Role of scandium in mechanical alloying of AlCoCrFeMo high-entropy alloy powders for flame spray coating deposition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The growing demand for advanced materials that can withstand extreme conditions in aerospace, defence, and energy sectors has driven interests in high-entropy alloys (HEAs). This study investigates the doping of Scandium (Sc), a rare earth element, into AlCoCrFeMo HEA using mechanical alloying via ball milling. Sc has gained significant attention for promoting grain refinement by forming precipitates that act as nucleation sites, inhibit grain growth, and enhance grain structure, thereby enhancing mechanical strength and ductility. This study optimizes the mechanical alloying parameters to achieve a homogeneous distribution of Sc in the AlCoCrFeMo HEA matrix. Results indicated that 12 h of mechanical alloying at 110 rpm is sufficient to ensure uniform distribution of Sc in the HEA. Various Sc compositions (0.1 wt%, 0.3 wt%, and 0.5 wt%) were investigated to determine the solubility limit of Sc in the HEA. The microstructural and chemical composition analyses revealed that the Sc solubility limit in the HEA lies between 0.2 and 0.3 wt%, providing critical insights for alloy design. Flame-sprayed coatings from Sc-doped powders exhibited significant refined splat morphologies, reduced porosity, and increased hardness, achieving up to 979 HV 0.3 , demonstrating superior coating performance enabled by Sc incorporation. These findings provide critical insights into the behaviour of Sc-doped HEAs, supporting their deployment in extreme environments where superior mechanical performance is essential. • A mechanical alloying process was developed and optimized to dope scandium into AlCoCrFeMo high entropy alloy (HEA). • The solubility limit of Sc in AlCoCrFeMo HEA was identified between 0.2 and 0.3 wt%. • Flame-sprayed Sc-doped coatings exhibited refined microstructures and a reduced amount of porosity. • The optimized coatings reached a 104 % hardness improvement over the base HEA.
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.
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 | 0.001 | 0.001 |
| 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 it