Abstract
Corrosion of alloys in molten salts is commonly understood from thermodynamics: the higher the content of noble elements in the alloy, the more corrosion resistant the alloy is expected to be. Here, we present an example in the CrFeMnNi compositionally complex space that defies this conventional intuition. Machine learning-facilitated analysis of the extensive dataset reveals that molten salt corrosion in this system is primarily predicted by the Ni mobility within the alloy. This discovery was made possible using high-throughput manufacturing and testing of a set of 110 compositionally complex alloys within the CrFeMnNi element space prepared by additive manufacturing in situ alloying processes and corrosion tested in standardized conditions of temperature and chlorine potential. A standardized, parametric dataset of this magnitude for corrosion in molten salts is a first of its kind. This dataset results in new insights into the corrosion mechanism of CrFeMnNi for clean energy-enabling technologies.
| Original language | English |
|---|---|
| Pages (from-to) | 2313-2335 |
| Number of pages | 23 |
| Journal | Matter |
| Volume | 7 |
| Issue number | 6 |
| Early online date | May 27 2024 |
| DOIs | |
| State | Published - Jun 5 2024 |
| Externally published | Yes |
Keywords
- high-entropy alloy
- high-throughput method
- machine learning
- MAP 3: Understanding
- molten salt corrosion
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