Interplay between phase stability and deformation mechanisms through compositional tuning: Insights into alloy design strategy from the Co-Cr-Ni medium-entropy system
Bibliographic record
Abstract
This study investigates the interplay between composition-dependent phase stability and deformation mechanisms in Co-Cr-Ni medium-entropy alloys (MEAs) of equiatomic and non-equiatomic compositions. Co-rich (Co 2 CrNi), Cr-rich (CoCr 2 Ni), and Ni-rich (CoCrNi 2 ) MEAs were designed with each composition determined by selecting a constituent element present in the highest proportion compared to the equiatomic CoCrNi system, which served as the master composition. The composition tuning resulted in distinct microstructure evolution, influencing the mechanical properties and flow mechanisms, closely correlated with variations in stacking fault energy (SFE). Notably, the results highlighted the superior formability, attributed to the distinct deformation mechanisms present in single-phase MEAs (i.e., Co 2 CrNi, CoCrNi 2 , and CoCrNi). On the other hand, the introduction of ∼50 at% Cr thermodynamically triggered the formation of σ-phase upon homogenization treatment, negatively affecting the stability of the face-centered cubic (FCC) matrix and the alloy's mechanical behavior. Macro-to-nanoscale hierarchical microstructure analyses confirmed the progression of deformation via deformation-induced twinning (CoCrNi) and ɛ-martensite formation (Co 2 CrNi) in low-SFE alloys. In contrast, the higher-SFE CoCrNi 2 alloy exhibited highly dense dislocation walls (HDDWs) with fewer planar dislocation arrays despite the presence of numerous annealing twins. This research provides a systematic alloy design strategy, integrating theoretical analyses with experimental observations to achieve an in-depth comprehension of composition tuning in the Co-Cr-Ni system.
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 | 0.001 | 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 | 0.001 |
| 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".