Oxidation Performance of Fe-Ni-Co-Cr-Mn High Entropy Alloy and its Al-Containing Variants in Supercritical CO2
Bibliographic record
Abstract
Abstract In this study, a new material type, known as high entropy alloy (HEA), is being evaluated for use in supercritical CO2 (sCO2) Brayton power cycle. This cycle is a promising power generation technology that offers an increased efficiency and smaller footprint compared to the conventional Rankine steam cycle. However, the construction of components that operate in the high temperature and pressure regions of the cycle requires the use of expensive high-performance alloys such as Inconel 740H and Incoloy 800HT. As an alternative to these alloys, three compositions of HEAs are evaluated, namely, FeNiCoCrMn (HEA-1), FeNi1.5CoCrMnAl0.5 (HEA-2), FeNi1.5CoCrAl0.5 (HEA-3). The alloy samples were exposed to sCO2 at 700 °C, 20 MPa for 600 hours. They were then evaluated through weight measurements and characterized using scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and x-ray diffraction (XRD). The results showed that HEA-3 outperformed the two other compositions. It experienced an impressively low mass gain of 0.04 mg/cm2, compared to 0.65mg/cm2 and 0.25 mg/cm2 for HEA-1 and HEA-2, respectively. The lack of Mn enabled HEA-3 to form protective Cr2O3 oxides, whereas the other two compositions formed porous oxides containing MnO and Mn3O4. Furthermore, the results showed that HEA-3 has the potential to outperform many conventional superalloys being evaluated for sCO2 applications. Additional test campaign has been planned to study the effects of prolonged exposure of HEA-3 to high temperature and pressure sCO2 environment to further assess its performance and to do initial benchmarking with respect to other Ni-based alloys.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".