scholarly journals Structure of High-Entropy AlCoCrFeNi Alloy Obtained by Laser Alloying

2021 ◽  
Vol 43 (3) ◽  
pp. 399-406
Author(s):  
V. V. Girzhon ◽  
◽  
V. V. Yemelianchenko ◽  
O. V. Smolyakov ◽  
◽  
...  
Keyword(s):  
Laser Alloying ◽  
High Entropy

scholarly journals Surface hardening of CrCoFeNi high-entropy alloys via Al laser alloying

2021 ◽  
Vol 9 (10) ◽  
pp. 437-444
Author(s):  
Shuyuan Gou ◽  
Shunchao Li ◽  
Hailei Hu ◽  
Youtong Fang ◽  
Jiabin Liu ◽  
...  
Keyword(s):  
Surface Hardening ◽  
High Entropy Alloys ◽  
Laser Alloying ◽  
High Entropy

Layered High-Entropy Oxide Structures for Reversible Energy Storage

2020 ◽  
Author(s):  
Junbo Wang ◽  
Yanyan Cui ◽  
Qingsong Wang ◽  
Kai Wang ◽  
Xiaohui Wang ◽  
...  
Keyword(s):  
Energy Storage ◽  
Cathode Material ◽  
Spray Pyrolysis ◽  
Lattice Structure ◽  
Oxide Materials ◽  
High Entropy ◽  
New Class ◽  
Nebulized Spray Pyrolysis

<p>Layered Li<i><sub>x</sub></i>MO<sub>2</sub> materials, a new class of high-entropy oxides, have been synthesized by nebulized spray pyrolysis. Specifically, the lattice structure of Li(Ni<sub>1/3</sub>Mn<sub>1/3</sub>Co<sub>1/3</sub>)O<sub>2</sub> (NCM111) cathode material has been replicated successfully while increasing the number of cations in equimolar proportions, thereby allowing transition to high-entropy oxide materials.</p>

High-Entropy Alloys as Catalysts for the CO2 and CO Reduction Reactions

2019 ◽  
Author(s):  
Jack Pedersen ◽  
Thomas Batchelor ◽  
Alexander Bagger ◽  
Jan Rossmeisl
Keyword(s):  
Density Functional ◽  
Rational Design ◽  
Hydrogen Adsorption ◽  
Co Adsorption ◽  
Reduction Reaction ◽  
Supervised Machine Learning ◽  
High Entropy Alloys ◽  
High Entropy ◽  
Co Reduction ◽  
Disordered Alloy

Using the high-entropy alloys (HEAs) CoCuGaNiZn and AgAuCuPdPt as starting points we provide a framework for tuning the composition of disordered multi-metallic alloys to control the selectivity and activity of the reduction of carbon dioxide (CO2) to highly reduced compounds. By combining density functional theory (DFT) with supervised machine learning we predicted the CO and hydrogen (H) adsorption energies of all surface sites on the (111) surface of the two HEAs. This allowed an optimization for the HEA compositions with increased likelihood for sites with weak hydrogen adsorption{to suppress the formation of molecular hydrogen (H2) and with strong CO adsorption to favor the reduction of CO. This led to the discovery of several disordered alloy catalyst candidates for which selectivity towards highly reduced carbon compounds is expected, as well as insights into the rational design of disordered alloy catalysts for the CO2 and CO reduction reaction.

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