Wu,Hanwen Liu,Jinlong Gao,Zezhou Li,Jihan Zhou,Teng Li,Zhiqiang Liang,Bingjun Xu,Yonggang Yao +11 more
TL;DR: In this paper , surface decoration of HEA nanoparticles to improve the overall activity, stability, and reduce cost is reported, and a two-step process is employed to first synthesize non-noble HEA (FeCoNiSn) nanoparticles and then are surface alloyed with Pd (main active site), denoted as NHEA@NHEA•Pd.
74
Data‐Driven High‐Throughput Rational Design of Double‐Atom Catalysts for Oxygen Evolution and Reduction
Lianping Wu,Tian Guo,Teng Li +2 more
TL;DR: This data‐driven high‐throughput approach not only yields remarkable prediction precision (>0.926 R‐squared) but also enables a notable 144 000‐fold reduction of screening time compared with pure DFT calculations, holding promise to drastically accelerate the design of high‐performance DACs.
71
Machine learning-accelerated prediction of overpotential of oxygen evolution reaction of single-atom catalysts.
Lianping Wu,Tian Guo,Teng Li +2 more
TL;DR: A topological information-based ML model is designed to map the OER overpotentials with atomic properties of the corresponding SACs and an intrinsic descriptor that correlates the overpotential of an SAC with its atomic properties is revealed.
70