High-temperature shape memory loss in nitinol: a first principles study
Bibliographic record
Abstract
We have performed first-principles calculations to investigate the possibility of shape memory loss in a member of the binary smart alloy family - NiTi. A detailed analysis of the transition kinetics and dynamical pathway reveals the possibility of the B19' phase of NiTi losing its shape memory when subjected to high stress conditions and is heated above a critical temperature, Tc. The B19' phase is predicted to transform to P1[combining macron]-NiTi, which is also predicted to be dynamically stable and temperature-quench recoverable. It is found that the B2(B33) → B19' transition is dominated by the β shearing mode with pronounced distortion in the (001) planes and significant volume reduction. Furthermore, the B19' → P1[combining macron] transition is dominated by the γ shearing mode with pronounced distortion in the (010) planes and slight volume expansion. The cumulative effect of both processes activates the lowering and eventual breaking of symmetry in the precursor phases and drives the permanent deformation and shape memory loss. We further show that the P1[combining macron]-NiTi structure is stabilized (over B19' structure) by kinetics. The findings of this study will stimulate further studies on how to retain and improve the shape memory feature in NiTi and other binary smart alloys to prevent property failure when used in the fabrication of devices operated in the high temperature and pressure regime.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".