Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 3,016

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

High-entropy alloy: challenges and prospects

Open access
Ye, Y.F., Wang, Q., Lu, J., Liu, C.T., Yang, Y.·2016·Materials Today

Abstract

High-entropy alloys (HEAs) are presently of great research interest in materials science and engineering. Unlike conventional alloys, which contain one and rarely two base elements, HEAs comprise multiple principal elements, with the possible number of HEA compositions extending considerably more than conventional alloys. With the advent of HEAs, fundamental issues that challenge the proposed theories, models, and methods for conventional alloys also emerge. Here, we provide a critical review of the recent studies aiming to address the fundamental issues related to phase formation in HEAs. In addition, novel properties of HEAs are also discussed, such as their excellent specific strength, superior mechanical performance at high temperatures, exceptional ductility and fracture toughness at cryogenic temperatures, superparamagnetism, and superconductivity. Due to their considerable structural and functional potential as well as richness of design, HEAs are promising candidates for new applications, which warrants further studies.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.mattod.2015.11.026

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 4 references

Cited by

See all 3,016

Metadata

Authors
Ye, Y. F., Wang, Q., Lu, J., Liu, C. T., & Yang, Y.
Venue
Materials Today
Publisher
Elsevier BV
Published
2016-07
Volume
19
Issue
6
Pages
349-362
ISSN
1369-7021 (ISSN-L)
OpenAlex
W2201563651
Cited by
3,016
References
4
FWCI
58.52
Type
journal article
Language
en
Open access
hybrid

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: High-entropy alloy: challenges and prospects. https://app.cochat.ai/papers/10.1016/j.mattod.2015.11.026
The paper
Ye, Y. F., Wang, Q., Lu, J., Liu, C. T., & Yang, Y. (2016). High-entropy alloy: challenges and prospects. Materials Today. https://doi.org/10.1016/j.mattod.2015.11.026

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.