Recent experimental and theoretical advances in the design and science of high-entropy alloy nanoparticles

被引:87
作者
Al Zoubi, Wail [1 ]
Putri, Rosy Amalia Kurnia [1 ]
Abukhadra, Mostafa R. [2 ,3 ]
Ko, Young Gun [1 ]
机构
[1] Yeungnam Univ, Sch Mat Sci & Engn, Mat Electrochem Lab, Gyongsan 38541, South Korea
[2] Beni Suef Univ, Fac Sci, Geol Dept, Bani Suwayf 65211, Egypt
[3] Beni Suef Univ, Fac Sci, Geol Dept, Mat Technol & their Applicat Lab, Bani Suwayf, Egypt
基金
新加坡国家研究基金会;
关键词
High -entropy alloy nanoparticles; Multiple elements; Stability; Catalysis; Machine learning; METALLIC NANOPARTICLES; BIMETALLIC CATALYSTS; OXIDE NANOPARTICLES; ASSISTED SYNTHESIS; SHOCK SYNTHESIS; PHASE SYNTHESIS; PLATINUM; PERFORMANCE; EVOLUTION; OXIDATION;
D O I
10.1016/j.nanoen.2023.108362
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
High-entropy alloy nanoparticles (HEA-NPs) hold unrestricted promise for various applications in an unlimited chemical compositional space, as shown in recent years. These HEA-NPs have received widespread attention in the design and exploration of catalysts with excellent activity because of their multiple elements with different compositions and unique HEA states (solid-solution) that can be used to guide improvements in stability and tunable performance. This great potential for synthesis is further assisted by formidable deviances originating from their structure of complex atoms and massive compositional space, which have delayed their inclusive exploration and an in-depth understanding. This review critically summarizes the evaluations of and currently existing fundamental knowledge on HEA-NPs and describes the steps necessary to develop applications in the fields of energy, catalysis, and sustainability. A roadmap is planned, demonstrating which of the concepts could be utilized using specific synthetic method and which advances might be likely to guide future scientific in-vestigations of this extremely versatile material for different applications, in particular by introducing the concept of machine learning towards HEA-NPs.
引用
收藏
页数:25
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