journal article·2018-10-14

Compositional gradient films constructed by sputtering in a multicomponent Ti–Al–(Cr, Fe, Ni) system

Yong Zhang, XueHui Yan, Jiang Ma, ZhaoPing Lu, YuHong Zhao

Journal of Materials Research· Volume 33· Issue 19
Publisher page

Abstract

It has been reported that the optimal properties of materials are usually not linear to the configuration entropy of materials; in another word, the high-entropy alloys may not have the best properties among all the alloys, including medium-entropy alloys, thus all of these alloys can be universally named as entropic alloys. For entropic alloys, the design, discovery, and optimization of new materials are more complicated than conventional materials. A technique of high-throughput processing is urgently needed to improve the efficiency. In this paper, a combined method by using multitarget deposition has been proposed for parallel preparation of high-entropy to medium-entropy alloys. Films with compositional gradient were constructed in a pseudo-ternary Ti–Al–(Cr, Fe, Ni) system in this study. To facilitate the characterization of the material library, it has been divided into 144 independent units with an area of 1 cm2and the maximum value of compositional gradient reaches ∼13 at.%/cm. The material library exhibits a high coverage of composition, and the range of element content varies from 3.3 to 89.2 at.% on average. The stability and homogeneity of the material library were analyzed from phase structure and microtopography. Preliminary screening of the phase structure and properties were performed. The phases are mainly composed of amorphous phase and body-centered cubic phase. Hardness changes nonlinearly with compositions. The material library synthesized in this study is expected to provide an effective platform for high-throughput screening of multicomponent materials.
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Research context

OpenAlex topics: High Entropy Alloys Studies · Metal and Thin Film Mechanics · Advanced materials and composites

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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