Tune Al/Ti to adjust FCC+L21 hetero-structured Ni-based high-entropy alloys for improved mechanical properties and wear resistance
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
Outstanding mechanical properties of Ni-based superalloy benefit from its coherent γ/γ’ structure via precipitation strengthening of γ matrix (FCC structure) by L1 2 Ni 3 Al-type γ’ phase. Back-stress strengthening is another effective strategy to further enhance the FCC+L1 2 structured Ni-based superalloy. In this work, we extend such approaches to high-entropy alloys (HEAs) by introducing different Al and Ti contents (5 at.% ∼18 at.%) into a Ni-based CrFe 2 Ni 4 alloy to form FCC+L2 1 heterostructured Al x CrFe 2 Ni 4 Ti y HEAs. Detailed microstructural analysis indicates that L1 2 Ni 3 (Al,Ti)-type nanoparticles form in a (Ni,Fe,Cr)-rich FCC matrix. The volume fraction of L2 1 AlNi 2 Ti-type phase can be varied by adjusting the Al/Ti ratio and concentrations of Al and Ti. Higher Al and Ti contents promote L2 1 phase formation and higher Al/Ti ratio (>1) prohibits the high Ti-containing compounds such as D0 24 η -Ni 3 Ti and C14 Laves Fe 2 Ti phases, which are hard but brittle. Corresponding Young's modulus , Poisson's ratio , hardness, and the bulk to shear modulus ratio (B/G) can be readily modified. Compressive tests demonstrate that Al 1.5 CrFe 2 Ni 4 Ti 1.0 alloy with half FCC and half L2 1 phases possesses the optimal strength-ductility combination (with compressive yield strength of ∼1564 MPa and fracture strain of ∼28 %). DFT calculations were performed to elucidate relevant mechanisms. Sliding wear tests were also performed, which demonstrate superior wear resistance of the HEAs at both room and elevated temperatures, compared with a commercial Ni-based superalloy, UHT-Nickel.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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