Evidence map›Paper›PMID 39619588›Full record

ReviewHeliyon2024

Exploring high entropy alloys: A review on thermodynamic design and computational modeling strategies for advanced materials applications.

Peter Ifeolu Odetola, Bukola Joseph Babalola, Ayodeji Ebenezer Afolabi, Ufoma Silas Anamu, Emmanuel Olorundaisi, Mutombo Christian Umba, Thabang Phahlane, Olusoji Oluremi Ayodele, Peter Apata Olubambi

RetractedAbstract readReviewRetracted Publication
PubMed Full text at PMC Publisher
In one paragraph

Review in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Peter Ifeolu OdetolaCentre for Nanoengineering and Advanced Materials, School of Mining, Metallurgy and Chemical Engineering, University of Johannesburg, Johannesburg, 2092, South Africa.
Bukola Joseph BabalolaCentre for Nanoengineering and Advanced Materials, School of Mining, Metallurgy and Chemical Engineering, University of Johannesburg, Johannesburg, 2092, South Africa.
Ayodeji Ebenezer AfolabiCentre for Nanoengineering and Advanced Materials, School of Mining, Metallurgy and Chemical Engineering, University of Johannesburg, Johannesburg, 2092, South Africa.
Ufoma Silas AnamuCentre for Nanoengineering and Advanced Materials, School of Mining, Metallurgy and Chemical Engineering, University of Johannesburg, Johannesburg, 2092, South Africa.
Emmanuel OlorundaisiCentre for Nanoengineering and Advanced Materials, School of Mining, Metallurgy and Chemical Engineering, University of Johannesburg, Johannesburg, 2092, South Africa.
Mutombo Christian UmbaCentre for Nanoengineering and Advanced Materials, School of Mining, Metallurgy and Chemical Engineering, University of Johannesburg, Johannesburg, 2092, South Africa.
Thabang PhahlaneCentre for Nanoengineering and Advanced Materials, School of Mining, Metallurgy and Chemical Engineering, University of Johannesburg, Johannesburg, 2092, South Africa.
Olusoji Oluremi AyodeleCentre for Nanoengineering and Advanced Materials, School of Mining, Metallurgy and Chemical Engineering, University of Johannesburg, Johannesburg, 2092, South Africa.
Peter Apata OlubambiCentre for Nanoengineering and Advanced Materials, School of Mining, Metallurgy and Chemical Engineering, University of Johannesburg, Johannesburg, 2092, South Africa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the quest for materials that can withstand the rigors of modern engineering applications, high-entropy alloys (HEAs) have emerged as a frontier in material science owing to their unprecedented combination of properties. This review focuses on intricate thermodynamic and computational modeling to guide the design and optimization of HEAs. By dissecting the foundational "four core effects" intrinsic to HEAs-high entropy, sluggish diffusion, severe lattice distortion, and cocktail effect-we illuminate the path towards predictable and tailored material properties. Central to the present discourse is the application of valence electron concentration (VEC) and cutting-edge strategies, including the CALculation of PHAse Diagrams (CALPHAD) method, first-principles approach, and machine-learning algorithms, which collectively empower the prediction and understanding of HEA behavior. Through a novel case study of a septenary equiatomic Ni-Al-Co-Cr-Cu-Mn-Ti HEA, this analysis demonstrates the utility of these computational tools in unveiling the alloy's phase stability and microstructural evolution, reinforcing the synergy between theoretical predictions and experimental validation. Furthermore, the review explores the burgeoning applications of HEAs across diverse sectors, such as aerospace, automotive, energy, and biomedical engineering, highlighting their transformative potential. Despite these advancements, challenges such as empirical design limitations, processing complexities, and the need for comprehensive databases are acknowledged, setting the stage for future exploration. This review not only charts a course for the rational design of HEAs, but also envisages their role in advancing material science towards novel applications, urging a concerted effort to overcome existing hurdles and explore uncharted territories in HEA research.

Indexed as

CALPHADCompositionComputational modelingHigh-entropy alloysMechanical propertiesMicrostructural evolutionMulti-componentTheoretical calculationsThermodynamic design

Identifiers

PMID39619588
PMCPMC11605370

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.