High-Entropy Alloys: Advantages and Applications in Challenging Environments
Bibliographic record
Abstract
Modern mechanical applications demand robust materials with boosted mechanical properties capable of resisting challenging working conditions.Single substances and pure material might fail to meet all application requirements translated by larger robustness and endurance.Correspondingly, scholars developed functional substances recognized with high-energy alloys (HEAs) with upgraded strength, durability, and corrosion behavior.Nonetheless, the available literature requires further research that provides sufficient insights pertaining to HEAs' contributions.Consequently, this research is guided, aiming to bridge this knowledge gap by exploring the contributory merits and valuable benefits of HEAs when engaged in challenging operating circumstances.The article addresses the promising HEA gains, their leading features, relevant properties, and diversified applications.The method adopted in this work comprises a scoping review through which multiple peer-reviewed articles and recent publications (2003 to 2023) were surveyed, addressing contributory gains of HEAs in fulfilling enhanced mechanical performance for different applications.Based on the scoping overview led in this paper, it was found that HEAs could serve in multiple engineering areas under challenging working conditions owing to their practical properties, namely elevated hardness, augmented mechanical strength, amended fatigue resistance, elaborated ductility, optimal toughness, superior microstructure stability at high temperatures, and exceptional wear resistance, considerable corrosion resistance, and boosted oxidation resilience.Accordingly, these excellent characteristics enable their broad implementation in vital engineering disciplines and arduous practices, notably aviation, automotive, maritime, energy storage systems (ESSs), and additive manufacturing.Additionally, the review outcomes revealed that mixing multiple elements together with numerous crystal structures could provide significant strength-toweight ratios, helping exhibit various potent features compared with traditional alloys.In light of this framework, the implications of this research are mirrored by focusing more attention on the consequential engineering influences and feasible practicalities of HEAs to promote their extensive utilization in multiple domains, allowing supportive qualities and advantageous effects on entire material characteristics to each application they are engaged in.From this perspective, it is suggested to manage additional research processes to classify vital gains of HEAs and elucidate their added value.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".