Evaluation of Equiatomic CrMnFeCoNiCu System and Subsequent Derivation of a Non-Equiatomic MnFeCoNiCu Alloy

合金 材料科学 冶金
作者
Artashes Ter-Isahakyan,T. John Balk
出处
期刊:Materials [Multidisciplinary Digital Publishing Institute]
卷期号:16 (6): 2455-2455 被引量:1
标识
DOI:10.3390/ma16062455
摘要

Investigation into non-equiatomic high-entropy alloys has grown in recent years due to questions about the role of entropy stabilization in forming single-phase solid solutions. Non-equiatomic alloys have been shown to retain the outstanding mechanical properties exhibited by their equiatomic counterparts and even improve electrical, thermal, and magnetic properties, albeit with relaxed composition bounds. However, much remains to understand the processing-structure-property relationships in all classes of so-called high-entropy alloys (HEAs). Here, we are motivated by the natural phenomena of crystal growth and equilibrium conditions to introduce a method of HEA development where controlled processing conditions determine the most probable and stable composition. This is demonstrated by cooling an equiatomic CrMnFeCoNiCu alloy from the melt steadily over 3 days (cooling rate ~4 °C/h). The result is an alloy containing large Cr-rich precipitates and an almost Cr-free matrix exhibiting compositions within the MnFeCoNiCu system (with trace amounts of Cr). From this juncture, it is argued that the most stable composition is within the CrMnFeCoNiCu system rather than the CrMnFeCoNi system. With further optimization and evaluation, a unique non-equiatomic alloy, Mn17Fe21Co24Ni24Cu14, is derived. The alloy solidifies and recrystallizes into a single-phase face-centered cubic (FCC) polycrystal. In addition to possible applications where Invar is currently utilized, this alloy can be used in fundamental studies that contrast its behavior with its equiatomic counterpart and shed light on the development of HEAs.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
1秒前
代纤绮发布了新的文献求助10
1秒前
1秒前
2秒前
晓桐发布了新的文献求助10
2秒前
妮妮宝发布了新的文献求助10
2秒前
碧蓝井发布了新的文献求助10
3秒前
3秒前
李健应助研友_ZlxK6Z采纳,获得10
3秒前
3秒前
3秒前
英俊的铭应助elle采纳,获得10
4秒前
一只完成签到,获得积分10
4秒前
梦梦完成签到 ,获得积分10
5秒前
加菲关注了科研通微信公众号
5秒前
彭于晏应助IsabelleKong采纳,获得10
6秒前
6秒前
6秒前
zzz应助予三千笔墨采纳,获得10
6秒前
6秒前
7秒前
7秒前
正直大树发布了新的文献求助10
7秒前
7秒前
Learn123完成签到,获得积分20
7秒前
MissF发布了新的文献求助10
8秒前
8秒前
8秒前
8秒前
9秒前
活力友绿发布了新的文献求助10
9秒前
10秒前
10秒前
Cris完成签到,获得积分10
10秒前
11秒前
热心市民发布了新的文献求助10
11秒前
暮商零七发布了新的文献求助10
11秒前
与君关注了科研通微信公众号
11秒前
12秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Chemistry and Physics of Carbon Volume 18 800
The Organometallic Chemistry of the Transition Metals 800
The formation of Australian attitudes towards China, 1918-1941 640
Signals, Systems, and Signal Processing 610
全相对论原子结构与含时波包动力学的理论研究--清华大学 500
Elevating Next Generation Genomic Science and Technology using Machine Learning in the Healthcare Industry Applied Machine Learning for IoT and Data Analytics 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6443142
求助须知:如何正确求助?哪些是违规求助? 8257058
关于积分的说明 17585007
捐赠科研通 5501690
什么是DOI,文献DOI怎么找? 2900830
邀请新用户注册赠送积分活动 1877812
关于科研通互助平台的介绍 1717461